Free LLM Models Directory

Discover free and open-source large language models for text generation, code completion, and embeddings.

The Ultimate Free LLM Models Directory

Welcome to the most comprehensive directory of free and open-source LLM models. Whether you're a developer, researcher, or AI enthusiast, our curated collection helps you discover powerful language models that you can run locally, fine-tune, or integrate into your applications.

Our directory features models from leading providers including Meta's Llama 2, Mistral AI, Microsoft's Phi, and hundreds more on Hugging Face. All models are verified to have permissive licenses allowing commercial use.

From text generation and code completion to embeddings and multimodal models - find everything you need to build the next generation of AI applications!

Showing 193-240 of 576 models

Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances. Mistral: Mistral Medium 3.1 is listed as a large language or generative AI model associated with mistralai, with the model identifier mistralai/mistral-medium-3.1. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmistralaiLLM

A powerful multimodal Mixture-of-Experts chat model featuring 28B total parameters with 3B activated per token, delivering exceptional text and vision understanding through its innovative heterogeneous MoE structure with modality-isolated routing. Baidu: ERNIE 4.5 VL 28B A3B is listed as a large language or generative AI model associated with baidu, with the model identifier baidu/ernie-4.5-vl-28b-a3b. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsbaiduLLM
Z.ai: GLM 4.5V logo

Z.ai: GLM 4.5V

LLM Models

Free

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 is listed as a large language or generative AI model associated with z-ai, with the model identifier z-ai/glm-4.5v. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsz-aiLLM
Free

Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context. AI21: Jamba Large 1.7 is listed as a large language or generative AI model associated with ai21, with the model identifier ai21/jamba-large-1.7. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsai21LLM
Free

GPT-5 Chat is designed for advanced, natural, multimodal, and context-aware conversations for enterprise applications. OpenAI: GPT-5 Chat is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-5-chat. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM
OpenAI: GPT-5 logo

OpenAI: GPT-5

LLM Models

Free

GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy. OpenAI: GPT-5 is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-5. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM
Free

GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost. OpenAI: GPT-5 Mini is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-5-mini. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM
Free

GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger. OpenAI: GPT-5 Nano is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-5-nano. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM

gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized. OpenAI: gpt-oss-120b (free) is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-oss-120b:free. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM
Free

gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized. OpenAI: gpt-oss-120b is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-oss-120b. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for. OpenAI: gpt-oss-20b (free) is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-oss-20b:free. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM
Free

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for. OpenAI: gpt-oss-20b is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-oss-20b. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM

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 is listed as a large language or generative AI model associated with anthropic, with the model identifier anthropic/claude-opus-4.1. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsanthropicLLM

Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. [Blog Post](https://mistral.ai/news/codestral-25-08). Mistral: Codestral 2508 is listed as a large language or generative AI model associated with mistralai, with the model identifier mistralai/codestral-2508. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmistralaiLLM

Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the. Qwen: Qwen3 Coder 30B A3B Instruct is listed as a large language or generative AI model associated with qwen, with the model identifier qwen/qwen3-coder-30b-a3b-instruct. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsqwenLLM

Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and. Qwen: Qwen3 30B A3B Instruct 2507 is listed as a large language or generative AI model associated with qwen, with the model identifier qwen/qwen3-30b-a3b-instruct-2507. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsqwenLLM
Z.ai: GLM 4.5 logo

Z.ai: GLM 4.5

LLM Models

Free

GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly. Z.ai: GLM 4.5 is listed as a large language or generative AI model associated with z-ai, with the model identifier z-ai/glm-4.5. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsz-aiLLM

GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter. Z.ai: GLM 4.5 Air (free) is listed as a large language or generative AI model associated with z-ai, with the model identifier z-ai/glm-4.5-air:free. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsz-aiLLM
Free

GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter. Z.ai: GLM 4.5 Air is listed as a large language or generative AI model associated with z-ai, with the model identifier z-ai/glm-4.5-air. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsz-aiLLM

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. Qwen: Qwen3 235B A22B Thinking 2507 is listed as a large language or generative AI model associated with qwen, with the model identifier qwen/qwen3-235b-a22b-thinking-2507. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsqwenLLM
Z.ai: GLM 4 32B logo

