From 0a920039558ef1eee1aa85a5ffb70b69f939a2cf Mon Sep 17 00:00:00 2001 From: babayaga Date: Sat, 26 Jul 2025 19:41:46 +0200 Subject: [PATCH] maintainence love:) --- packages/kbot/dist-in/data/openai_models.json | 2 +- .../kbot/dist-in/data/openrouter_models.json | 2928 +++++++++-------- .../dist-in/src/models/cache/openrouter.ts | 2 +- packages/kbot/dist/package-lock.json | 4 +- packages/kbot/dist/package.json | 2 +- packages/kbot/schema.json | 2 +- packages/kbot/schema_ui.json | 2 +- .../models/cache/openrouter-models-free.ts | 5 +- .../src/models/cache/openrouter-models.ts | 70 +- packages/kbot/src/zod_types.ts | 41 +- 10 files changed, 1569 insertions(+), 1489 deletions(-) diff --git a/packages/kbot/dist-in/data/openai_models.json b/packages/kbot/dist-in/data/openai_models.json index c5829484..2ff4fd1d 100644 --- a/packages/kbot/dist-in/data/openai_models.json +++ b/packages/kbot/dist-in/data/openai_models.json @@ -1,5 +1,5 @@ { - "timestamp": 1752753846841, + "timestamp": 1753551692806, "models": [ { "id": "gpt-4-0613", diff --git a/packages/kbot/dist-in/data/openrouter_models.json b/packages/kbot/dist-in/data/openrouter_models.json index 269e801e..1200035a 100644 --- a/packages/kbot/dist-in/data/openrouter_models.json +++ b/packages/kbot/dist-in/data/openrouter_models.json @@ -1,6 +1,404 @@ { - "timestamp": 1752753847193, + "timestamp": 1753551693103, "models": [ + { + "id": "qwen/qwen3-235b-a22b-thinking-2507", + "canonical_slug": "qwen/qwen3-235b-a22b-thinking-2507", + "hugging_face_id": "Qwen/Qwen3-235B-A22B-Thinking-2507", + "name": "Qwen: Qwen3 235B A22B Thinking 2507", + "created": 1753449557, + "description": "Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144 tokens of context. This \"thinking-only\" variant enhances structured logical reasoning, mathematics, science, and long-form generation, showing strong benchmark performance across AIME, SuperGPQA, LiveCodeBench, and MMLU-Redux. It enforces a special reasoning mode () and is designed for high-token outputs (up to 81,920 tokens) in challenging domains.\n\nThe model is instruction-tuned and excels at step-by-step reasoning, tool use, agentic workflows, and multilingual tasks. This release represents the most capable open-source variant in the Qwen3-235B series, surpassing many closed models in structured reasoning use cases.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Qwen3", + "instruct_type": "qwen3" + }, + "pricing": { + "prompt": "0.0000001179", + "completion": "0.0000001179", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs", + "response_format" + ] + }, + { + "id": "z-ai/glm-4-32b", + "canonical_slug": "z-ai/glm-4-32b-0414", + "hugging_face_id": "", + "name": "Z.AI: GLM 4 32B ", + "created": 1753376617, + "description": "GLM 4 32B is a cost-effective foundation language model.\n\nIt can efficiently perform complex tasks and has significantly enhanced capabilities in tool use, online search, and code-related intelligent tasks.\n\nIt is made by the same lab behind the thudm models.", + "context_length": 128000, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000001", + "completion": "0.0000001", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice" + ] + }, + { + "id": "qwen/qwen3-coder:free", + "canonical_slug": "qwen/qwen3-coder-480b-a35b-07-25", + "hugging_face_id": "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "name": "Qwen: Qwen3 Coder (free)", + "created": 1753230546, + "description": "Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).\n\nPricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0", + "completion": "0", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, + { + "id": "qwen/qwen3-coder", + "canonical_slug": "qwen/qwen3-coder-480b-a35b-07-25", + "hugging_face_id": "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "name": "Qwen: Qwen3 Coder ", + "created": 1753230546, + "description": "Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over repositories. The model features 480 billion total parameters, with 35 billion active per forward pass (8 out of 160 experts).\n\nPricing for the Alibaba endpoints varies by context length. Once a request is greater than 128k input tokens, the higher pricing is used.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000003", + "completion": "0.0000012", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "top_k", + "repetition_penalty", + "logit_bias", + "min_p", + "response_format", + "tools", + "tool_choice", + "seed", + "structured_outputs", + "logprobs", + "top_logprobs" + ] + }, + { + "id": "bytedance/ui-tars-1.5-7b", + "canonical_slug": "bytedance/ui-tars-1.5-7b", + "hugging_face_id": "ByteDance-Seed/UI-TARS-1.5-7B", + "name": "Bytedance: UI-TARS 7B ", + "created": 1753205056, + "description": "UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, it builds upon the UI-TARS framework with reinforcement learning-based reasoning, enabling robust action planning and execution across virtual interfaces.\n\nThis model achieves state-of-the-art results on a range of interactive and grounding benchmarks, including OSworld, WebVoyager, AndroidWorld, and ScreenSpot. It also demonstrates perfect task completion across diverse Poki games and outperforms prior models in Minecraft agent tasks. UI-TARS-1.5 supports thought decomposition during inference and shows strong scaling across variants, with the 1.5 version notably exceeding the performance of earlier 72B and 7B checkpoints.", + "context_length": 128000, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "image", + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000001", + "completion": "0.0000002", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 2048, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "frequency_penalty", + "min_p", + "presence_penalty", + "repetition_penalty", + "seed", + "stop", + "top_k" + ] + }, + { + "id": "google/gemini-2.5-flash-lite", + "canonical_slug": "google/gemini-2.5-flash-lite", + "hugging_face_id": "", + "name": "Google: Gemini 2.5 Flash Lite", + "created": 1753200276, + "description": "Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance across common benchmarks compared to earlier Flash models. By default, \"thinking\" (i.e. multi-pass reasoning) is disabled to prioritize speed, but developers can enable it via the [Reasoning API parameter](https://openrouter.ai/docs/use-cases/reasoning-tokens) to selectively trade off cost for intelligence. ", + "context_length": 1048576, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "file", + "image", + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.0000001", + "completion": "0.0000004", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0", + "input_cache_read": "0.000000025", + "input_cache_write": "0.0000001833" + }, + "top_provider": { + "context_length": 1048576, + "max_completion_tokens": 65535, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "reasoning", + "include_reasoning", + "structured_outputs", + "response_format", + "seed" + ] + }, + { + "id": "qwen/qwen3-235b-a22b-2507:free", + "canonical_slug": "qwen/qwen3-235b-a22b-07-25", + "hugging_face_id": "Qwen/Qwen3-235B-A22B-Instruct-2507", + "name": "Qwen: Qwen3 235B A22B Instruct 2507 (free)", + "created": 1753119555, + "description": "Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement \"thinking mode\" ( blocks).\n\nCompared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0", + "completion": "0", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, + { + "id": "qwen/qwen3-235b-a22b-2507", + "canonical_slug": "qwen/qwen3-235b-a22b-07-25", + "hugging_face_id": "Qwen/Qwen3-235B-A22B-Instruct-2507", + "name": "Qwen: Qwen3 235B A22B Instruct 2507", + "created": 1753119555, + "description": "Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following, logical reasoning, math, code, and tool usage. The model supports a native 262K context length and does not implement \"thinking mode\" ( blocks).\n\nCompared to its base variant, this version delivers significant gains in knowledge coverage, long-context reasoning, coding benchmarks, and alignment with open-ended tasks. It is particularly strong on multilingual understanding, math reasoning (e.g., AIME, HMMT), and alignment evaluations like Arena-Hard and WritingBench.", + "context_length": 262144, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Qwen3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000012", + "completion": "0.00000059", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 262144, + "max_completion_tokens": 262144, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "seed", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "repetition_penalty", + "response_format", + "top_k", + "min_p", + "structured_outputs", + "logit_bias", + "logprobs", + "top_logprobs" + ] + }, { "id": "switchpoint/router", "canonical_slug": "switchpoint/router", @@ -52,7 +450,7 @@ "name": "MoonshotAI: Kimi K2 (free)", "created": 1752263252, "description": "Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.", - "context_length": 65536, + "context_length": 32768, "architecture": { "modality": "text->text", "input_modalities": [ @@ -73,7 +471,7 @@ "internal_reasoning": "0" }, "top_provider": { - "context_length": 65536, + "context_length": 32768, "max_completion_tokens": null, "is_moderated": false }, @@ -144,9 +542,9 @@ "repetition_penalty", "logit_bias", "min_p", - "seed", + "logprobs", "top_logprobs", - "logprobs" + "seed" ] }, { @@ -588,6 +986,57 @@ "top_logprobs" ] }, + { + "id": "tngtech/deepseek-r1t2-chimera", + "canonical_slug": "tngtech/deepseek-r1t2-chimera", + "hugging_face_id": "tngtech/DeepSeek-TNG-R1T2-Chimera", + "name": "TNG: DeepSeek R1T2 Chimera", + "created": 1751986985, + "description": "DeepSeek-TNG-R1T2-Chimera is the second-generation Chimera model from TNG Tech. It is a 671 B-parameter mixture-of-experts text-generation model assembled from DeepSeek-AI’s R1-0528, R1, and V3-0324 checkpoints with an Assembly-of-Experts merge. The tri-parent design yields strong reasoning performance while running roughly 20 % faster than the original R1 and more than 2× faster than R1-0528 under vLLM, giving a favorable cost-to-intelligence trade-off. The checkpoint supports contexts up to 60 k tokens in standard use (tested to ~130 k) and maintains consistent token behaviour, making it suitable for long-context analysis, dialogue and other open-ended generation tasks.", + "context_length": 163840, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "DeepSeek", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000000302", + "completion": "0.000000302", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "morph/morph-v3-large", "canonical_slug": "morph/morph-v3-large", @@ -1203,7 +1652,7 @@ "output_modalities": [ "text" ], - "tokenizer": "Other", + "tokenizer": "GPT", "instruct_type": null }, "pricing": { @@ -1336,7 +1785,7 @@ "name": "Mistral: Magistral Small 2506", "created": 1749569561, "description": "Magistral Small is a 24B parameter instruction-tuned model based on Mistral-Small-3.1 (2503), enhanced through supervised fine-tuning on traces from Magistral Medium and further refined via reinforcement learning. It is optimized for reasoning and supports a wide multilingual range, including over 20 languages.", - "context_length": 40960, + "context_length": 40000, "architecture": { "modality": "text->text", "input_modalities": [ @@ -1349,16 +1798,16 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000001", - "completion": "0.0000003", + "prompt": "0.0000005", + "completion": "0.0000015", "request": "0", "image": "0", "web_search": "0", "internal_reasoning": "0" }, "top_provider": { - "context_length": 40960, - "max_completion_tokens": 40960, + "context_length": 40000, + "max_completion_tokens": 40000, "is_moderated": false }, "per_request_limits": null, @@ -1366,22 +1815,16 @@ "max_tokens", "temperature", "top_p", + "tools", + "tool_choice", "reasoning", "include_reasoning", + "structured_outputs", + "response_format", "stop", "frequency_penalty", "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", - "logprobs", - "top_logprobs", - "min_p", - "seed", - "tools", - "tool_choice", - "structured_outputs", - "response_format" + "seed" ] }, { @@ -1774,10 +2217,10 @@ "logit_bias", "min_p", "response_format", - "tools", - "tool_choice", "structured_outputs", "seed", + "tools", + "tool_choice", "logprobs", "top_logprobs" ] @@ -1887,7 +2330,7 @@ "name": "TheDrummer: Valkyrie 49B V1", "created": 1748022670, "description": "Built on top of NVIDIA's Llama 3.3 Nemotron Super 49B, Valkyrie is TheDrummer's newest model drop for creative writing.", - "context_length": 32768, + "context_length": 131072, "architecture": { "modality": "text->text", "input_modalities": [ @@ -1900,16 +2343,16 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000006", - "completion": "0.0000008", + "prompt": "0.00000065", + "completion": "0.000001", "request": "0", "image": "0", "web_search": "0", "internal_reasoning": "0" }, "top_provider": { - "context_length": 32768, - "max_completion_tokens": null, + "context_length": 131072, + "max_completion_tokens": 131072, "is_moderated": false }, "per_request_limits": null, @@ -1917,16 +2360,14 @@ "max_tokens", "temperature", "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "response_format", - "structured_outputs", "reasoning", "include_reasoning", + "frequency_penalty", "min_p", + "presence_penalty", "repetition_penalty", "seed", + "stop", "top_k" ] }, @@ -2265,6 +2706,57 @@ "structured_outputs" ] }, + { + "id": "nousresearch/deephermes-3-mistral-24b-preview", + "canonical_slug": "nousresearch/deephermes-3-mistral-24b-preview", + "hugging_face_id": "NousResearch/DeepHermes-3-Mistral-24B-Preview", + "name": "Nous: DeepHermes 3 Mistral 24B Preview", + "created": 1746830904, + "description": "DeepHermes 3 (Mistral 24B Preview) is an instruction-tuned language model by Nous Research based on Mistral-Small-24B, designed for chat, function calling, and advanced multi-turn reasoning. It introduces a dual-mode system that toggles between intuitive chat responses and structured “deep reasoning” mode using special system prompts. Fine-tuned via distillation from R1, it supports structured output (JSON mode) and function call syntax for agent-based applications.