3,424 models

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snowflake/llama4-maverick 128K context $0.24/M input $0.97/M output

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wandb/qwen/qwen3.6-35b-a3b 262.144K context $0.25/M input $1.25/M output

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wandb/qwen/qwen3.8-27b 262.144K context $0.4/M input $3/M output

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databricks/databricks-glm-5-2 1M context $1.4/M input $4.4/M output

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wandb/openpipe/qwen3-14b-instruct 32.768K context $0.05/M input $0.22/M output

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snowflake/openai-gpt-5-nano 5M context $0.15/M input $0.6/M output

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libertai/gemma-4-31b-it-thinking 262.144K context $0.15/M input $0.4/M output

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wandb/moonshotai/kimi-k2.6 262.144K context $0.65/M input $3.41/M output

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wandb/moonshotai/kimi-k2.7-code 262.144K context $0.71/M input $3.5/M output

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snowflake/openai-gpt-5-mini 1M context $0.3/M input $1.2/M output

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wandb/minimaxai/minimax-m3 262.144K context $0.23/M input $0.96/M output

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...

qwen/qwen3-coder 262.144K context $0.3/M input $1/M output

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snowflake/openai-gpt-5 300K context $1.25/M input $10/M output

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wandb/ibm-granite/granite-4.1-8b 131.072K context $0.05/M input $0.1/M output

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wandb/google/gemma-4-31b-it 262.144K context $0.1/M input $0.34/M output

Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...

anthropic/claude-opus-4.1:batch 200K context $7.5/M input $37.5/M output

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wandb/deepseek-ai/deepseek-v4-pro 1.04858M context $1.15/M input $2.55/M output

GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...

z-ai/glm-4.5v 65.536K context $0.6/M input $1.8/M output

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snowflake/openai-gpt-4.1 300K context $2/M input $8/M output

Nex-N2.5 is an agentic model built to turn goals into working, verified outcomes. Its core strength is agentic coding within a visual feedback loop: it can explore codebases, implement multi-file...

nex-agi/nex-n2.5-pro:free 262.144K context Free input Free output
Open weights

Granite 4.2 8B is a dense reasoning model from IBM. It is suited for mathematics, code generation, multilingual dialogue, and agentic workflows that need multi-step reasoning. It supports full, low-effort,...

ibm-granite/granite-4.2-8b 131.072K context $0.06/M input $0.25/M output

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wandb/deepseek-ai/deepseek-v4-flash 1.04858M context $0.14/M input $0.28/M output

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snowflake/claude-3-7-sonnet 200K context $3/M input $15/M output

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libertai/gemma-4-31b-it 262.144K context $0.15/M input $0.4/M output

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llamagate/openthinker-7b 32.768K context $0.08/M input $0.15/M output

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novita/thudm/glm-4-32b-0414 32K context $0.55/M input $1.66/M output

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qwen_ai_platform/qwen-plus-2025-07-28 997.952K context Input not listed Output not listed

GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon...

openai/gpt-6-astra:batch 1.05M context $5/M input $25/M output

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snowflake/claude-haiku-4-5 200K context $1/M input $5/M output

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qwen_ai_platform/qwen-plus-2025-09-11 997.952K context Input not listed Output not listed

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qwen_ai_platform/qwen-plus-latest 997.952K context Input not listed Output not listed

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qwen_ai_platform/qwen-turbo 129.024K context $0.05/M input $0.2/M output

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qwen_ai_platform/qwen-plus 129.024K context $0.4/M input $1.2/M output

GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks. Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

openai/gpt-5.6-luna-pro:batch 1.05M context $0.1/M input $0.6/M output