Z.ai: GLM 4 32B

LLM Models

Free

GLM 4 32B is a cost-effective foundation language model. It can efficiently perform complex tasks and has significantly enhanced capabilities in tool use, online search, and code-related intelligent tasks. It. Z.ai: GLM 4 32B is listed as a large language or generative AI model associated with z-ai, with the model identifier z-ai/glm-4-32b. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsz-aiLLM

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 480B A35B (free) is listed as a large language or generative AI model associated with qwen, with the model identifier qwen/qwen3-coder:free. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsqwenLLM

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 480B A35B is listed as a large language or generative AI model associated with qwen, with the model identifier qwen/qwen3-coder. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsqwenLLM
Free

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. ByteDance: UI-TARS 7B is listed as a large language or generative AI model associated with bytedance, with the model identifier bytedance/ui-tars-1.5-7b. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsbytedanceLLM

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. Google: Gemini 2.5 Flash Lite is listed as a large language or generative AI model associated with google, with the model identifier google/gemini-2.5-flash-lite. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsgoogleLLM

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,. Qwen: Qwen3 235B A22B Instruct 2507 is listed as a large language or generative AI model associated with qwen, with the model identifier qwen/qwen3-235b-a22b-2507. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsqwenLLM
Free

Switchpoint AI's router instantly analyzes your request and directs it to the optimal AI from an ever-evolving library. As the world of LLMs advances, our router gets smarter, ensuring you. Switchpoint Router is listed as a large language or generative AI model associated with switchpoint, with the model identifier switchpoint/router. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsswitchpointLLM

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. MoonshotAI: Kimi K2 0711 is listed as a large language or generative AI model associated with moonshotai, with the model identifier moonshotai/kimi-k2. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmoonshotaiLLM

Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving. Venice: Uncensored (free) is listed as a large language or generative AI model associated with cognitivecomputations, with the model identifier cognitivecomputations/dolphin-mistral-24b-venice-edition:free. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelscognitivecomputationsLLM

Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark. Tencent: Hunyuan A13B Instruct is listed as a large language or generative AI model associated with tencent, with the model identifier tencent/hunyuan-a13b-instruct. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelstencentLLM
Free

Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initial_code}</code>. Morph: Morph V3 Large is listed as a large language or generative AI model associated with morph, with the model identifier morph/morph-v3-large. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmorphLLM
Free

Morph's fastest apply model for code edits. ~10,500 tokens/sec with 96% accuracy for rapid code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initial_code}</code> <update>{edit_snippet}</update>. Morph: Morph V3 Fast is listed as a large language or generative AI model associated with morph, with the model identifier morph/morph-v3-fast. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmorphLLM

ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data. Baidu: ERNIE 4.5 VL 424B A47B is listed as a large language or generative AI model associated with baidu, with the model identifier baidu/ernie-4.5-vl-424b-a47b. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsbaiduLLM

Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on. Mistral: Mistral Small 3.2 24B is listed as a large language or generative AI model associated with mistralai, with the model identifier mistralai/mistral-small-3.2-24b-instruct. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmistralaiLLM
Free

MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it. MiniMax: MiniMax M1 is listed as a large language or generative AI model associated with minimax, with the model identifier minimax/minimax-m1. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsminimaxLLM

Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater. Google: Gemini 2.5 Flash is listed as a large language or generative AI model associated with google, with the model identifier google/gemini-2.5-flash. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsgoogleLLM

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy. Google: Gemini 2.5 Pro is listed as a large language or generative AI model associated with google, with the model identifier google/gemini-2.5-pro. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsgoogleLLM
OpenAI: o3 Pro logo

OpenAI: o3 Pro

LLM Models

Free

The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently. OpenAI: o3 Pro is listed as a large language or generative AI model associated with openai, with the model identifier openai/o3-pro. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsopenaiLLM

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy. Google: Gemini 2.5 Pro Preview 06-05 is listed as a large language or generative AI model associated with google, with the model identifier google/gemini-2.5-pro-preview. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsgoogleLLM
Free

May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active. DeepSeek: R1 0528 is listed as a large language or generative AI model associated with deepseek, with the model identifier deepseek/deepseek-r1-0528. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsdeepseekLLM

Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in. Anthropic: Claude Opus 4 is listed as a large language or generative AI model associated with anthropic, with the model identifier anthropic/claude-opus-4. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsanthropicLLM

Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),. Anthropic: Claude Sonnet 4 is listed as a large language or generative AI model associated with anthropic, with the model identifier anthropic/claude-sonnet-4. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsanthropicLLM
Free

Gemma 3n E4B-it is optimized for efficient execution on mobile and low-resource devices, such as phones, laptops, and tablets. It supports multimodal inputs—including text, visual data, and audio—enabling diverse tasks. Google: Gemma 3n 4B is listed as a large language or generative AI model associated with google, with the model identifier google/gemma-3n-e4b-it. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsgoogleLLM

Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost. Mistral: Mistral Medium 3 is listed as a large language or generative AI model associated with mistralai, with the model identifier mistralai/mistral-medium-3. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsmistralaiLLM

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy. Google: Gemini 2.5 Pro Preview 05-06 is listed as a large language or generative AI model associated with google, with the model identifier google/gemini-2.5-pro-preview-05-06. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM ModelsgoogleLLM
Free

Spotlight is a 7‑billion‑parameter vision‑language model derived from Qwen 2.5‑VL and fine‑tuned by Arcee AI for tight image‑text grounding tasks. It offers a 32 k‑token context window, enabling rich multimodal. Arcee AI: Spotlight is listed as a large language or generative AI model associated with arcee-ai, with the model identifier arcee-ai/spotlight. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsarcee-aiLLM

Maestro Reasoning is Arcee's flagship analysis model: a 32 B‑parameter derivative of Qwen 2.5‑32 B tuned with DPO and chain‑of‑thought RL for step‑by‑step logic. Compared to the earlier 7 B. Arcee AI: Maestro Reasoning is listed as a large language or generative AI model associated with arcee-ai, with the model identifier arcee-ai/maestro-reasoning. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsarcee-aiLLM

Virtuoso‑Large is Arcee's top‑tier general‑purpose LLM at 72 B parameters, tuned to tackle cross‑domain reasoning, creative writing and enterprise QA. Unlike many 70 B peers, it retains the 128 k. Arcee AI: Virtuoso Large is listed as a large language or generative AI model associated with arcee-ai, with the model identifier arcee-ai/virtuoso-large. It can be considered by developers, researchers, and AI application builders when evaluating models for programmatic AI workflows, experimentation, prototyping, model comparison, or integration into applications. The supplied description above captures the model-specific capabilities available in this dataset; exact context limits, supported input and output modalities, tool-calling behavior, structured-output support, pricing, rate limits, licensing, deployment options, and availability should be confirmed from the linked provider or model documentation because these technical details can vary between model versions and may change over time. For production use, developers should also evaluate output quality, latency, reliability, safety behavior, privacy requirements, and workload-specific performance against their own application requirements rather than assuming that one model configuration is suitable for every use case.

LLM Modelsarcee-aiLLM

Frequently Asked Questions About LLM Models

What are LLM models and how do they work?

Large Language Models (LLMs) are AI models trained on vast amounts of text data to understand and generate human-like text. They use transformer architecture and can perform tasks like text generation, translation, summarization, and code completion.

Which LLM models are free to use?

Many LLM models are freely available including Llama 2, Mistral, Mixtral, Phi-2, and various models on Hugging Face. Our directory features only models with permissive licenses that allow commercial use.

How do I run LLM models locally?

You can run LLMs locally using tools like Ollama, LM Studio, or text-generation-webui. Most models require significant GPU memory (8GB+ VRAM recommended). Smaller models like Phi-2 and Llama 2 7B can run on consumer hardware.

What's the difference between open-source and proprietary LLMs?

Open-source LLMs like Llama 2 and Mistral have publicly available weights and can be run locally or fine-tuned. Proprietary models like GPT-4 are only accessible via API. Open-source models offer more control and privacy.

Can I use these LLM models for commercial projects?

Most models in our directory have permissive licenses (Apache 2.0, MIT) allowing commercial use. Always check the specific model's license on Hugging Face or the provider's website before using in production.