\n\nDeepHermes 3 supports a **reasoning toggle via system prompt**, allowing users to switch between fast, intuitive responses and deliberate, multi-step reasoning. When activated with the following specific system instruction, the model enters a *\"deep thinking\"* mode—generating extended chains of thought wrapped in `` tags before delivering a final answer. \n\nSystem Prompt: You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside tags, and then provide your solution or response to the problem.\n", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000000141", + "completion": "0.000000141", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "mistralai/mistral-medium-3", "canonical_slug": "mistralai/mistral-medium-3", @@ -2364,55 +2856,6 @@ "seed" ] }, - { - "id": "arcee-ai/caller-large", - "canonical_slug": "arcee-ai/caller-large", - "hugging_face_id": "", - "name": "Arcee AI: Caller Large", - "created": 1746487869, - "description": "Caller Large is Arcee's specialist \"function‑calling\" SLM built to orchestrate external tools and APIs. Instead of maximizing next‑token accuracy, training focuses on structured JSON outputs, parameter extraction and multi‑step tool chains, making Caller a natural choice for retrieval‑augmented generation, robotic process automation or data‑pull chatbots. It incorporates a routing head that decides when (and how) to invoke a tool versus answering directly, reducing hallucinated calls. The model is already the backbone of Arcee Conductor's auto‑tool mode, where it parses user intent, emits clean function signatures and hands control back once the tool response is ready. Developers thus gain an OpenAI‑style function‑calling UX without handing requests to a frontier‑scale model. ", - "context_length": 32768, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Other", - "instruct_type": null - }, - "pricing": { - "prompt": "0.00000055", - "completion": "0.00000085", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 32768, - "max_completion_tokens": null, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", - "min_p", - "response_format" - ] - }, { "id": "arcee-ai/spotlight", "canonical_slug": "arcee-ai/spotlight", @@ -2604,102 +3047,6 @@ "response_format" ] }, - { - "id": "arcee-ai/virtuoso-medium-v2", - "canonical_slug": "arcee-ai/virtuoso-medium-v2", - "hugging_face_id": "arcee-ai/Virtuoso-Medium-v2", - "name": "Arcee AI: Virtuoso Medium V2", - "created": 1746478434, - "description": "Virtuoso‑Medium‑v2 is a 32 B model distilled from DeepSeek‑v3 logits and merged back onto a Qwen 2.5 backbone, yielding a sharper, more factual successor to the original Virtuoso Medium. The team harvested ~1.1 B logit tokens and applied \"fusion‑merging\" plus DPO alignment, which pushed scores past Arcee‑Nova 2024 and many 40 B‑plus peers on MMLU‑Pro, MATH and HumanEval. With a 128 k context and aggressive quantization options (from BF16 down to 4‑bit GGUF), it balances capability with deployability on single‑GPU nodes. Typical use cases include enterprise chat assistants, technical writing aids and medium‑complexity code drafting where Virtuoso‑Large would be overkill. ", - "context_length": 131072, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Other", - "instruct_type": null - }, - "pricing": { - "prompt": "0.0000005", - "completion": "0.0000008", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 131072, - "max_completion_tokens": 32768, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", - "min_p", - "response_format" - ] - }, - { - "id": "arcee-ai/arcee-blitz", - "canonical_slug": "arcee-ai/arcee-blitz", - "hugging_face_id": "arcee-ai/arcee-blitz", - "name": "Arcee AI: Arcee Blitz", - "created": 1746470100, - "description": "Arcee Blitz is a 24 B‑parameter dense model distilled from DeepSeek and built on Mistral architecture for \"everyday\" chat. The distillation‑plus‑refinement pipeline trims compute while keeping DeepSeek‑style reasoning, so Blitz punches above its weight on MMLU, GSM‑8K and BBH compared with other mid‑size open models. With a default 128 k context window and competitive throughput, it serves as a cost‑efficient workhorse for summarization, brainstorming and light code help. Internally, Arcee uses Blitz as the default writer in Conductor pipelines when the heavier Virtuoso line is not required. Users therefore get near‑70 B quality at ~⅓ the latency and price. ", - "context_length": 32768, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Other", - "instruct_type": null - }, - "pricing": { - "prompt": "0.00000045", - "completion": "0.00000075", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 32768, - "max_completion_tokens": null, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", - "min_p", - "response_format" - ] - }, { "id": "microsoft/phi-4-reasoning-plus", "canonical_slug": "microsoft/phi-4-reasoning-plus-04-30", @@ -3260,60 +3607,6 @@ "web_search": "0", "internal_reasoning": "0" }, - "top_provider": { - "context_length": 40960, - "max_completion_tokens": 40960, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "reasoning", - "include_reasoning", - "frequency_penalty", - "min_p", - "presence_penalty", - "repetition_penalty", - "seed", - "stop", - "top_k", - "tools", - "tool_choice", - "response_format", - "logit_bias", - "logprobs", - "top_logprobs" - ] - }, - { - "id": "qwen/qwen3-32b:free", - "canonical_slug": "qwen/qwen3-32b-04-28", - "hugging_face_id": "Qwen/Qwen3-32B", - "name": "Qwen: Qwen3 32B (free)", - "created": 1745875945, - "description": "Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for tasks like math, coding, and logical inference, and a \"non-thinking\" mode for faster, general-purpose conversation. The model demonstrates strong performance in instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling. ", - "context_length": 40960, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Qwen3", - "instruct_type": "qwen3" - }, - "pricing": { - "prompt": "0", - "completion": "0", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, "top_provider": { "context_length": 40960, "max_completion_tokens": null, @@ -3326,15 +3619,19 @@ "top_p", "reasoning", "include_reasoning", - "stop", "frequency_penalty", - "presence_penalty", - "seed", - "top_k", "min_p", + "presence_penalty", "repetition_penalty", - "logprobs", + "seed", + "stop", + "top_k", + "response_format", + "structured_outputs", + "tools", + "tool_choice", "logit_bias", + "logprobs", "top_logprobs" ] }, @@ -3358,8 +3655,8 @@ "instruct_type": "qwen3" }, "pricing": { - "prompt": "0.0000001", - "completion": "0.0000003", + "prompt": "0.000000027", + "completion": "0.000000027", "request": "0", "image": "0", "web_search": "0", @@ -3605,6 +3902,57 @@ "top_logprobs" ] }, + { + "id": "microsoft/mai-ds-r1", + "canonical_slug": "microsoft/mai-ds-r1", + "hugging_face_id": "microsoft/MAI-DS-R1", + "name": "Microsoft: MAI DS R1", + "created": 1745194100, + "description": "MAI-DS-R1 is a post-trained variant of DeepSeek-R1 developed by the Microsoft AI team to improve the model’s responsiveness on previously blocked topics while enhancing its safety profile. Built on top of DeepSeek-R1’s reasoning foundation, it integrates 110k examples from the Tulu-3 SFT dataset and 350k internally curated multilingual safety-alignment samples. The model retains strong reasoning, coding, and problem-solving capabilities, while unblocking a wide range of prompts previously restricted in R1.\n\nMAI-DS-R1 demonstrates improved performance on harm mitigation benchmarks and maintains competitive results across general reasoning tasks. It surpasses R1-1776 in satisfaction metrics for blocked queries and reduces leakage in harmful content categories. The model is based on a transformer MoE architecture and is suitable for general-purpose use cases, excluding high-stakes domains such as legal, medical, or autonomous systems.", + "context_length": 163840, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "DeepSeek", + "instruct_type": "deepseek-r1" + }, + "pricing": { + "prompt": "0.000000302", + "completion": "0.000000302", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 163840, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "thudm/glm-z1-32b:free", "canonical_slug": "thudm/glm-z1-32b-0414", @@ -3656,6 +4004,57 @@ "top_logprobs" ] }, + { + "id": "thudm/glm-z1-32b", + "canonical_slug": "thudm/glm-z1-32b-0414", + "hugging_face_id": "THUDM/GLM-Z1-32B-0414", + "name": "THUDM: GLM Z1 32B", + "created": 1744924148, + "description": "GLM-Z1-32B-0414 is an enhanced reasoning variant of GLM-4-32B, built for deep mathematical, logical, and code-oriented problem solving. It applies extended reinforcement learning—both task-specific and general pairwise preference-based—to improve performance on complex multi-step tasks. Compared to the base GLM-4-32B model, Z1 significantly boosts capabilities in structured reasoning and formal domains.\n\nThe model supports enforced “thinking” steps via prompt engineering and offers improved coherence for long-form outputs. It’s optimized for use in agentic workflows, and includes support for long context (via YaRN), JSON tool calling, and fine-grained sampling configuration for stable inference. Ideal for use cases requiring deliberate, multi-step reasoning or formal derivations.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Other", + "instruct_type": "deepseek-r1" + }, + "pricing": { + "prompt": "0.00000003", + "completion": "0.00000003", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "thudm/glm-4-32b:free", "canonical_slug": "thudm/glm-4-32b-0414", @@ -3815,7 +4214,7 @@ "output_modalities": [ "text" ], - "tokenizer": "Other", + "tokenizer": "GPT", "instruct_type": null }, "pricing": { @@ -3935,6 +4334,55 @@ "top_logprobs" ] }, + { + "id": "shisa-ai/shisa-v2-llama3.3-70b", + "canonical_slug": "shisa-ai/shisa-v2-llama3.3-70b", + "hugging_face_id": "shisa-ai/shisa-v2-llama3.3-70b", + "name": "Shisa AI: Shisa V2 Llama 3.3 70B ", + "created": 1744754858, + "description": "Shisa V2 Llama 3.3 70B is a bilingual Japanese-English chat model fine-tuned by Shisa.AI on Meta’s Llama-3.3-70B-Instruct base. It prioritizes Japanese language performance while retaining strong English capabilities. The model was optimized entirely through post-training, using a refined mix of supervised fine-tuning (SFT) and DPO datasets including regenerated ShareGPT-style data, translation tasks, roleplaying conversations, and instruction-following prompts. Unlike earlier Shisa releases, this version avoids tokenizer modifications or extended pretraining.\n\nShisa V2 70B achieves leading Japanese task performance across a wide range of custom and public benchmarks, including JA MT Bench, ELYZA 100, and Rakuda. It supports a 128K token context length and integrates smoothly with inference frameworks like vLLM and SGLang. While it inherits safety characteristics from its base model, no additional alignment was applied. The model is intended for high-performance bilingual chat, instruction following, and translation tasks across JA/EN.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Llama3", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000003", + "completion": "0.00000003", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "openai/gpt-4.1", "canonical_slug": "openai/gpt-4.1-2025-04-14", @@ -4239,6 +4687,57 @@ "top_logprobs" ] }, + { + "id": "arliai/qwq-32b-arliai-rpr-v1", + "canonical_slug": "arliai/qwq-32b-arliai-rpr-v1", + "hugging_face_id": "ArliAI/QwQ-32B-ArliAI-RpR-v1", + "name": "ArliAI: QwQ 32B RpR v1", + "created": 1744555982, + "description": "QwQ-32B-ArliAI-RpR-v1 is a 32B parameter model fine-tuned from Qwen/QwQ-32B using a curated creative writing and roleplay dataset originally developed for the RPMax series. It is designed to maintain coherence and reasoning across long multi-turn conversations by introducing explicit reasoning steps per dialogue turn, generated and refined using the base model itself.\n\nThe model was trained using RS-QLORA+ on 8K sequence lengths and supports up to 128K context windows (with practical performance around 32K). It is optimized for creative roleplay and dialogue generation, with an emphasis on minimizing cross-context repetition while preserving stylistic diversity.