Model Radar
AI models directory for capability-aware discovery
Focused on open models for self-hosting, research, and product development.
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30
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Open-source models
Not sure which model to inspect? Filter by openness, category, and keyword, then open profiles for capabilities, licensing, use cases, and related tools.
Ollama
mixtral
Mixtral is a family of sparse Mixture-of-Experts (SMoE) large language models developed by Mistral AI, available in 8x7B (46.7B total, 12.9B active) and 8x22B (141B total, 39B active) sizes. It supports multilingual text (English, French, Italian, German, Spanish), a 64K token context window (8x22B), and is released under Apache 2.0. The model excels at reasoning, code generation, and instruction following, outperforming Llama 2 70B and GPT-3.5 on benchmarks. It can be run locally via Ollama or accessed through Mistral AI's API.
Alibaba Cloud
Qwen
Qwen is a family of large language models developed by Alibaba Cloud, covering chat, code, and multimodal variants. Designed for enterprise deployment, Qwen models are available in various sizes (1.8B to 72B parameters) and support both open-source and commercial use under the Qwen License. They excel in natural language understanding, code generation, mathematical reasoning, and multimodal tasks, with options for local deployment, cloud API, and fine-tuning.
Zhipu AI
GLM-4
GLM-4 is the latest generation of the General Language Model (GLM) series developed by Zhipu AI. It is a bilingual (Chinese and English) large language model with 130 billion parameters, supporting both API access and open-source releases. The model excels in long-context understanding, multimodal tasks, and agent-based applications, with a context window of up to 128K tokens. It is designed for a wide range of NLP tasks including text generation, reasoning, and code synthesis.
google/electra-base-discriminator · Hugging Face
ELECTRA-base-discriminator is a Transformer encoder pretrained with replaced token detection (RTD), achieving strong NLP performance efficiently. It is available under Apache 2.0 license.
OpenAI
Whisper Large v3
Whisper Large v3 is a state-of-the-art open-source speech-to-text model developed by OpenAI, designed for robust multilingual transcription and translation. It excels in production audio workflows, offering high accuracy across diverse languages and acoustic conditions. The model is self-hostable, customizable, and widely used in voice applications, meeting accessibility, and batch processing pipelines.
Gemma 2 27B
Gemma 2 27B is an open-weight language model from Google, designed for self-hosted and customizable AI stacks. It offers strong performance for general assistant and reasoning tasks, backed by Google's ecosystem and available under a permissive license.
Ollama
qwen2.5
Qwen2.5 is a family of large language models by Alibaba Cloud's Qwen team, ranging from 0.5B to 72B parameters, with improved reasoning, coding, multilingual support, and up to 128K token context. Released under Apache 2.0 (except 3B and 72B under Qwen license).
Ollama
deepseek-r1
DeepSeek-R1 is a family of open reasoning large language models developed by DeepSeek, available via Ollama for local deployment. It excels in complex reasoning, mathematics, coding, and logical problem-solving, with performance approaching leading models like O3 and Gemini 2.5 Pro. The model is released under the MIT License and can be run locally using `ollama run deepseek-r1`, supporting various sizes from 1.5B to 671B parameters.
Ollama
llama3.3
Llama 3.3 is a state-of-the-art 70B parameter large language model from Meta, optimized for local deployment via Ollama. It offers performance comparable to Llama 3.1 405B, supports up to 128K token context, and is designed for text generation, reasoning, instruction following, and multilingual dialogue.
Ollama
gemma2
Gemma 2 is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. It is available in 2B, 9B, and 27B parameter sizes, designed for efficient deployment on resource-constrained devices and cloud environments. The model excels in text generation, reasoning, and coding tasks, and is optimized for local inference via Ollama.