Models

Google's lightweight open-weight model with 1 billion parameters from the Gemma 3 family. Designed for on-device and resource-constrained deployments. Supports text-only tasks with a 32K context window. Efficient for chat and basic completion workloads.

Google's largest open-weight model in the Gemma 3 family with 27 billion parameters. Supports multimodal inputs including text and images with a 128K context window. Delivers strong performance across reasoning, code generation, and vision tasks, competitive with larger proprietary models.

Alibaba's QwQ 32B reasoning-focused model designed for complex problem solving, mathematical reasoning, and step-by-step logical analysis with strong chain-of-thought capabilities.

Mistral AI's Small 3.1 model with 24B parameters offering efficient multimodal capabilities including vision, function calling, and code generation with a large 128K context window.

Cohere's flagship 111B parameter model optimized for demanding enterprises requiring fast, secure, and high-quality AI. Excels at RAG, tool use, and multilingual tasks with strong reasoning capabilities.

Microsoft's Phi-4 Mini model with 3.8B parameters providing lightweight yet capable language understanding and code generation, optimized for resource-constrained deployments with a large 128K context window.

DeepSeek's reasoning-focused model trained with reinforcement learning for complex multi-step reasoning. Excels at math, science, and coding problems requiring chain-of-thought reasoning.

Mistral AI's cutting-edge code generation model specializing in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code completion, correction, and test generation. Features efficient architecture with 2x faster generation than its predecessor.

Large language model developed by ISSAI (Nazarbayev University) customized from Llama 3.1 70B to improve helpfulness of responses in the Kazakh language. Part of Kazakhstan's initiative to ensure the country benefits from generative AI advancements.

Meta's flagship open-weight model with 70 billion parameters. Strong multilingual capabilities with competitive performance on reasoning and coding benchmarks. Available for self-hosting and through various inference providers.

Microsoft's Phi-4 model with 14B parameters excelling at reasoning and code generation tasks, delivering strong performance relative to its compact size with efficient inference characteristics.

DeepSeek's third-generation large language model featuring mixture-of-experts architecture, strong multilingual capabilities, and competitive performance on reasoning and coding benchmarks.

Cohere's compact 7B parameter model optimized for RAG, tool use, and code tasks. Delivers top-tier speed and efficiency on commodity GPUs and edge devices with 128K context window.

MTS AI's lightweight 1.5B parameter language model optimized for resource-constrained environments. Excels at Russian and English language tasks including content creation, translation, and text analysis. Runs efficiently on both CPU and GPU, including laptops and smartphones.

Highly performant 32B multilingual language model from Cohere For AI, designed to rival monolingual model performance across 23 languages. Built using innovations in multilingual data arbitrage, direct preference optimization, and model merging techniques. Outperforms previous multilingual models on both automatic and human evaluations.

Meta's multimodal open-weight model with 11 billion parameters from the Llama 3.2 family. Supports both text and image inputs, enabling visual understanding tasks alongside standard text generation. Suitable for applications requiring vision capabilities at moderate scale.

Meta's largest multimodal open-weight model with 90 billion parameters from the Llama 3.2 family. Delivers strong performance on both text and image understanding tasks with competitive results on visual reasoning benchmarks. Designed for high-quality inference requiring vision capabilities.

Meta's lightweight open-weight model with 3 billion parameters from the Llama 3.2 family. Designed for on-device and edge deployment with strong text generation capabilities relative to its size. Supports instruction following and general-purpose tasks.

Meta's efficient open-weight model with 8 billion parameters from the Llama 3.1 family. Optimized for instruction following with strong performance on general tasks, coding, and multilingual benchmarks. Ideal for cost-effective deployment and edge inference scenarios.

Anthropic's most powerful model in the Claude 3 family, excelling at complex analysis, nuanced content generation, scientific reasoning, and code generation with extended context support.

OpenAI's flagship large language model with advanced reasoning, instruction following, and code generation capabilities. Supports multimodal inputs including text and images.

OpenAI's Whisper automatic speech recognition model capable of multilingual audio transcription and translation, trained on a large dataset of diverse audio for robust real-world performance.