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Other", + "instruct_type": "deepseek-r1" + }, + "pricing": { + "prompt": "0.000000015", + "completion": "0.000000015", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "agentica-org/deepcoder-14b-preview:free", "canonical_slug": "agentica-org/deepcoder-14b-preview", @@ -4393,6 +4892,58 @@ "top_logprobs" ] }, + { + "id": "moonshotai/kimi-vl-a3b-thinking", + "canonical_slug": "moonshotai/kimi-vl-a3b-thinking", + "hugging_face_id": "moonshotai/Kimi-VL-A3B-Thinking", + "name": "Moonshot AI: Kimi VL A3B Thinking", + "created": 1744304841, + "description": "Kimi-VL is a lightweight Mixture-of-Experts vision-language model that activates only 2.8B parameters per step while delivering strong performance on multimodal reasoning and long-context tasks. The Kimi-VL-A3B-Thinking variant, fine-tuned with chain-of-thought and reinforcement learning, excels in math and visual reasoning benchmarks like MathVision, MMMU, and MathVista, rivaling much larger models such as Qwen2.5-VL-7B and Gemma-3-12B. It supports 128K context and high-resolution input via its MoonViT encoder.", + "context_length": 131072, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "image", + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Other", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000000038", + "completion": "0.000000038", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "reasoning", + "include_reasoning", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs" + ] + }, { "id": "x-ai/grok-3-mini-beta", "canonical_slug": "x-ai/grok-3-mini-beta", @@ -4683,11 +5234,11 @@ "seed", "min_p", "structured_outputs", - "logit_bias", "tools", "tool_choice", "logprobs", - "top_logprobs" + "top_logprobs", + "logit_bias" ] }, { @@ -4745,10 +5296,10 @@ ] }, { - "id": "deepseek/deepseek-v3-base:free", + "id": "deepseek/deepseek-v3-base", "canonical_slug": "deepseek/deepseek-v3-base", "hugging_face_id": "deepseek-ai/Deepseek-v3-base", - "name": "DeepSeek: DeepSeek V3 Base (free)", + "name": "DeepSeek: DeepSeek V3 Base", "created": 1743272023, "description": "Note that this is a base model mostly meant for testing, you need to provide detailed prompts for the model to return useful responses. \n\nDeepSeek-V3 Base is a 671B parameter open Mixture-of-Experts (MoE) language model with 37B active parameters per forward pass and a context length of 128K tokens. Trained on 14.8T tokens using FP8 mixed precision, it achieves high training efficiency and stability, with strong performance across language, reasoning, math, and coding tasks. \n\nDeepSeek-V3 Base is the pre-trained model behind [DeepSeek V3](/deepseek/deepseek-chat-v3)", "context_length": 163840, @@ -4764,8 +5315,8 @@ "instruct_type": null }, "pricing": { - "prompt": "0", - "completion": "0", + "prompt": "0.000000302", + "completion": "0.000000302", "request": "0", "image": "0", "web_search": "0", @@ -5027,15 +5578,15 @@ "max_tokens", "temperature", "top_p", - "tools", - "tool_choice", "stop", "frequency_penalty", "presence_penalty", "seed", "top_k", - "min_p", "repetition_penalty", + "tools", + "tool_choice", + "min_p", "logprobs", "logit_bias", "top_logprobs" @@ -5097,10 +5648,10 @@ { "id": "featherless/qwerky-72b:free", "canonical_slug": "featherless/qwerky-72b", - "hugging_face_id": "featherless-ai/Qwerky-72B", - "name": "Qwerky 72B (free)", + "hugging_face_id": "featherless-ai/QRWKV-72B", + "name": "Qrwkv 72B (free)", "created": 1742481597, - "description": "Qwerky-72B is a linear-attention RWKV variant of the Qwen 2.5 72B model, optimized to significantly reduce computational cost at scale. Leveraging linear attention, it achieves substantial inference speedups (>1000x) while retaining competitive accuracy on common benchmarks like ARC, HellaSwag, Lambada, and MMLU. It inherits knowledge and language support from Qwen 2.5, supporting approximately 30 languages, making it suitable for efficient inference in large-context applications.", + "description": "Qrwkv-72B is a linear-attention RWKV variant of the Qwen 2.5 72B model, optimized to significantly reduce computational cost at scale. Leveraging linear attention, it achieves substantial inference speedups (>1000x) while retaining competitive accuracy on common benchmarks like ARC, HellaSwag, Lambada, and MMLU. It inherits knowledge and language support from Qwen 2.5, supporting approximately 30 languages, making it suitable for efficient inference in large-context applications.", "context_length": 32768, "architecture": { "modality": "text->text", @@ -5242,7 +5793,7 @@ "name": "Mistral: Mistral Small 3.1 24B", "created": 1742238937, "description": "Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and vision tasks, including image analysis, programming, mathematical reasoning, and multilingual support across dozens of languages. Equipped with an extensive 128k token context window and optimized for efficient local inference, it supports use cases such as conversational agents, function calling, long-document comprehension, and privacy-sensitive deployments. The updated version is [Mistral Small 3.2](mistralai/mistral-small-3.2-24b-instruct)", - "context_length": 128000, + "context_length": 96000, "architecture": { "modality": "text+image->text", "input_modalities": [ @@ -5256,16 +5807,16 @@ "instruct_type": null }, "pricing": { - "prompt": "0.00000005", - "completion": "0.0000001", + "prompt": "0.000000027", + "completion": "0.000000027", "request": "0", "image": "0", "web_search": "0", "internal_reasoning": "0" }, "top_provider": { - "context_length": 128000, - "max_completion_tokens": null, + "context_length": 96000, + "max_completion_tokens": 96000, "is_moderated": false }, "per_request_limits": null, @@ -5283,10 +5834,10 @@ "min_p", "tools", "tool_choice", - "structured_outputs", - "logit_bias", "logprobs", - "top_logprobs" + "logit_bias", + "top_logprobs", + "structured_outputs" ] }, { @@ -5373,8 +5924,9 @@ "stop", "frequency_penalty", "presence_penalty", - "repetition_penalty", "response_format", + "structured_outputs", + "repetition_penalty", "top_k", "seed", "min_p" @@ -5574,7 +6126,7 @@ "name": "Cohere: Command A", "created": 1741894342, "description": "Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases.\nCompared to other leading proprietary and open-weights models Command A delivers maximum performance with minimum hardware costs, excelling on business-critical agentic and multilingual tasks.", - "context_length": 256000, + "context_length": 32768, "architecture": { "modality": "text->text", "input_modalities": [ @@ -5587,17 +6139,17 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000025", - "completion": "0.00001", + "prompt": "0.000002", + "completion": "0.000008", "request": "0", "image": "0", "web_search": "0", "internal_reasoning": "0" }, "top_provider": { - "context_length": 256000, - "max_completion_tokens": 8192, - "is_moderated": true + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false }, "per_request_limits": null, "supported_parameters": [ @@ -5607,10 +6159,10 @@ "stop", "frequency_penalty", "presence_penalty", - "top_k", - "seed", "response_format", - "structured_outputs" + "structured_outputs", + "top_k", + "seed" ] }, { @@ -5893,9 +6445,9 @@ "top_k", "seed", "min_p", + "logprobs", "logit_bias", - "top_logprobs", - "logprobs" + "top_logprobs" ] }, { @@ -5951,7 +6503,7 @@ "name": "TheDrummer: Skyfall 36B V2", "created": 1741636566, "description": "Skyfall 36B v2 is an enhanced iteration of Mistral Small 2501, specifically fine-tuned for improved creativity, nuanced writing, role-playing, and coherent storytelling.", - "context_length": 32768, + "context_length": 16384, "architecture": { "modality": "text->text", "input_modalities": [ @@ -5964,15 +6516,15 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000004", - "completion": "0.0000007", + "prompt": "0.00000001512", + "completion": "0.00000001512", "request": "0", "image": "0", "web_search": "0", "internal_reasoning": "0" }, "top_provider": { - "context_length": 32768, + "context_length": 16384, "max_completion_tokens": null, "is_moderated": false }, @@ -5986,10 +6538,13 @@ "presence_penalty", "response_format", "structured_outputs", + "seed", + "top_k", "min_p", "repetition_penalty", - "seed", - "top_k" + "logprobs", + "logit_bias", + "top_logprobs" ] }, { @@ -6219,12 +6774,7 @@ "top_k", "reasoning", "include_reasoning", - "seed", - "min_p", - "repetition_penalty", - "logprobs", - "logit_bias", - "top_logprobs" + "seed" ] }, { @@ -6269,11 +6819,12 @@ "stop", "frequency_penalty", "presence_penalty", + "response_format", + "structured_outputs", "top_k", "repetition_penalty", "logit_bias", "min_p", - "response_format", "logprobs", "top_logprobs", "seed" @@ -7575,13 +8126,13 @@ "stop", "frequency_penalty", "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", - "logprobs", - "top_logprobs", - "min_p", "seed", + "top_k", + "min_p", + "repetition_penalty", + "logprobs", + "logit_bias", + "top_logprobs", "response_format", "tools", "tool_choice", @@ -8233,7 +8784,7 @@ "instruct_type": null }, "pricing": { - "prompt": "0.00000007", + "prompt": "0.00000006", "completion": "0.00000014", "request": "0", "image": "0", @@ -8242,56 +8793,6 @@ }, "top_provider": { "context_length": 16384, - "max_completion_tokens": 16384, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "seed", - "top_k", - "logit_bias", - "logprobs", - "top_logprobs", - "repetition_penalty", - "response_format", - "min_p" - ] - }, - { - "id": "deepseek/deepseek-chat:free", - "canonical_slug": "deepseek/deepseek-chat-v3", - "hugging_face_id": "deepseek-ai/DeepSeek-V3", - "name": "DeepSeek: DeepSeek V3 (free)", - "created": 1735241320, - "description": "DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations reveal that the model outperforms other open-source models and rivals leading closed-source models.\n\nFor model details, please visit [the DeepSeek-V3 repo](https://github.com/deepseek-ai/DeepSeek-V3) for more information, or see the [launch announcement](https://api-docs.deepseek.com/news/news1226).", - "context_length": 163840, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "DeepSeek", - "instruct_type": null - }, - "pricing": { - "prompt": "0", - "completion": "0", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 163840, "max_completion_tokens": null, "is_moderated": false }, @@ -8303,13 +8804,15 @@ "stop", "frequency_penalty", "presence_penalty", + "response_format", + "structured_outputs", "seed", "top_k", - "min_p", - "repetition_penalty", - "logprobs", "logit_bias", - "top_logprobs" + "logprobs", + "top_logprobs", + "repetition_penalty", + "min_p" ] }, { @@ -8332,8 +8835,8 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000003", - "completion": "0.00000085", + "prompt": "0.000000272", + "completion": "0.000000272", "request": "0", "image": "0", "web_search": "0", @@ -8341,7 +8844,7 @@ }, "top_provider": { "context_length": 163840, - "max_completion_tokens": 163840, + "max_completion_tokens": null, "is_moderated": false }, "per_request_limits": null, @@ -8458,52 +8961,6 @@ "structured_outputs" ] }, - { - "id": "eva-unit-01/eva-llama-3.33-70b", - "canonical_slug": "eva-unit-01/eva-llama-3.33-70b", - "hugging_face_id": "EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1", - "name": "EVA Llama 3.33 70B", - "created": 1734377303, - "description": "EVA Llama 3.33 70b is a roleplay and storywriting specialist model. It is a full-parameter finetune of [Llama-3.3-70B-Instruct](https://openrouter.ai/meta-llama/llama-3.3-70b-instruct) on mixture of synthetic and natural data.\n\nIt uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and \"flavor\" of the resulting model\n\nThis model was built with Llama by Meta.\n", - "context_length": 16384, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Llama3", - "instruct_type": "llama3" - }, - "pricing": { - "prompt": "0.000004", - "completion": "0.000006", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 16384, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "repetition_penalty", - "top_k", - "min_p", - "seed" - ] - }, { "id": "x-ai/grok-2-vision-1212", "canonical_slug": "x-ai/grok-2-vision-1212", @@ -9391,14 +9848,14 @@ "stop", "frequency_penalty", "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", - "min_p", - "response_format", "seed", + "top_k", + "min_p", + "repetition_penalty", "logprobs", - "top_logprobs" + "logit_bias", + "top_logprobs", + "response_format" ] }, { @@ -9725,10 +10182,10 @@ "frequency_penalty", "presence_penalty", "repetition_penalty", + "logit_bias", "top_k", "min_p", "seed", - "logit_bias", "top_a" ] }, @@ -9824,53 +10281,6 @@ "stop" ] }, - { - "id": "mistralai/ministral-3b", - "canonical_slug": "mistralai/ministral-3b", - "hugging_face_id": null, - "name": "Mistral: Ministral 3B", - "created": 1729123200, - "description": "Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it’s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.", - "context_length": 131072, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Mistral", - "instruct_type": null - }, - "pricing": { - "prompt": "0.00000004", - "completion": "0.00000004", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 131072, - "max_completion_tokens": null, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "response_format", - "structured_outputs", - "seed" - ] - }, { "id": "mistralai/ministral-8b", "canonical_slug": "mistralai/ministral-8b", @@ -9918,6 +10328,53 @@ "seed" ] }, + { + "id": "mistralai/ministral-3b", + "canonical_slug": "mistralai/ministral-3b", + "hugging_face_id": null, + "name": "Mistral: Ministral 3B", + "created": 1729123200, + "description": "Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it’s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Mistral", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000004", + "completion": "0.00000004", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "response_format", + "structured_outputs", + "seed" + ] + }, { "id": "qwen/qwen-2.5-7b-instruct", "canonical_slug": "qwen/qwen-2.5-7b-instruct", @@ -10019,12 +10476,12 @@ ] }, { - "id": "inflection/inflection-3-productivity", - "canonical_slug": "inflection/inflection-3-productivity", + "id": "inflection/inflection-3-pi", + "canonical_slug": "inflection/inflection-3-pi", "hugging_face_id": null, - "name": "Inflection: Inflection 3 Productivity", + "name": "Inflection: Inflection 3 Pi", "created": 1728604800, - "description": "Inflection 3 Productivity is optimized for following instructions. It is better for tasks requiring JSON output or precise adherence to provided guidelines. It has access to recent news.