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 337-384 of 576 models
OpenAI: GPT-4 Turbo (older v1106)
LLM Models
The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Training data: up to April 2023. OpenAI: GPT-4 Turbo (older v1106) is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-4-1106-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.
OpenAI: GPT-3.5 Turbo Instruct
LLM Models
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. OpenAI: GPT-3.5 Turbo Instruct is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-3.5-turbo-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.
OpenAI: GPT-3.5 Turbo 16k
LLM Models
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up. OpenAI: GPT-3.5 Turbo 16k is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-3.5-turbo-16k. 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.
Mancer: Weaver (alpha)
LLM Models
An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations. Mancer: Weaver (alpha) is listed as a large language or generative AI model associated with mancer, with the model identifier mancer/weaver. 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.
ReMM SLERP 13B
LLM Models
A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge. ReMM SLERP 13B is listed as a large language or generative AI model associated with undi95, with the model identifier undi95/remm-slerp-l2-13b. 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.
MythoMax 13B
LLM Models
One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge. MythoMax 13B is listed as a large language or generative AI model associated with gryphe, with the model identifier gryphe/mythomax-l2-13b. 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.
OpenAI: GPT-4
LLM Models
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. OpenAI: GPT-4 is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-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.
OpenAI: GPT-3.5 Turbo
LLM Models
GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks. Training data up to Sep 2021. OpenAI: GPT-3.5 Turbo is listed as a large language or generative AI model associated with openai, with the model identifier openai/gpt-3.5-turbo. 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.
llama3.1
LLM Models
llama3.1 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
deepseek-r1
LLM Models
deepseek-r1 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
nomic-embed-text
LLM Models
nomic-embed-text is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llama3.2
LLM Models
llama3.2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
gemma3
LLM Models
gemma3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen2.5
LLM Models
qwen2.5 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen3
LLM Models
qwen3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mistral
LLM Models
mistral is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
gemma2
LLM Models
gemma2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llama3
LLM Models
llama3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
phi3
LLM Models
phi3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen2.5-coder
LLM Models
qwen2.5-coder is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llava
LLM Models
llava is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen3.5
LLM Models
qwen3.5 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
gemma4
LLM Models
gemma4 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mxbai-embed-large
LLM Models
mxbai-embed-large is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
gpt-oss
LLM Models
gpt-oss is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
phi4
LLM Models
phi4 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
gemma
LLM Models
gemma is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llama2
LLM Models
llama2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen
LLM Models
qwen is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen3-coder
LLM Models
qwen3-coder is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen2
LLM Models
qwen2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
codellama
LLM Models
codellama is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
minicpm-v
LLM Models
minicpm-v is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
tinyllama
LLM Models
tinyllama is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mistral-nemo
LLM Models
mistral-nemo is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
bge-m3
LLM Models
bge-m3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llama3.2-vision
LLM Models
llama3.2-vision is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
deepseek-coder
LLM Models
deepseek-coder is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
qwen3-vl
LLM Models
qwen3-vl is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
llama3.3
LLM Models
llama3.3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
dolphin3
LLM Models
dolphin3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
deepseek-v3
LLM Models
deepseek-v3 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
olmo2
LLM Models
olmo2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
smollm2
LLM Models
smollm2 is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
all-minilm
LLM Models
all-minilm is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
snowflake-arctic-embed
LLM Models
snowflake-arctic-embed is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
mistral-small
LLM Models
mistral-small is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
codegemma
LLM Models
codegemma is a model entry associated with Ollama. This listing is intended to help developers, researchers, students, and AI application builders discover and organize available language or generative AI models for experimentation, prototyping, model comparison, and software integration. The supplied dataset does not include a model-specific capability description for this record, so capabilities such as reasoning, coding, instruction following, multilingual support, vision or audio processing, tool use, structured outputs, and long-context processing should not be assumed from the model name alone. Before using this model in an application, review the linked provider or model documentation for its architecture or model family, supported modalities, context window, input and output formats, API requirements, licensing terms, pricing or free-tier conditions, rate limits, safety constraints, and current availability. Production deployments should additionally test model quality, latency, reliability, privacy requirements, and workload-specific performance using representative data from the intended application.
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.