\n\nFor emotional intelligence similar to Pi, see [Inflect 3 Pi](/inflection/inflection-3-pi)\n\nSee [Inflection's announcement](https://inflection.ai/blog/enterprise) for more details.", + "description": "Inflection 3 Pi powers Inflection's [Pi](https://pi.ai) chatbot, including backstory, emotional intelligence, productivity, and safety. It has access to recent news, and excels in scenarios like customer support and roleplay.\n\nPi has been trained to mirror your tone and style, if you use more emojis, so will Pi! Try experimenting with various prompts and conversation styles.", "context_length": 8000, "architecture": { "modality": "text->text", @@ -10059,12 +10516,12 @@ ] }, { - "id": "inflection/inflection-3-pi", - "canonical_slug": "inflection/inflection-3-pi", + "id": "inflection/inflection-3-productivity", + "canonical_slug": "inflection/inflection-3-productivity", "hugging_face_id": null, - "name": "Inflection: Inflection 3 Pi", + "name": "Inflection: Inflection 3 Productivity", "created": 1728604800, - "description": "Inflection 3 Pi powers Inflection's [Pi](https://pi.ai) chatbot, including backstory, emotional intelligence, productivity, and safety. It has access to recent news, and excels in scenarios like customer support and roleplay.\n\nPi has been trained to mirror your tone and style, if you use more emojis, so will Pi! Try experimenting with various prompts and conversation styles.", + "description": "Inflection 3 Productivity is optimized for following instructions. It is better for tasks requiring JSON output or precise adherence to provided guidelines. It has access to recent news.\n\nFor emotional intelligence similar to Pi, see [Inflect 3 Pi](/inflection/inflection-3-pi)\n\nSee [Inflection's announcement](https://inflection.ai/blog/enterprise) for more details.", "context_length": 8000, "architecture": { "modality": "text->text", @@ -10148,6 +10605,53 @@ "structured_outputs" ] }, + { + "id": "anthracite-org/magnum-v2-72b", + "canonical_slug": "anthracite-org/magnum-v2-72b", + "hugging_face_id": "anthracite-org/magnum-v2-72b", + "name": "Magnum v2 72B", + "created": 1727654400, + "description": "From the maker of [Goliath](https://openrouter.ai/models/alpindale/goliath-120b), Magnum 72B is the seventh in a family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/models/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.", + "context_length": 32768, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Qwen", + "instruct_type": "chatml" + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000003", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 32768, + "max_completion_tokens": null, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "repetition_penalty", + "logit_bias", + "top_k", + "min_p", + "seed" + ] + }, { "id": "thedrummer/rocinante-12b", "canonical_slug": "thedrummer/rocinante-12b", @@ -10207,7 +10711,7 @@ "name": "Liquid: LFM 40B MoE", "created": 1727654400, "description": "Liquid's 40.3B Mixture of Experts (MoE) model. Liquid Foundation Models (LFMs) are large neural networks built with computational units rooted in dynamic systems.\n\nLFMs are general-purpose AI models that can be used to model any kind of sequential data, including video, audio, text, time series, and signals.\n\nSee the [launch announcement](https://www.liquid.ai/liquid-foundation-models) for benchmarks and more info.", - "context_length": 32768, + "context_length": 65536, "architecture": { "modality": "text->text", "input_modalities": [ @@ -10228,8 +10732,8 @@ "internal_reasoning": "0" }, "top_provider": { - "context_length": 32768, - "max_completion_tokens": null, + "context_length": 65536, + "max_completion_tokens": 65536, "is_moderated": false }, "per_request_limits": null, @@ -10241,60 +10745,13 @@ "frequency_penalty", "presence_penalty", "seed", - "top_k", - "min_p", - "repetition_penalty", "logit_bias", "logprobs", "top_logprobs", - "response_format" - ] - }, - { - "id": "anthracite-org/magnum-v2-72b", - "canonical_slug": "anthracite-org/magnum-v2-72b", - "hugging_face_id": "anthracite-org/magnum-v2-72b", - "name": "Magnum v2 72B", - "created": 1727654400, - "description": "From the maker of [Goliath](https://openrouter.ai/models/alpindale/goliath-120b), Magnum 72B is the seventh in a family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/models/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.", - "context_length": 32768, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Qwen", - "instruct_type": "chatml" - }, - "pricing": { - "prompt": "0.000003", - "completion": "0.000003", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 32768, - "max_completion_tokens": null, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "repetition_penalty", - "logit_bias", - "top_k", + "response_format", "min_p", - "seed" + "repetition_penalty", + "top_k" ] }, { @@ -10660,8 +11117,8 @@ "instruct_type": "chatml" }, "pricing": { - "prompt": "0.00000012", - "completion": "0.00000039", + "prompt": "0.000000101", + "completion": "0.000000101", "request": "0", "image": "0", "web_search": "0", @@ -10669,7 +11126,7 @@ }, "top_provider": { "context_length": 32768, - "max_completion_tokens": 16384, + "max_completion_tokens": null, "is_moderated": false }, "per_request_limits": null, @@ -10680,16 +11137,17 @@ "stop", "frequency_penalty", "presence_penalty", + "response_format", + "structured_outputs", "seed", "top_k", - "logit_bias", + "min_p", + "repetition_penalty", "logprobs", + "logit_bias", "top_logprobs", "tools", - "tool_choice", - "repetition_penalty", - "response_format", - "min_p" + "tool_choice" ] }, { @@ -10743,10 +11201,10 @@ ] }, { - "id": "openai/o1-preview", - "canonical_slug": "openai/o1-preview", + "id": "openai/o1-preview-2024-09-12", + "canonical_slug": "openai/o1-preview-2024-09-12", "hugging_face_id": null, - "name": "OpenAI: o1-preview", + "name": "OpenAI: o1-preview (2024-09-12)", "created": 1726099200, "description": "The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.", "context_length": 128000, @@ -10821,10 +11279,10 @@ ] }, { - "id": "openai/o1-preview-2024-09-12", - "canonical_slug": "openai/o1-preview-2024-09-12", + "id": "openai/o1-preview", + "canonical_slug": "openai/o1-preview", "hugging_face_id": null, - "name": "OpenAI: o1-preview (2024-09-12)", + "name": "OpenAI: o1-preview", "created": 1726099200, "description": "The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.", "context_length": 128000, @@ -10953,12 +11411,12 @@ ] }, { - "id": "cohere/command-r-plus-08-2024", - "canonical_slug": "cohere/command-r-plus-08-2024", + "id": "cohere/command-r-08-2024", + "canonical_slug": "cohere/command-r-08-2024", "hugging_face_id": null, - "name": "Cohere: Command R+ (08-2024)", + "name": "Cohere: Command R (08-2024)", "created": 1724976000, - "description": "command-r-plus-08-2024 is an update of the [Command R+](/models/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint the same.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", + "description": "command-r-08-2024 is an update of the [Command R](/models/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and is competitive with the previous version of the larger Command R+ model.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", "context_length": 128000, "architecture": { "modality": "text->text", @@ -10972,8 +11430,8 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000025", - "completion": "0.00001", + "prompt": "0.00000015", + "completion": "0.0000006", "request": "0", "image": "0", "web_search": "0", @@ -11000,12 +11458,12 @@ ] }, { - "id": "cohere/command-r-08-2024", - "canonical_slug": "cohere/command-r-08-2024", + "id": "cohere/command-r-plus-08-2024", + "canonical_slug": "cohere/command-r-plus-08-2024", "hugging_face_id": null, - "name": "Cohere: Command R (08-2024)", + "name": "Cohere: Command R+ (08-2024)", "created": 1724976000, - "description": "command-r-08-2024 is an update of the [Command R](/models/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and is competitive with the previous version of the larger Command R+ model.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", + "description": "command-r-plus-08-2024 is an update of the [Command R+](/models/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint the same.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", "context_length": 128000, "architecture": { "modality": "text->text", @@ -11019,8 +11477,8 @@ "instruct_type": null }, "pricing": { - "prompt": "0.00000015", - "completion": "0.0000006", + "prompt": "0.0000025", + "completion": "0.00001", "request": "0", "image": "0", "web_search": "0", @@ -11087,13 +11545,13 @@ "stop", "frequency_penalty", "presence_penalty", - "top_k", - "repetition_penalty", - "logit_bias", "logprobs", "top_logprobs", + "seed", + "logit_bias", + "top_k", "min_p", - "seed" + "repetition_penalty" ] }, { @@ -11231,6 +11689,7 @@ "top_k", "seed", "min_p", + "structured_outputs", "logit_bias", "logprobs", "top_logprobs", @@ -11337,52 +11796,6 @@ "structured_outputs" ] }, - { - "id": "aetherwiing/mn-starcannon-12b", - "canonical_slug": "aetherwiing/mn-starcannon-12b", - "hugging_face_id": "aetherwiing/MN-12B-Starcannon-v2", - "name": "Aetherwiing: Starcannon 12B", - "created": 1723507200, - "description": "Starcannon 12B v2 is a creative roleplay and story writing model, based on Mistral Nemo, using [nothingiisreal/mn-celeste-12b](/nothingiisreal/mn-celeste-12b) as a base, with [intervitens/mini-magnum-12b-v1.1](https://huggingface.co/intervitens/mini-magnum-12b-v1.1) merged in using the [TIES](https://arxiv.org/abs/2306.01708) method.\n\nAlthough more similar to Magnum overall, the model remains very creative, with a pleasant writing style. It is recommended for people wanting more variety than Magnum, and yet more verbose prose than Celeste.", - "context_length": 16384, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Mistral", - "instruct_type": "chatml" - }, - "pricing": { - "prompt": "0.0000008", - "completion": "0.0000012", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 16384, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "repetition_penalty", - "top_k", - "min_p", - "seed" - ] - }, { "id": "sao10k/l3-lunaris-8b", "canonical_slug": "sao10k/l3-lunaris-8b", @@ -11580,59 +11993,6 @@ "seed" ] }, - { - "id": "meta-llama/llama-3.1-8b-instruct", - "canonical_slug": "meta-llama/llama-3.1-8b-instruct", - "hugging_face_id": "meta-llama/Meta-Llama-3.1-8B-Instruct", - "name": "Meta: Llama 3.1 8B Instruct", - "created": 1721692800, - "description": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", - "context_length": 131000, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Llama3", - "instruct_type": "llama3" - }, - "pricing": { - "prompt": "0.000000015", - "completion": "0.00000002", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 131000, - "max_completion_tokens": 131000, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "seed", - "logit_bias", - "logprobs", - "top_logprobs", - "response_format", - "min_p", - "repetition_penalty", - "top_k", - "structured_outputs" - ] - }, { "id": "meta-llama/llama-3.1-70b-instruct", "canonical_slug": "meta-llama/llama-3.1-70b-instruct", @@ -11686,6 +12046,59 @@ "structured_outputs" ] }, + { + "id": "meta-llama/llama-3.1-8b-instruct", + "canonical_slug": "meta-llama/llama-3.1-8b-instruct", + "hugging_face_id": "meta-llama/Meta-Llama-3.1-8B-Instruct", + "name": "Meta: Llama 3.1 8B Instruct", + "created": 1721692800, + "description": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3-1/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { + "prompt": "0.000000015", + "completion": "0.00000002", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "logit_bias", + "logprobs", + "top_logprobs", + "response_format", + "min_p", + "repetition_penalty", + "top_k", + "structured_outputs" + ] + }, { "id": "meta-llama/llama-3.1-405b-instruct:free", "canonical_slug": "meta-llama/llama-3.1-405b-instruct", @@ -11873,70 +12286,16 @@ "stop", "frequency_penalty", "presence_penalty", - "top_k", "repetition_penalty", - "logit_bias", - "logprobs", - "top_logprobs", + "response_format", + "top_k", + "seed", "min_p", - "seed", - "response_format", - "tools", - "tool_choice", - "structured_outputs" - ] - }, - { - "id": "openai/gpt-4o-mini-2024-07-18", - "canonical_slug": "openai/gpt-4o-mini-2024-07-18", - "hugging_face_id": null, - "name": "OpenAI: GPT-4o-mini (2024-07-18)", - "created": 1721260800, - "description": "GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal", - "context_length": 128000, - "architecture": { - "modality": "text+image->text", - "input_modalities": [ - "text", - "image", - "file" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "GPT", - "instruct_type": null - }, - "pricing": { - "prompt": "0.00000015", - "completion": "0.0000006", - "request": "0", - "image": "0.007225", - "web_search": "0", - "internal_reasoning": "0", - "input_cache_read": "0.000000075" - }, - "top_provider": { - "context_length": 128000, - "max_completion_tokens": 16384, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "web_search_options", - "seed", - "logit_bias", "logprobs", + "logit_bias", "top_logprobs", - "response_format", + "tools", + "tool_choice", "structured_outputs" ] }, @@ -11994,6 +12353,60 @@ "tool_choice" ] }, + { + "id": "openai/gpt-4o-mini-2024-07-18", + "canonical_slug": "openai/gpt-4o-mini-2024-07-18", + "hugging_face_id": null, + "name": "OpenAI: GPT-4o-mini (2024-07-18)", + "created": 1721260800, + "description": "GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal", + "context_length": 128000, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "text", + "image", + "file" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000015", + "completion": "0.0000006", + "request": "0", + "image": "0.007225", + "web_search": "0", + "internal_reasoning": "0", + "input_cache_read": "0.000000075" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 16384, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "web_search_options", + "seed", + "logit_bias", + "logprobs", + "top_logprobs", + "response_format", + "structured_outputs" + ] + }, { "id": "google/gemma-2-27b-it", "canonical_slug": "google/gemma-2-27b-it", @@ -12014,8 +12427,8 @@ "instruct_type": "gemma" }, "pricing": { - "prompt": "0.0000008", - "completion": "0.0000008", + "prompt": "0.00000065", + "completion": "0.00000065", "request": "0", "image": "0", "web_search": "0", @@ -12023,7 +12436,7 @@ }, "top_provider": { "context_length": 8192, - "max_completion_tokens": 2048, + "max_completion_tokens": null, "is_moderated": false }, "per_request_limits": null, @@ -12034,57 +12447,12 @@ "stop", "frequency_penalty", "presence_penalty", + "response_format", + "structured_outputs", "top_k", "repetition_penalty", "logit_bias", - "min_p", - "response_format" - ] - }, - { - "id": "alpindale/magnum-72b", - "canonical_slug": "alpindale/magnum-72b", - "hugging_face_id": "alpindale/magnum-72b-v1", - "name": "Magnum 72B", - "created": 1720656000, - "description": "From the maker of [Goliath](https://openrouter.ai/models/alpindale/goliath-120b), Magnum 72B is the first in a new family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/models/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.", - "context_length": 16384, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Qwen", - "instruct_type": "chatml" - }, - "pricing": { - "prompt": "0.000004", - "completion": "0.000006", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 16384, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "repetition_penalty", - "top_k", - "min_p", - "seed" + "min_p" ] }, { @@ -12519,6 +12887,56 @@ "logprobs" ] }, + { + "id": "nousresearch/hermes-2-pro-llama-3-8b", + "canonical_slug": "nousresearch/hermes-2-pro-llama-3-8b", + "hugging_face_id": "NousResearch/Hermes-2-Pro-Llama-3-8B", + "name": "NousResearch: Hermes 2 Pro - Llama-3 8B", + "created": 1716768000, + "description": "Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.", + "context_length": 131072, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Llama3", + "instruct_type": "chatml" + }, + "pricing": { + "prompt": "0.000000025", + "completion": "0.00000004", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 131072, + "max_completion_tokens": 131072, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "logit_bias", + "logprobs", + "top_logprobs", + "response_format", + "min_p", + "repetition_penalty", + "top_k" + ] + }, { "id": "mistralai/mistral-7b-instruct:free", "canonical_slug": "mistralai/mistral-7b-instruct", @@ -12619,56 +13037,6 @@ "tool_choice" ] }, - { - "id": "nousresearch/hermes-2-pro-llama-3-8b", - "canonical_slug": "nousresearch/hermes-2-pro-llama-3-8b", - "hugging_face_id": "NousResearch/Hermes-2-Pro-Llama-3-8B", - "name": "NousResearch: Hermes 2 Pro - Llama-3 8B", - "created": 1716768000, - "description": "Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.", - "context_length": 131072, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Llama3", - "instruct_type": "chatml" - }, - "pricing": { - "prompt": "0.000000025", - "completion": "0.00000004", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 131072, - "max_completion_tokens": 131072, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "seed", - "logit_bias", - "logprobs", - "top_logprobs", - "response_format", - "min_p", - "repetition_penalty", - "top_k" - ] - }, { "id": "microsoft/phi-3-mini-128k-instruct", "canonical_slug": "microsoft/phi-3-mini-128k-instruct", @@ -12847,59 +13215,6 @@ "structured_outputs" ] }, - { - "id": "openai/gpt-4o-2024-05-13", - "canonical_slug": "openai/gpt-4o-2024-05-13", - "hugging_face_id": null, - "name": "OpenAI: GPT-4o (2024-05-13)", - "created": 1715558400, - "description": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal", - "context_length": 128000, - "architecture": { - "modality": "text+image->text", - "input_modalities": [ - "text", - "image", - "file" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "GPT", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000005", - "completion": "0.000015", - "request": "0", - "image": "0.007225", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 128000, - "max_completion_tokens": 4096, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "web_search_options", - "seed", - "logit_bias", - "logprobs", - "top_logprobs", - "response_format", - "structured_outputs" - ] - }, { "id": "meta-llama/llama-guard-2-8b", "canonical_slug": "meta-llama/llama-guard-2-8b", @@ -13054,6 +13369,59 @@ "structured_outputs" ] }, + { + "id": "openai/gpt-4o-2024-05-13", + "canonical_slug": "openai/gpt-4o-2024-05-13", + "hugging_face_id": null, + "name": "OpenAI: GPT-4o (2024-05-13)", + "created": 1715558400, + "description": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal", + "context_length": 128000, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "text", + "image", + "file" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "GPT", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000005", + "completion": "0.000015", + "request": "0", + "image": "0.007225", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 128000, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "web_search_options", + "seed", + "logit_bias", + "logprobs", + "top_logprobs", + "response_format", + "structured_outputs" + ] + }, { "id": "sao10k/fimbulvetr-11b-v2", "canonical_slug": "sao10k/fimbulvetr-11b-v2", @@ -13100,58 +13468,6 @@ "seed" ] }, - { - "id": "meta-llama/llama-3-8b-instruct", - "canonical_slug": "meta-llama/llama-3-8b-instruct", - "hugging_face_id": "meta-llama/Meta-Llama-3-8B-Instruct", - "name": "Meta: Llama 3 8B Instruct", - "created": 1713398400, - "description": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", - "context_length": 8192, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Llama3", - "instruct_type": "llama3" - }, - "pricing": { - "prompt": "0.00000003", - "completion": "0.00000006", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 8192, - "max_completion_tokens": 16384, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "top_k", - "seed", - "repetition_penalty", - "frequency_penalty", - "presence_penalty", - "stop", - "min_p", - "logit_bias", - "tools", - "tool_choice", - "response_format", - "top_logprobs", - "logprobs" - ] - }, { "id": "meta-llama/llama-3-70b-instruct", "canonical_slug": "meta-llama/llama-3-70b-instruct", @@ -13204,6 +13520,58 @@ "tool_choice" ] }, + { + "id": "meta-llama/llama-3-8b-instruct", + "canonical_slug": "meta-llama/llama-3-8b-instruct", + "hugging_face_id": "meta-llama/Meta-Llama-3-8B-Instruct", + "name": "Meta: Llama 3 8B Instruct", + "created": 1713398400, + "description": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).", + "context_length": 8192, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Llama3", + "instruct_type": "llama3" + }, + "pricing": { + "prompt": "0.00000003", + "completion": "0.00000006", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 8192, + "max_completion_tokens": 16384, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "top_k", + "seed", + "repetition_penalty", + "frequency_penalty", + "presence_penalty", + "stop", + "min_p", + "logit_bias", + "tools", + "tool_choice", + "response_format", + "top_logprobs", + "logprobs" + ] + }, { "id": "mistralai/mixtral-8x22b-instruct", "canonical_slug": "mistralai/mixtral-8x22b-instruct", @@ -13304,54 +13672,6 @@ "response_format" ] }, - { - "id": "google/gemini-pro-1.5", - "canonical_slug": "google/gemini-pro-1.5", - "hugging_face_id": null, - "name": "Google: Gemini 1.5 Pro", - "created": 1712620800, - "description": "Google's latest multimodal model, supports image and video[0] in text or chat prompts.\n\nOptimized for language tasks including:\n\n- Code generation\n- Text generation\n- Text editing\n- Problem solving\n- Recommendations\n- Information extraction\n- Data extraction or generation\n- AI agents\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n* [0]: Video input is not available through OpenRouter at this time.", - "context_length": 2000000, - "architecture": { - "modality": "text+image->text", - "input_modalities": [ - "text", - "image" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Gemini", - "instruct_type": null - }, - "pricing": { - "prompt": "0.00000125", - "completion": "0.000005", - "request": "0", - "image": "0.0006575", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 2000000, - "max_completion_tokens": 8192, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "stop", - "frequency_penalty", - "presence_penalty", - "seed", - "response_format", - "structured_outputs" - ] - }, { "id": "openai/gpt-4-turbo", "canonical_slug": "openai/gpt-4-turbo", @@ -13402,6 +13722,54 @@ "response_format" ] }, + { + "id": "google/gemini-pro-1.5", + "canonical_slug": "google/gemini-pro-1.5", + "hugging_face_id": null, + "name": "Google: Gemini 1.5 Pro", + "created": 1712620800, + "description": "Google's latest multimodal model, supports image and video[0] in text or chat prompts.\n\nOptimized for language tasks including:\n\n- Code generation\n- Text generation\n- Text editing\n- Problem solving\n- Recommendations\n- Information extraction\n- Data extraction or generation\n- AI agents\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n* [0]: Video input is not available through OpenRouter at this time.", + "context_length": 2000000, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "text", + "image" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Gemini", + "instruct_type": null + }, + "pricing": { + "prompt": "0.00000125", + "completion": "0.000005", + "request": "0", + "image": "0.0006575", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 2000000, + "max_completion_tokens": 8192, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "response_format", + "structured_outputs" + ] + }, { "id": "cohere/command-r-plus", "canonical_slug": "cohere/command-r-plus", @@ -13543,6 +13911,52 @@ "logit_bias" ] }, + { + "id": "cohere/command", + "canonical_slug": "cohere/command", + "hugging_face_id": null, + "name": "Cohere: Command", + "created": 1710374400, + "description": "Command is an instruction-following conversational model that performs language tasks with high quality, more reliably and with a longer context than our base generative models.\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", + "context_length": 4096, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Cohere", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000001", + "completion": "0.000002", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 4096, + "max_completion_tokens": 4000, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "top_k", + "seed", + "response_format", + "structured_outputs" + ] + }, { "id": "cohere/command-r", "canonical_slug": "cohere/command-r", @@ -13590,52 +14004,6 @@ "structured_outputs" ] }, - { - "id": "cohere/command", - "canonical_slug": "cohere/command", - "hugging_face_id": null, - "name": "Cohere: Command", - "created": 1710374400, - "description": "Command is an instruction-following conversational model that performs language tasks with high quality, more reliably and with a longer context than our base generative models.\n\nUse of this model is subject to Cohere's [Usage Policy](https://docs.cohere.com/docs/usage-policy) and [SaaS Agreement](https://cohere.com/saas-agreement).", - "context_length": 4096, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Cohere", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000001", - "completion": "0.000002", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 4096, - "max_completion_tokens": 4000, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "top_k", - "seed", - "response_format", - "structured_outputs" - ] - }, { "id": "anthropic/claude-3-haiku:beta", "canonical_slug": "anthropic/claude-3-haiku", @@ -13728,6 +14096,52 @@ "stop" ] }, + { + "id": "anthropic/claude-3-sonnet", + "canonical_slug": "anthropic/claude-3-sonnet", + "hugging_face_id": null, + "name": "Anthropic: Claude 3 Sonnet", + "created": 1709596800, + "description": "Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal", + "context_length": 200000, + "architecture": { + "modality": "text+image->text", + "input_modalities": [ + "text", + "image" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "Claude", + "instruct_type": null + }, + "pricing": { + "prompt": "0.000003", + "completion": "0.000015", + "request": "0", + "image": "0.0048", + "web_search": "0", + "internal_reasoning": "0", + "input_cache_read": "0.0000003", + "input_cache_write": "0.00000375" + }, + "top_provider": { + "context_length": 200000, + "max_completion_tokens": 4096, + "is_moderated": false + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "tools", + "tool_choice", + "top_k", + "stop" + ] + }, { "id": "anthropic/claude-3-opus:beta", "canonical_slug": "anthropic/claude-3-opus", @@ -13820,98 +14234,6 @@ "stop" ] }, - { - "id": "anthropic/claude-3-sonnet:beta", - "canonical_slug": "anthropic/claude-3-sonnet", - "hugging_face_id": null, - "name": "Anthropic: Claude 3 Sonnet (self-moderated)", - "created": 1709596800, - "description": "Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal", - "context_length": 200000, - "architecture": { - "modality": "text+image->text", - "input_modalities": [ - "text", - "image" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000003", - "completion": "0.000015", - "request": "0", - "image": "0.0048", - "web_search": "0", - "internal_reasoning": "0", - "input_cache_read": "0.0000003", - "input_cache_write": "0.00000375" - }, - "top_provider": { - "context_length": 200000, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "top_k", - "stop" - ] - }, - { - "id": "anthropic/claude-3-sonnet", - "canonical_slug": "anthropic/claude-3-sonnet", - "hugging_face_id": null, - "name": "Anthropic: Claude 3 Sonnet", - "created": 1709596800, - "description": "Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal", - "context_length": 200000, - "architecture": { - "modality": "text+image->text", - "input_modalities": [ - "text", - "image" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000003", - "completion": "0.000015", - "request": "0", - "image": "0.0048", - "web_search": "0", - "internal_reasoning": "0", - "input_cache_read": "0.0000003", - "input_cache_write": "0.00000375" - }, - "top_provider": { - "context_length": 200000, - "max_completion_tokens": 4096, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "tools", - "tool_choice", - "top_k", - "stop" - ] - }, { "id": "cohere/command-r-03-2024", "canonical_slug": "cohere/command-r-03-2024", @@ -14154,12 +14476,12 @@ ] }, { - "id": "mistralai/mistral-tiny", - "canonical_slug": "mistralai/mistral-tiny", + "id": "mistralai/mistral-small", + "canonical_slug": "mistralai/mistral-small", "hugging_face_id": null, - "name": "Mistral Tiny", + "name": "Mistral Small", "created": 1704844800, - "description": "Note: This model is being deprecated. Recommended replacement is the newer [Ministral 8B](/mistral/ministral-8b)\n\nThis model is currently powered by Mistral-7B-v0.2, and incorporates a \"better\" fine-tuning than [Mistral 7B](/models/mistralai/mistral-7b-instruct-v0.1), inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.", + "description": "With 22 billion parameters, Mistral Small v24.09 offers a convenient mid-point between (Mistral NeMo 12B)[/mistralai/mistral-nemo] and (Mistral Large 2)[/mistralai/mistral-large], providing a cost-effective solution that can be deployed across various platforms and environments. It has better reasoning, exhibits more capabilities, can produce and reason about code, and is multiligual, supporting English, French, German, Italian, and Spanish.", "context_length": 32768, "architecture": { "modality": "text->text", @@ -14173,8 +14495,8 @@ "instruct_type": null }, "pricing": { - "prompt": "0.00000025", - "completion": "0.00000025", + "prompt": "0.0000002", + "completion": "0.0000006", "request": "0", "image": "0", "web_search": "0", @@ -14201,12 +14523,12 @@ ] }, { - "id": "mistralai/mistral-small", - "canonical_slug": "mistralai/mistral-small", + "id": "mistralai/mistral-tiny", + "canonical_slug": "mistralai/mistral-tiny", "hugging_face_id": null, - "name": "Mistral Small", + "name": "Mistral Tiny", "created": 1704844800, - "description": "With 22 billion parameters, Mistral Small v24.09 offers a convenient mid-point between (Mistral NeMo 12B)[/mistralai/mistral-nemo] and (Mistral Large 2)[/mistralai/mistral-large], providing a cost-effective solution that can be deployed across various platforms and environments. It has better reasoning, exhibits more capabilities, can produce and reason about code, and is multiligual, supporting English, French, German, Italian, and Spanish.", + "description": "Note: This model is being deprecated. Recommended replacement is the newer [Ministral 8B](/mistral/ministral-8b)\n\nThis model is currently powered by Mistral-7B-v0.2, and incorporates a \"better\" fine-tuning than [Mistral 7B](/models/mistralai/mistral-7b-instruct-v0.1), inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.", "context_length": 32768, "architecture": { "modality": "text->text", @@ -14220,8 +14542,8 @@ "instruct_type": null }, "pricing": { - "prompt": "0.0000002", - "completion": "0.0000006", + "prompt": "0.00000025", + "completion": "0.00000025", "request": "0", "image": "0", "web_search": "0", @@ -14393,13 +14715,13 @@ ] }, { - "id": "anthropic/claude-2.1:beta", - "canonical_slug": "anthropic/claude-2.1", - "hugging_face_id": null, - "name": "Anthropic: Claude v2.1 (self-moderated)", - "created": 1700611200, - "description": "Claude 2 delivers advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.", - "context_length": 200000, + "id": "undi95/toppy-m-7b", + "canonical_slug": "undi95/toppy-m-7b", + "hugging_face_id": "Undi95/Toppy-M-7B", + "name": "Toppy M 7B", + "created": 1699574400, + "description": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/models/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored", + "context_length": 4096, "architecture": { "modality": "text->text", "input_modalities": [ @@ -14408,19 +14730,19 @@ "output_modalities": [ "text" ], - "tokenizer": "Claude", - "instruct_type": null + "tokenizer": "Mistral", + "instruct_type": "alpaca" }, "pricing": { - "prompt": "0.000008", - "completion": "0.000024", + "prompt": "0.0000008", + "completion": "0.0000012", "request": "0", "image": "0", "web_search": "0", "internal_reasoning": "0" }, "top_provider": { - "context_length": 200000, + "context_length": 4096, "max_completion_tokens": 4096, "is_moderated": false }, @@ -14429,131 +14751,13 @@ "max_tokens", "temperature", "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "repetition_penalty", "top_k", - "stop" - ] - }, - { - "id": "anthropic/claude-2.1", - "canonical_slug": "anthropic/claude-2.1", - "hugging_face_id": null, - "name": "Anthropic: Claude v2.1", - "created": 1700611200, - "description": "Claude 2 delivers advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.", - "context_length": 200000, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000008", - "completion": "0.000024", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 200000, - "max_completion_tokens": 4096, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "top_k", - "stop" - ] - }, - { - "id": "anthropic/claude-2:beta", - "canonical_slug": "anthropic/claude-2", - "hugging_face_id": null, - "name": "Anthropic: Claude v2 (self-moderated)", - "created": 1700611200, - "description": "Claude 2 delivers advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.", - "context_length": 200000, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000008", - "completion": "0.000024", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 200000, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "top_k", - "stop" - ] - }, - { - "id": "anthropic/claude-2", - "canonical_slug": "anthropic/claude-2", - "hugging_face_id": null, - "name": "Anthropic: Claude v2", - "created": 1700611200, - "description": "Claude 2 delivers advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.", - "context_length": 200000, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000008", - "completion": "0.000024", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 200000, - "max_completion_tokens": 4096, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "top_k", - "stop" + "min_p", + "seed" ] }, { @@ -14606,52 +14810,6 @@ "top_a" ] }, - { - "id": "undi95/toppy-m-7b", - "canonical_slug": "undi95/toppy-m-7b", - "hugging_face_id": "Undi95/Toppy-M-7B", - "name": "Toppy M 7B", - "created": 1699574400, - "description": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/models/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored", - "context_length": 4096, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Mistral", - "instruct_type": "alpaca" - }, - "pricing": { - "prompt": "0.0000008", - "completion": "0.0000012", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 4096, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "repetition_penalty", - "top_k", - "min_p", - "seed" - ] - }, { "id": "openrouter/auto", "canonical_slug": "openrouter/auto", @@ -14733,6 +14891,53 @@ "structured_outputs" ] }, + { + "id": "openai/gpt-3.5-turbo-instruct", + "canonical_slug": "openai/gpt-3.5-turbo-instruct", + "hugging_face_id": null, + "name": "OpenAI: GPT-3.5 Turbo Instruct", + "created": 1695859200, + "description": "This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.", + "context_length": 4095, + "architecture": { + "modality": "text->text", + "input_modalities": [ + "text" + ], + "output_modalities": [ + "text" + ], + "tokenizer": "GPT", + "instruct_type": "chatml" + }, + "pricing": { + "prompt": "0.0000015", + "completion": "0.000002", + "request": "0", + "image": "0", + "web_search": "0", + "internal_reasoning": "0" + }, + "top_provider": { + "context_length": 4095, + "max_completion_tokens": 4096, + "is_moderated": true + }, + "per_request_limits": null, + "supported_parameters": [ + "max_tokens", + "temperature", + "top_p", + "stop", + "frequency_penalty", + "presence_penalty", + "seed", + "logit_bias", + "logprobs", + "top_logprobs", + "response_format" + ] + }, { "id": "mistralai/mistral-7b-instruct-v0.1", "canonical_slug": "mistralai/mistral-7b-instruct-v0.1", @@ -14783,53 +14988,6 @@ "seed" ] }, - { - "id": "openai/gpt-3.5-turbo-instruct", - "canonical_slug": "openai/gpt-3.5-turbo-instruct", - "hugging_face_id": null, - "name": "OpenAI: GPT-3.5 Turbo Instruct", - "created": 1695859200, - "description": "This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.", - "context_length": 4095, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "GPT", - "instruct_type": "chatml" - }, - "pricing": { - "prompt": "0.0000015", - "completion": "0.000002", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 4095, - "max_completion_tokens": 4096, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "stop", - "frequency_penalty", - "presence_penalty", - "seed", - "logit_bias", - "logprobs", - "top_logprobs", - "response_format" - ] - }, { "id": "pygmalionai/mythalion-13b", "canonical_slug": "pygmalionai/mythalion-13b", @@ -14973,88 +15131,6 @@ "top_a" ] }, - { - "id": "anthropic/claude-2.0:beta", - "canonical_slug": "anthropic/claude-2.0", - "hugging_face_id": null, - "name": "Anthropic: Claude v2.0 (self-moderated)", - "created": 1690502400, - "description": "Anthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.", - "context_length": 100000, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000008", - "completion": "0.000024", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 100000, - "max_completion_tokens": 4096, - "is_moderated": false - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "top_k", - "stop" - ] - }, - { - "id": "anthropic/claude-2.0", - "canonical_slug": "anthropic/claude-2.0", - "hugging_face_id": null, - "name": "Anthropic: Claude v2.0", - "created": 1690502400, - "description": "Anthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.", - "context_length": 100000, - "architecture": { - "modality": "text->text", - "input_modalities": [ - "text" - ], - "output_modalities": [ - "text" - ], - "tokenizer": "Claude", - "instruct_type": null - }, - "pricing": { - "prompt": "0.000008", - "completion": "0.000024", - "request": "0", - "image": "0", - "web_search": "0", - "internal_reasoning": "0" - }, - "top_provider": { - "context_length": 100000, - "max_completion_tokens": 4096, - "is_moderated": true - }, - "per_request_limits": null, - "supported_parameters": [ - "max_tokens", - "temperature", - "top_p", - "top_k", - "stop" - ] - }, { "id": "undi95/remm-slerp-l2-13b", "canonical_slug": "undi95/remm-slerp-l2-13b", @@ -15156,12 +15232,12 @@ ] }, { - "id": "openai/gpt-4", - "canonical_slug": "openai/gpt-4", + "id": "openai/gpt-4-0314", + "canonical_slug": "openai/gpt-4-0314", "hugging_face_id": null, - "name": "OpenAI: GPT-4", + "name": "OpenAI: GPT-4 (older v0314)", "created": 1685232000, - "description": "OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning capabilities. Training data: up to Sep 2021.", + "description": "GPT-4-0314 is the first version of GPT-4 released, with a context length of 8,192 tokens, and was supported until June 14. Training data: up to Sep 2021.", "context_length": 8191, "architecture": { "modality": "text->text", @@ -15201,7 +15277,8 @@ "logit_bias", "logprobs", "top_logprobs", - "response_format" + "response_format", + "structured_outputs" ] }, { @@ -15254,12 +15331,12 @@ ] }, { - "id": "openai/gpt-4-0314", - "canonical_slug": "openai/gpt-4-0314", + "id": "openai/gpt-4", + "canonical_slug": "openai/gpt-4", "hugging_face_id": null, - "name": "OpenAI: GPT-4 (older v0314)", + "name": "OpenAI: GPT-4", "created": 1685232000, - "description": "GPT-4-0314 is the first version of GPT-4 released, with a context length of 8,192 tokens, and was supported until June 14. Training data: up to Sep 2021.", + "description": "OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning capabilities. Training data: up to Sep 2021.", "context_length": 8191, "architecture": { "modality": "text->text", @@ -15299,8 +15376,7 @@ "logit_bias", "logprobs", "top_logprobs", - "response_format", - "structured_outputs" + "response_format" ] } ] diff --git a/packages/kbot/dist-in/src/models/cache/openrouter.ts b/packages/kbot/dist-in/src/models/cache/openrouter.ts index 82de1206..70ce1935 100644 --- a/packages/kbot/dist-in/src/models/cache/openrouter.ts +++ b/packages/kbot/dist-in/src/models/cache/openrouter.ts @@ -1 +1 @@ -export const models = [{"id":"switchpoint/router","name":"Switchpoint Router","pricing":{"prompt":"0.00000085","completion":"0.0000034","request":"0","image":"0","web_search":"0","internal_reasoning":"0"},"created":1752272899,"top_provider":{"context_length":131072,"max_completion_tokens":null,"is_moderated":false}},{"id":"moonshotai/kimi-k2:free","name":"MoonshotAI: Kimi K2 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paid\nthudm/glm-4-32b:free | free\nthudm/glm-4.1v-9b-thinking | paid\nthudm/glm-z1-32b:free | free\ntngtech/deepseek-r1t-chimera:free | free\ntngtech/deepseek-r1t2-chimera:free | free\nundi95/toppy-m-7b | paid\nscb10x/llama3.1-typhoon2-70b-instruct | paid\ncognitivecomputations/dolphin-mistral-24b-venice-edition:free | free\nmicrosoft/wizardlm-2-8x22b | paid\nx-ai/grok-2-1212 | paid\nx-ai/grok-2-vision-1212 | paid\nx-ai/grok-3 | paid\nx-ai/grok-3-beta | paid\nx-ai/grok-3-mini | paid\nx-ai/grok-3-mini-beta | paid\nx-ai/grok-4 | paid\nx-ai/grok-vision-beta | paid\n\u001b[35m\u001b[1m\u001b[22m\u001b[39m\n\u001b[35m\u001b[1m OpenAI models:\u001b[22m\u001b[39m\n\u001b[35m\u001b[1m\u001b[22m\u001b[39m\nbabbage-002\nchatgpt-4o-latest\ncodex-mini-latest\ndall-e-2\ndall-e-3\ndavinci-002\ngpt-3.5-turbo\ngpt-3.5-turbo-0125\ngpt-3.5-turbo-1106\ngpt-3.5-turbo-16k\ngpt-3.5-turbo-instruct\ngpt-3.5-turbo-instruct-0914\ngpt-4\ngpt-4-0125-preview\ngpt-4-0613\ngpt-4-1106-preview\ngpt-4-turbo\ngpt-4-turbo-2024-04-09\ngpt-4-turbo-preview\ngpt-4.1\ngpt-4.1-2025-04-14\ngpt-4.1-mini\ngpt-4.1-mini-2025-04-14\ngpt-4.1-nano\ngpt-4.1-nano-2025-04-14\ngpt-4o\ngpt-4o-2024-05-13\ngpt-4o-2024-08-06\ngpt-4o-2024-11-20\ngpt-4o-audio-preview\ngpt-4o-audio-preview-2024-10-01\ngpt-4o-audio-preview-2024-12-17\ngpt-4o-audio-preview-2025-06-03\ngpt-4o-mini\ngpt-4o-mini-2024-07-18\ngpt-4o-mini-audio-preview\ngpt-4o-mini-audio-preview-2024-12-17\ngpt-4o-mini-realtime-preview\ngpt-4o-mini-realtime-preview-2024-12-17\ngpt-4o-mini-search-preview\ngpt-4o-mini-search-preview-2025-03-11\ngpt-4o-mini-transcribe\ngpt-4o-mini-tts\ngpt-4o-realtime-preview\ngpt-4o-realtime-preview-2024-10-01\ngpt-4o-realtime-preview-2024-12-17\ngpt-4o-realtime-preview-2025-06-03\ngpt-4o-search-preview\ngpt-4o-search-preview-2025-03-11\ngpt-4o-transcribe\ngpt-image-1\no1\no1-2024-12-17\no1-mini\no1-mini-2024-09-12\no1-preview\no1-preview-2024-09-12\no1-pro\no1-pro-2025-03-19\no3-mini\no3-mini-2025-01-31\no4-mini\no4-mini-2025-04-16\no4-mini-deep-research\no4-mini-deep-research-2025-06-26\nomni-moderation-2024-09-26\nomni-moderation-latest\ntext-embedding-3-large\ntext-embedding-3-small\ntext-embedding-ada-002\ntts-1\ntts-1-1106\ntts-1-hd\ntts-1-hd-1106\nwhisper-1\n-----\n\n\u001b[35m\u001b[1m\u001b[22m\u001b[39m\n\u001b[35m\u001b[1m Deepseek models:\u001b[22m\u001b[39m\n\u001b[35m\u001b[1m\u001b[22m\u001b[39m\ndeepseek-chat\ndeepseek-reasoner\n-----\n", "ui:title": "Model" }, "router": { diff --git a/packages/kbot/src/models/cache/openrouter-models-free.ts b/packages/kbot/src/models/cache/openrouter-models-free.ts index 3c5be551..141e621a 100644 --- a/packages/kbot/src/models/cache/openrouter-models-free.ts +++ b/packages/kbot/src/models/cache/openrouter-models-free.ts @@ -1,4 +1,6 @@ export enum E_OPENROUTER_MODEL_FREE { + MODEL_FREE_QWEN_QWEN3_CODER_FREE = "qwen/qwen3-coder:free", + MODEL_FREE_QWEN_QWEN3_235B_A22B_2507_FREE = "qwen/qwen3-235b-a22b-2507:free", MODEL_FREE_MOONSHOTAI_KIMI_K2_FREE = "moonshotai/kimi-k2:free", MODEL_FREE_COGNITIVECOMPUTATIONS_DOLPHIN_MISTRAL_24B_VENICE_EDITION_FREE = "cognitivecomputations/dolphin-mistral-24b-venice-edition:free", MODEL_FREE_GOOGLE_GEMMA_3N_E2B_IT_FREE = "google/gemma-3n-e2b-it:free", @@ -15,7 +17,6 @@ export enum E_OPENROUTER_MODEL_FREE { MODEL_FREE_QWEN_QWEN3_30B_A3B_FREE = "qwen/qwen3-30b-a3b:free", MODEL_FREE_QWEN_QWEN3_8B_FREE = "qwen/qwen3-8b:free", MODEL_FREE_QWEN_QWEN3_14B_FREE = "qwen/qwen3-14b:free", - MODEL_FREE_QWEN_QWEN3_32B_FREE = "qwen/qwen3-32b:free", MODEL_FREE_QWEN_QWEN3_235B_A22B_FREE = "qwen/qwen3-235b-a22b:free", MODEL_FREE_TNGTECH_DEEPSEEK_R1T_CHIMERA_FREE = "tngtech/deepseek-r1t-chimera:free", MODEL_FREE_MICROSOFT_MAI_DS_R1_FREE = "microsoft/mai-ds-r1:free", @@ -26,7 +27,6 @@ export enum E_OPENROUTER_MODEL_FREE { MODEL_FREE_AGENTICA_ORG_DEEPCODER_14B_PREVIEW_FREE = "agentica-org/deepcoder-14b-preview:free", MODEL_FREE_MOONSHOTAI_KIMI_VL_A3B_THINKING_FREE = "moonshotai/kimi-vl-a3b-thinking:free", MODEL_FREE_NVIDIA_LLAMA_3_1_NEMOTRON_ULTRA_253B_V1_FREE = "nvidia/llama-3.1-nemotron-ultra-253b-v1:free", - MODEL_FREE_DEEPSEEK_DEEPSEEK_V3_BASE_FREE = "deepseek/deepseek-v3-base:free", MODEL_FREE_GOOGLE_GEMINI_2_5_PRO_EXP_03_25 = "google/gemini-2.5-pro-exp-03-25", MODEL_FREE_QWEN_QWEN2_5_VL_32B_INSTRUCT_FREE = "qwen/qwen2.5-vl-32b-instruct:free", MODEL_FREE_DEEPSEEK_DEEPSEEK_CHAT_V3_0324_FREE = "deepseek/deepseek-chat-v3-0324:free", @@ -45,7 +45,6 @@ export enum E_OPENROUTER_MODEL_FREE { MODEL_FREE_DEEPSEEK_DEEPSEEK_R1_DISTILL_QWEN_14B_FREE = "deepseek/deepseek-r1-distill-qwen-14b:free", MODEL_FREE_DEEPSEEK_DEEPSEEK_R1_DISTILL_LLAMA_70B_FREE = "deepseek/deepseek-r1-distill-llama-70b:free", MODEL_FREE_DEEPSEEK_DEEPSEEK_R1_FREE = "deepseek/deepseek-r1:free", - MODEL_FREE_DEEPSEEK_DEEPSEEK_CHAT_FREE = "deepseek/deepseek-chat:free", MODEL_FREE_GOOGLE_GEMINI_2_0_FLASH_EXP_FREE = "google/gemini-2.0-flash-exp:free", MODEL_FREE_META_LLAMA_LLAMA_3_3_70B_INSTRUCT_FREE = "meta-llama/llama-3.3-70b-instruct:free", MODEL_FREE_QWEN_QWEN_2_5_CODER_32B_INSTRUCT_FREE = "qwen/qwen-2.5-coder-32b-instruct:free", diff --git a/packages/kbot/src/models/cache/openrouter-models.ts b/packages/kbot/src/models/cache/openrouter-models.ts index 76db5f97..2b235fc3 100644 --- a/packages/kbot/src/models/cache/openrouter-models.ts +++ b/packages/kbot/src/models/cache/openrouter-models.ts @@ -1,4 +1,12 @@ export enum E_OPENROUTER_MODEL { + MODEL_QWEN_QWEN3_235B_A22B_THINKING_2507 = "qwen/qwen3-235b-a22b-thinking-2507", + MODEL_Z_AI_GLM_4_32B = "z-ai/glm-4-32b", + MODEL_QWEN_QWEN3_CODER_FREE = "qwen/qwen3-coder:free", + MODEL_QWEN_QWEN3_CODER = "qwen/qwen3-coder", + MODEL_BYTEDANCE_UI_TARS_1_5_7B = "bytedance/ui-tars-1.5-7b", + MODEL_GOOGLE_GEMINI_2_5_FLASH_LITE = "google/gemini-2.5-flash-lite", + MODEL_QWEN_QWEN3_235B_A22B_2507_FREE = "qwen/qwen3-235b-a22b-2507:free", + MODEL_QWEN_QWEN3_235B_A22B_2507 = "qwen/qwen3-235b-a22b-2507", MODEL_SWITCHPOINT_ROUTER = "switchpoint/router", MODEL_MOONSHOTAI_KIMI_K2_FREE = "moonshotai/kimi-k2:free", MODEL_MOONSHOTAI_KIMI_K2 = "moonshotai/kimi-k2", @@ -11,6 +19,7 @@ export enum E_OPENROUTER_MODEL { MODEL_TENCENT_HUNYUAN_A13B_INSTRUCT_FREE = "tencent/hunyuan-a13b-instruct:free", MODEL_TENCENT_HUNYUAN_A13B_INSTRUCT = "tencent/hunyuan-a13b-instruct", MODEL_TNGTECH_DEEPSEEK_R1T2_CHIMERA_FREE = "tngtech/deepseek-r1t2-chimera:free", + MODEL_TNGTECH_DEEPSEEK_R1T2_CHIMERA = "tngtech/deepseek-r1t2-chimera", MODEL_MORPH_MORPH_V3_LARGE = "morph/morph-v3-large", MODEL_MORPH_MORPH_V3_FAST = "morph/morph-v3-fast", MODEL_BAIDU_ERNIE_4_5_300B_A47B = "baidu/ernie-4.5-300b-a47b", @@ -46,15 +55,13 @@ export enum E_OPENROUTER_MODEL { MODEL_GOOGLE_GEMMA_3N_E4B_IT_FREE = "google/gemma-3n-e4b-it:free", MODEL_GOOGLE_GEMMA_3N_E4B_IT = "google/gemma-3n-e4b-it", MODEL_OPENAI_CODEX_MINI = "openai/codex-mini", + MODEL_NOUSRESEARCH_DEEPHERMES_3_MISTRAL_24B_PREVIEW = "nousresearch/deephermes-3-mistral-24b-preview", MODEL_MISTRALAI_MISTRAL_MEDIUM_3 = "mistralai/mistral-medium-3", MODEL_GOOGLE_GEMINI_2_5_PRO_PREVIEW_05_06 = "google/gemini-2.5-pro-preview-05-06", - MODEL_ARCEE_AI_CALLER_LARGE = "arcee-ai/caller-large", MODEL_ARCEE_AI_SPOTLIGHT = "arcee-ai/spotlight", MODEL_ARCEE_AI_MAESTRO_REASONING = "arcee-ai/maestro-reasoning", MODEL_ARCEE_AI_VIRTUOSO_LARGE = "arcee-ai/virtuoso-large", MODEL_ARCEE_AI_CODER_LARGE = "arcee-ai/coder-large", - MODEL_ARCEE_AI_VIRTUOSO_MEDIUM_V2 = "arcee-ai/virtuoso-medium-v2", - MODEL_ARCEE_AI_ARCEE_BLITZ = "arcee-ai/arcee-blitz", MODEL_MICROSOFT_PHI_4_REASONING_PLUS = "microsoft/phi-4-reasoning-plus", MODEL_INCEPTION_MERCURY_CODER = "inception/mercury-coder", MODEL_QWEN_QWEN3_4B_FREE = "qwen/qwen3-4b:free", @@ -67,28 +74,32 @@ export enum E_OPENROUTER_MODEL { MODEL_QWEN_QWEN3_8B = "qwen/qwen3-8b", MODEL_QWEN_QWEN3_14B_FREE = "qwen/qwen3-14b:free", MODEL_QWEN_QWEN3_14B = "qwen/qwen3-14b", - MODEL_QWEN_QWEN3_32B_FREE = "qwen/qwen3-32b:free", MODEL_QWEN_QWEN3_32B = "qwen/qwen3-32b", MODEL_QWEN_QWEN3_235B_A22B_FREE = "qwen/qwen3-235b-a22b:free", MODEL_QWEN_QWEN3_235B_A22B = "qwen/qwen3-235b-a22b", MODEL_TNGTECH_DEEPSEEK_R1T_CHIMERA_FREE = "tngtech/deepseek-r1t-chimera:free", MODEL_MICROSOFT_MAI_DS_R1_FREE = "microsoft/mai-ds-r1:free", + MODEL_MICROSOFT_MAI_DS_R1 = "microsoft/mai-ds-r1", MODEL_THUDM_GLM_Z1_32B_FREE = "thudm/glm-z1-32b:free", + MODEL_THUDM_GLM_Z1_32B = "thudm/glm-z1-32b", MODEL_THUDM_GLM_4_32B_FREE = "thudm/glm-4-32b:free", MODEL_THUDM_GLM_4_32B = "thudm/glm-4-32b", MODEL_OPENAI_O4_MINI_HIGH = "openai/o4-mini-high", MODEL_OPENAI_O3 = "openai/o3", MODEL_OPENAI_O4_MINI = "openai/o4-mini", MODEL_SHISA_AI_SHISA_V2_LLAMA3_3_70B_FREE = "shisa-ai/shisa-v2-llama3.3-70b:free", + MODEL_SHISA_AI_SHISA_V2_LLAMA3_3_70B = "shisa-ai/shisa-v2-llama3.3-70b", MODEL_OPENAI_GPT_4_1 = "openai/gpt-4.1", MODEL_OPENAI_GPT_4_1_MINI = "openai/gpt-4.1-mini", MODEL_OPENAI_GPT_4_1_NANO = "openai/gpt-4.1-nano", MODEL_ELEUTHERAI_LLEMMA_7B = "eleutherai/llemma_7b", MODEL_ALFREDPROS_CODELLAMA_7B_INSTRUCT_SOLIDITY = "alfredpros/codellama-7b-instruct-solidity", MODEL_ARLIAI_QWQ_32B_ARLIAI_RPR_V1_FREE = "arliai/qwq-32b-arliai-rpr-v1:free", + MODEL_ARLIAI_QWQ_32B_ARLIAI_RPR_V1 = "arliai/qwq-32b-arliai-rpr-v1", MODEL_AGENTICA_ORG_DEEPCODER_14B_PREVIEW_FREE = "agentica-org/deepcoder-14b-preview:free", MODEL_AGENTICA_ORG_DEEPCODER_14B_PREVIEW = "agentica-org/deepcoder-14b-preview", MODEL_MOONSHOTAI_KIMI_VL_A3B_THINKING_FREE = "moonshotai/kimi-vl-a3b-thinking:free", + MODEL_MOONSHOTAI_KIMI_VL_A3B_THINKING = "moonshotai/kimi-vl-a3b-thinking", MODEL_X_AI_GROK_3_MINI_BETA = "x-ai/grok-3-mini-beta", MODEL_X_AI_GROK_3_BETA = "x-ai/grok-3-beta", MODEL_NVIDIA_LLAMA_3_3_NEMOTRON_SUPER_49B_V1 = "nvidia/llama-3.3-nemotron-super-49b-v1", @@ -96,7 +107,7 @@ export enum E_OPENROUTER_MODEL { MODEL_NVIDIA_LLAMA_3_1_NEMOTRON_ULTRA_253B_V1 = "nvidia/llama-3.1-nemotron-ultra-253b-v1", MODEL_META_LLAMA_LLAMA_4_MAVERICK = "meta-llama/llama-4-maverick", MODEL_META_LLAMA_LLAMA_4_SCOUT = "meta-llama/llama-4-scout", - MODEL_DEEPSEEK_DEEPSEEK_V3_BASE_FREE = "deepseek/deepseek-v3-base:free", + MODEL_DEEPSEEK_DEEPSEEK_V3_BASE = "deepseek/deepseek-v3-base", MODEL_SCB10X_LLAMA3_1_TYPHOON2_70B_INSTRUCT = "scb10x/llama3.1-typhoon2-70b-instruct", MODEL_GOOGLE_GEMINI_2_5_PRO_EXP_03_25 = "google/gemini-2.5-pro-exp-03-25", MODEL_QWEN_QWEN2_5_VL_32B_INSTRUCT_FREE = "qwen/qwen2.5-vl-32b-instruct:free", @@ -170,11 +181,9 @@ export enum E_OPENROUTER_MODEL { MODEL_MINIMAX_MINIMAX_01 = "minimax/minimax-01", MODEL_MISTRALAI_CODESTRAL_2501 = "mistralai/codestral-2501", MODEL_MICROSOFT_PHI_4 = "microsoft/phi-4", - MODEL_DEEPSEEK_DEEPSEEK_CHAT_FREE = "deepseek/deepseek-chat:free", MODEL_DEEPSEEK_DEEPSEEK_CHAT = "deepseek/deepseek-chat", MODEL_SAO10K_L3_3_EURYALE_70B = "sao10k/l3.3-euryale-70b", MODEL_OPENAI_O1 = "openai/o1", - MODEL_EVA_UNIT_01_EVA_LLAMA_3_33_70B = "eva-unit-01/eva-llama-3.33-70b", MODEL_X_AI_GROK_2_VISION_1212 = "x-ai/grok-2-vision-1212", MODEL_X_AI_GROK_2_1212 = "x-ai/grok-2-1212", MODEL_COHERE_COMMAND_R7B_12_2024 = "cohere/command-r7b-12-2024", @@ -203,16 +212,16 @@ export enum E_OPENROUTER_MODEL { MODEL_ANTHRACITE_ORG_MAGNUM_V4_72B = "anthracite-org/magnum-v4-72b", MODEL_ANTHROPIC_CLAUDE_3_5_SONNET_BETA = "anthropic/claude-3.5-sonnet:beta", MODEL_ANTHROPIC_CLAUDE_3_5_SONNET = "anthropic/claude-3.5-sonnet", - MODEL_MISTRALAI_MINISTRAL_3B = "mistralai/ministral-3b", MODEL_MISTRALAI_MINISTRAL_8B = "mistralai/ministral-8b", + MODEL_MISTRALAI_MINISTRAL_3B = "mistralai/ministral-3b", MODEL_QWEN_QWEN_2_5_7B_INSTRUCT = "qwen/qwen-2.5-7b-instruct", MODEL_NVIDIA_LLAMA_3_1_NEMOTRON_70B_INSTRUCT = "nvidia/llama-3.1-nemotron-70b-instruct", - MODEL_INFLECTION_INFLECTION_3_PRODUCTIVITY = "inflection/inflection-3-productivity", MODEL_INFLECTION_INFLECTION_3_PI = "inflection/inflection-3-pi", + MODEL_INFLECTION_INFLECTION_3_PRODUCTIVITY = "inflection/inflection-3-productivity", MODEL_GOOGLE_GEMINI_FLASH_1_5_8B = "google/gemini-flash-1.5-8b", + MODEL_ANTHRACITE_ORG_MAGNUM_V2_72B = "anthracite-org/magnum-v2-72b", MODEL_THEDRUMMER_ROCINANTE_12B = "thedrummer/rocinante-12b", MODEL_LIQUID_LFM_40B = "liquid/lfm-40b", - MODEL_ANTHRACITE_ORG_MAGNUM_V2_72B = "anthracite-org/magnum-v2-72b", MODEL_META_LLAMA_LLAMA_3_2_3B_INSTRUCT_FREE = "meta-llama/llama-3.2-3b-instruct:free", MODEL_META_LLAMA_LLAMA_3_2_3B_INSTRUCT = "meta-llama/llama-3.2-3b-instruct", MODEL_META_LLAMA_LLAMA_3_2_1B_INSTRUCT = "meta-llama/llama-3.2-1b-instruct", @@ -222,34 +231,32 @@ export enum E_OPENROUTER_MODEL { MODEL_QWEN_QWEN_2_5_72B_INSTRUCT_FREE = "qwen/qwen-2.5-72b-instruct:free", MODEL_QWEN_QWEN_2_5_72B_INSTRUCT = "qwen/qwen-2.5-72b-instruct", MODEL_NEVERSLEEP_LLAMA_3_1_LUMIMAID_8B = "neversleep/llama-3.1-lumimaid-8b", - MODEL_OPENAI_O1_PREVIEW = "openai/o1-preview", - MODEL_OPENAI_O1_MINI_2024_09_12 = "openai/o1-mini-2024-09-12", MODEL_OPENAI_O1_PREVIEW_2024_09_12 = "openai/o1-preview-2024-09-12", + MODEL_OPENAI_O1_MINI_2024_09_12 = "openai/o1-mini-2024-09-12", + MODEL_OPENAI_O1_PREVIEW = "openai/o1-preview", MODEL_OPENAI_O1_MINI = "openai/o1-mini", MODEL_MISTRALAI_PIXTRAL_12B = "mistralai/pixtral-12b", - MODEL_COHERE_COMMAND_R_PLUS_08_2024 = "cohere/command-r-plus-08-2024", MODEL_COHERE_COMMAND_R_08_2024 = "cohere/command-r-08-2024", + MODEL_COHERE_COMMAND_R_PLUS_08_2024 = "cohere/command-r-plus-08-2024", MODEL_QWEN_QWEN_2_5_VL_7B_INSTRUCT = "qwen/qwen-2.5-vl-7b-instruct", MODEL_SAO10K_L3_1_EURYALE_70B = "sao10k/l3.1-euryale-70b", MODEL_MICROSOFT_PHI_3_5_MINI_128K_INSTRUCT = "microsoft/phi-3.5-mini-128k-instruct", MODEL_NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B = "nousresearch/hermes-3-llama-3.1-70b", MODEL_NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B = "nousresearch/hermes-3-llama-3.1-405b", MODEL_OPENAI_CHATGPT_4O_LATEST = "openai/chatgpt-4o-latest", - MODEL_AETHERWIING_MN_STARCANNON_12B = "aetherwiing/mn-starcannon-12b", MODEL_SAO10K_L3_LUNARIS_8B = "sao10k/l3-lunaris-8b", MODEL_OPENAI_GPT_4O_2024_08_06 = "openai/gpt-4o-2024-08-06", MODEL_META_LLAMA_LLAMA_3_1_405B = "meta-llama/llama-3.1-405b", MODEL_NOTHINGIISREAL_MN_CELESTE_12B = "nothingiisreal/mn-celeste-12b", - MODEL_META_LLAMA_LLAMA_3_1_8B_INSTRUCT = "meta-llama/llama-3.1-8b-instruct", MODEL_META_LLAMA_LLAMA_3_1_70B_INSTRUCT = "meta-llama/llama-3.1-70b-instruct", + MODEL_META_LLAMA_LLAMA_3_1_8B_INSTRUCT = "meta-llama/llama-3.1-8b-instruct", MODEL_META_LLAMA_LLAMA_3_1_405B_INSTRUCT_FREE = "meta-llama/llama-3.1-405b-instruct:free", MODEL_META_LLAMA_LLAMA_3_1_405B_INSTRUCT = "meta-llama/llama-3.1-405b-instruct", MODEL_MISTRALAI_MISTRAL_NEMO_FREE = "mistralai/mistral-nemo:free", MODEL_MISTRALAI_MISTRAL_NEMO = "mistralai/mistral-nemo", - MODEL_OPENAI_GPT_4O_MINI_2024_07_18 = "openai/gpt-4o-mini-2024-07-18", MODEL_OPENAI_GPT_4O_MINI = "openai/gpt-4o-mini", + MODEL_OPENAI_GPT_4O_MINI_2024_07_18 = "openai/gpt-4o-mini-2024-07-18", MODEL_GOOGLE_GEMMA_2_27B_IT = "google/gemma-2-27b-it", - MODEL_ALPINDALE_MAGNUM_72B = "alpindale/magnum-72b", MODEL_GOOGLE_GEMMA_2_9B_IT_FREE = "google/gemma-2-9b-it:free", MODEL_GOOGLE_GEMMA_2_9B_IT = "google/gemma-2-9b-it", MODEL_01_AI_YI_LARGE = "01-ai/yi-large", @@ -259,63 +266,56 @@ export enum E_OPENROUTER_MODEL { MODEL_COGNITIVECOMPUTATIONS_DOLPHIN_MIXTRAL_8X22B = "cognitivecomputations/dolphin-mixtral-8x22b", MODEL_QWEN_QWEN_2_72B_INSTRUCT = "qwen/qwen-2-72b-instruct", MODEL_MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3 = "mistralai/mistral-7b-instruct-v0.3", + MODEL_NOUSRESEARCH_HERMES_2_PRO_LLAMA_3_8B = "nousresearch/hermes-2-pro-llama-3-8b", MODEL_MISTRALAI_MISTRAL_7B_INSTRUCT_FREE = "mistralai/mistral-7b-instruct:free", MODEL_MISTRALAI_MISTRAL_7B_INSTRUCT = "mistralai/mistral-7b-instruct", - MODEL_NOUSRESEARCH_HERMES_2_PRO_LLAMA_3_8B = "nousresearch/hermes-2-pro-llama-3-8b", MODEL_MICROSOFT_PHI_3_MINI_128K_INSTRUCT = "microsoft/phi-3-mini-128k-instruct", MODEL_MICROSOFT_PHI_3_MEDIUM_128K_INSTRUCT = "microsoft/phi-3-medium-128k-instruct", MODEL_NEVERSLEEP_LLAMA_3_LUMIMAID_70B = "neversleep/llama-3-lumimaid-70b", MODEL_GOOGLE_GEMINI_FLASH_1_5 = "google/gemini-flash-1.5", - MODEL_OPENAI_GPT_4O_2024_05_13 = "openai/gpt-4o-2024-05-13", MODEL_META_LLAMA_LLAMA_GUARD_2_8B = "meta-llama/llama-guard-2-8b", MODEL_OPENAI_GPT_4O = "openai/gpt-4o", MODEL_OPENAI_GPT_4O_EXTENDED = "openai/gpt-4o:extended", + MODEL_OPENAI_GPT_4O_2024_05_13 = "openai/gpt-4o-2024-05-13", MODEL_SAO10K_FIMBULVETR_11B_V2 = "sao10k/fimbulvetr-11b-v2", - MODEL_META_LLAMA_LLAMA_3_8B_INSTRUCT = "meta-llama/llama-3-8b-instruct", MODEL_META_LLAMA_LLAMA_3_70B_INSTRUCT = "meta-llama/llama-3-70b-instruct", + MODEL_META_LLAMA_LLAMA_3_8B_INSTRUCT = "meta-llama/llama-3-8b-instruct", MODEL_MISTRALAI_MIXTRAL_8X22B_INSTRUCT = "mistralai/mixtral-8x22b-instruct", MODEL_MICROSOFT_WIZARDLM_2_8X22B = "microsoft/wizardlm-2-8x22b", - MODEL_GOOGLE_GEMINI_PRO_1_5 = "google/gemini-pro-1.5", MODEL_OPENAI_GPT_4_TURBO = "openai/gpt-4-turbo", + MODEL_GOOGLE_GEMINI_PRO_1_5 = "google/gemini-pro-1.5", MODEL_COHERE_COMMAND_R_PLUS = "cohere/command-r-plus", MODEL_COHERE_COMMAND_R_PLUS_04_2024 = "cohere/command-r-plus-04-2024", MODEL_SOPHOSYMPATHEIA_MIDNIGHT_ROSE_70B = "sophosympatheia/midnight-rose-70b", - MODEL_COHERE_COMMAND_R = "cohere/command-r", MODEL_COHERE_COMMAND = "cohere/command", + MODEL_COHERE_COMMAND_R = "cohere/command-r", MODEL_ANTHROPIC_CLAUDE_3_HAIKU_BETA = "anthropic/claude-3-haiku:beta", MODEL_ANTHROPIC_CLAUDE_3_HAIKU = "anthropic/claude-3-haiku", + MODEL_ANTHROPIC_CLAUDE_3_SONNET = "anthropic/claude-3-sonnet", MODEL_ANTHROPIC_CLAUDE_3_OPUS_BETA = "anthropic/claude-3-opus:beta", MODEL_ANTHROPIC_CLAUDE_3_OPUS = "anthropic/claude-3-opus", - MODEL_ANTHROPIC_CLAUDE_3_SONNET_BETA = "anthropic/claude-3-sonnet:beta", - MODEL_ANTHROPIC_CLAUDE_3_SONNET = "anthropic/claude-3-sonnet", MODEL_COHERE_COMMAND_R_03_2024 = "cohere/command-r-03-2024", MODEL_MISTRALAI_MISTRAL_LARGE = "mistralai/mistral-large", MODEL_OPENAI_GPT_4_TURBO_PREVIEW = "openai/gpt-4-turbo-preview", MODEL_OPENAI_GPT_3_5_TURBO_0613 = "openai/gpt-3.5-turbo-0613", MODEL_NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO = "nousresearch/nous-hermes-2-mixtral-8x7b-dpo", - MODEL_MISTRALAI_MISTRAL_TINY = "mistralai/mistral-tiny", MODEL_MISTRALAI_MISTRAL_SMALL = "mistralai/mistral-small", + MODEL_MISTRALAI_MISTRAL_TINY = "mistralai/mistral-tiny", MODEL_MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2 = "mistralai/mistral-7b-instruct-v0.2", MODEL_MISTRALAI_MIXTRAL_8X7B_INSTRUCT = "mistralai/mixtral-8x7b-instruct", MODEL_NEVERSLEEP_NOROMAID_20B = "neversleep/noromaid-20b", - MODEL_ANTHROPIC_CLAUDE_2_1_BETA = "anthropic/claude-2.1:beta", - MODEL_ANTHROPIC_CLAUDE_2_1 = "anthropic/claude-2.1", - MODEL_ANTHROPIC_CLAUDE_2_BETA = "anthropic/claude-2:beta", - MODEL_ANTHROPIC_CLAUDE_2 = "anthropic/claude-2", - MODEL_ALPINDALE_GOLIATH_120B = "alpindale/goliath-120b", MODEL_UNDI95_TOPPY_M_7B = "undi95/toppy-m-7b", + MODEL_ALPINDALE_GOLIATH_120B = "alpindale/goliath-120b", MODEL_OPENROUTER_AUTO = "openrouter/auto", MODEL_OPENAI_GPT_4_1106_PREVIEW = "openai/gpt-4-1106-preview", - MODEL_MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1 = "mistralai/mistral-7b-instruct-v0.1", MODEL_OPENAI_GPT_3_5_TURBO_INSTRUCT = "openai/gpt-3.5-turbo-instruct", + MODEL_MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1 = "mistralai/mistral-7b-instruct-v0.1", MODEL_PYGMALIONAI_MYTHALION_13B = "pygmalionai/mythalion-13b", MODEL_OPENAI_GPT_3_5_TURBO_16K = "openai/gpt-3.5-turbo-16k", MODEL_MANCER_WEAVER = "mancer/weaver", - MODEL_ANTHROPIC_CLAUDE_2_0_BETA = "anthropic/claude-2.0:beta", - MODEL_ANTHROPIC_CLAUDE_2_0 = "anthropic/claude-2.0", MODEL_UNDI95_REMM_SLERP_L2_13B = "undi95/remm-slerp-l2-13b", MODEL_GRYPHE_MYTHOMAX_L2_13B = "gryphe/mythomax-l2-13b", - MODEL_OPENAI_GPT_4 = "openai/gpt-4", + MODEL_OPENAI_GPT_4_0314 = "openai/gpt-4-0314", MODEL_OPENAI_GPT_3_5_TURBO = "openai/gpt-3.5-turbo", - MODEL_OPENAI_GPT_4_0314 = "openai/gpt-4-0314" + MODEL_OPENAI_GPT_4 = "openai/gpt-4" } \ No newline at end of file diff --git a/packages/kbot/src/zod_types.ts b/packages/kbot/src/zod_types.ts index b8e805cf..99a7db72 100644 --- a/packages/kbot/src/zod_types.ts +++ b/packages/kbot/src/zod_types.ts @@ -33,6 +33,7 @@ export interface IKBotOptions {  01-ai/yi-large | paid aetherwiing/mn-starcannon-12b | paid + agentica-org/deepcoder-14b-preview | paid agentica-org/deepcoder-14b-preview:free | free ai21/jamba-1.6-large | paid ai21/jamba-1.6-mini | paid @@ -87,7 +88,6 @@ export interface IKBotOptions { cohere/command-r-plus-04-2024 | paid cohere/command-r-plus-08-2024 | paid cohere/command-r7b-12-2024 | paid - openrouter/cypher-alpha:free | free deepseek/deepseek-prover-v2 | paid deepseek/deepseek-r1-0528-qwen3-8b | paid deepseek/deepseek-r1-0528-qwen3-8b:free | free @@ -110,10 +110,10 @@ export interface IKBotOptions { deepseek/deepseek-r1-distill-qwen-7b | paid cognitivecomputations/dolphin-mixtral-8x22b | paid cognitivecomputations/dolphin3.0-mistral-24b:free | free + cognitivecomputations/dolphin3.0-r1-mistral-24b | paid cognitivecomputations/dolphin3.0-r1-mistral-24b:free | free eleutherai/llemma_7b | paid eva-unit-01/eva-llama-3.33-70b | paid - eva-unit-01/eva-qwen-2.5-32b | paid eva-unit-01/eva-qwen-2.5-72b | paid sao10k/fimbulvetr-11b-v2 | paid alpindale/goliath-120b | paid @@ -125,10 +125,6 @@ export interface IKBotOptions { google/gemini-2.0-flash-lite-001 | paid google/gemini-2.5-flash | paid google/gemini-2.5-flash-lite-preview-06-17 | paid - google/gemini-2.5-flash-preview | paid - google/gemini-2.5-flash-preview:thinking | paid - google/gemini-2.5-flash-preview-05-20 | paid - google/gemini-2.5-flash-preview-05-20:thinking | paid google/gemini-2.5-pro | paid google/gemini-2.5-pro-exp-03-25 | paid google/gemini-2.5-pro-preview-05-06 | paid @@ -142,6 +138,7 @@ export interface IKBotOptions { google/gemma-3-27b-it:free | free google/gemma-3-4b-it | paid google/gemma-3-4b-it:free | free + google/gemma-3n-e2b-it:free | free google/gemma-3n-e4b-it | paid google/gemma-3n-e4b-it:free | free inception/mercury | paid @@ -162,19 +159,19 @@ export interface IKBotOptions { meta-llama/llama-3-8b-instruct | paid meta-llama/llama-3.1-405b | paid meta-llama/llama-3.1-405b-instruct | paid + meta-llama/llama-3.1-405b-instruct:free | free meta-llama/llama-3.1-70b-instruct | paid meta-llama/llama-3.1-8b-instruct | paid meta-llama/llama-3.2-11b-vision-instruct | paid meta-llama/llama-3.2-11b-vision-instruct:free | free meta-llama/llama-3.2-1b-instruct | paid meta-llama/llama-3.2-3b-instruct | paid + meta-llama/llama-3.2-3b-instruct:free | free meta-llama/llama-3.2-90b-vision-instruct | paid meta-llama/llama-3.3-70b-instruct | paid meta-llama/llama-3.3-70b-instruct:free | free meta-llama/llama-4-maverick | paid - meta-llama/llama-4-maverick:free | free meta-llama/llama-4-scout | paid - meta-llama/llama-4-scout:free | free meta-llama/llama-guard-4-12b | paid meta-llama/llama-guard-2-8b | paid microsoft/mai-ds-r1:free | free @@ -194,8 +191,10 @@ export interface IKBotOptions { mistralai/mistral-small | paid mistralai/mistral-tiny | paid mistralai/codestral-2501 | paid + mistralai/devstral-medium | paid mistralai/devstral-small | paid - mistralai/devstral-small:free | free + mistralai/devstral-small-2505 | paid + mistralai/devstral-small-2505:free | free mistralai/magistral-medium-2506 | paid mistralai/magistral-medium-2506:thinking | paid mistralai/magistral-small-2506 | paid @@ -221,11 +220,13 @@ export interface IKBotOptions { mistralai/pixtral-large-2411 | paid mistralai/mistral-saba | paid moonshotai/kimi-vl-a3b-thinking:free | free + moonshotai/kimi-k2 | paid + moonshotai/kimi-k2:free | free morph/morph-v2 | paid + morph/morph-v3-fast | paid + morph/morph-v3-large | paid gryphe/mythomax-l2-13b | paid neversleep/llama-3-lumimaid-70b | paid - neversleep/llama-3-lumimaid-8b | paid - neversleep/llama-3.1-lumimaid-70b | paid neversleep/llama-3.1-lumimaid-8b | paid neversleep/noromaid-20b | paid nousresearch/deephermes-3-llama-3-8b-preview:free | free @@ -237,9 +238,9 @@ export interface IKBotOptions { nvidia/llama-3.1-nemotron-ultra-253b-v1 | paid nvidia/llama-3.1-nemotron-ultra-253b-v1:free | free nvidia/llama-3.3-nemotron-super-49b-v1 | paid - nvidia/llama-3.3-nemotron-super-49b-v1:free | free openai/chatgpt-4o-latest | paid openai/codex-mini | paid + openai/gpt-3.5-turbo | paid openai/gpt-3.5-turbo-0613 | paid openai/gpt-3.5-turbo-16k | paid openai/gpt-3.5-turbo-instruct | paid @@ -251,7 +252,6 @@ export interface IKBotOptions { openai/gpt-4.1 | paid openai/gpt-4.1-mini | paid openai/gpt-4.1-nano | paid - openai/gpt-4.5-preview | paid openai/gpt-4o | paid openai/gpt-4o-2024-05-13 | paid openai/gpt-4o-2024-08-06 | paid @@ -274,9 +274,6 @@ export interface IKBotOptions { openai/o4-mini | paid openai/o4-mini-high | paid opengvlab/internvl3-14b | paid - all-hands/openhands-lm-32b-v0.1 | paid - perplexity/llama-3.1-sonar-large-128k-online | paid - perplexity/llama-3.1-sonar-small-128k-online | paid perplexity/r1-1776 | paid perplexity/sonar | paid perplexity/sonar-deep-research | paid @@ -303,6 +300,7 @@ export interface IKBotOptions { qwen/qwen3-30b-a3b:free | free qwen/qwen3-32b | paid qwen/qwen3-32b:free | free + qwen/qwen3-4b:free | free qwen/qwen3-8b | paid qwen/qwen3-8b:free | free qwen/qwq-32b | paid @@ -314,15 +312,20 @@ export interface IKBotOptions { qwen/qwen-2.5-coder-32b-instruct | paid qwen/qwen-2.5-coder-32b-instruct:free | free featherless/qwerky-72b:free | free + rekaai/reka-flash-3 | paid rekaai/reka-flash-3:free | free undi95/remm-slerp-l2-13b | paid sao10k/l3-lunaris-8b | paid sao10k/l3-euryale-70b | paid sao10k/l3.1-euryale-70b | paid sao10k/l3.3-euryale-70b | paid + sarvamai/sarvam-m | paid sarvamai/sarvam-m:free | free shisa-ai/shisa-v2-llama3.3-70b:free | free raifle/sorcererlm-8x22b | paid + switchpoint/router | paid + tencent/hunyuan-a13b-instruct | paid + tencent/hunyuan-a13b-instruct:free | free thedrummer/anubis-70b-v1.1 | paid thedrummer/anubis-pro-105b-v1 | paid thedrummer/rocinante-12b | paid @@ -331,10 +334,13 @@ export interface IKBotOptions { thedrummer/valkyrie-49b-v1 | paid thudm/glm-4-32b | paid thudm/glm-4-32b:free | free + thudm/glm-4.1v-9b-thinking | paid thudm/glm-z1-32b:free | free tngtech/deepseek-r1t-chimera:free | free + tngtech/deepseek-r1t2-chimera:free | free undi95/toppy-m-7b | paid scb10x/llama3.1-typhoon2-70b-instruct | paid + cognitivecomputations/dolphin-mistral-24b-venice-edition:free | free microsoft/wizardlm-2-8x22b | paid x-ai/grok-2-1212 | paid x-ai/grok-2-vision-1212 | paid @@ -342,6 +348,7 @@ export interface IKBotOptions { x-ai/grok-3-beta | paid x-ai/grok-3-mini | paid x-ai/grok-3-mini-beta | paid + x-ai/grok-4 | paid x-ai/grok-vision-beta | paid   OpenAI models: @@ -371,8 +378,6 @@ export interface IKBotOptions { gpt-4.1-mini-2025-04-14 gpt-4.1-nano gpt-4.1-nano-2025-04-14 - gpt-4.5-preview - gpt-4.5-preview-2025-02-27 gpt-4o gpt-4o-2024-05-13 gpt-4o-2024-08-06