Модели

Mistral AI's largest open-weight model with 41B active parameters (675B total MoE). State-of-the-art general-purpose multimodal model with 256K context window and powerful agentic capabilities. Released under Apache 2.0.

Mistral AI's frontier code agents model designed for solving software engineering tasks. Open-weight model optimized for agentic coding workflows and complex development tasks.

xAI's fast and cost-effective model with 2M token context window. Offers both reasoning and non-reasoning modes at significantly lower pricing than flagship models.

Anthropic's previous-generation balanced model with strong coding and analysis capabilities. Offers excellent price-performance ratio for production workloads requiring reliable quality.

Zhipu AI's multilingual agentic coding model with strong reasoning, tool use, and UI generation capabilities. Predecessor to GLM-5.1 with competitive performance on coding benchmarks.

Anthropic's fastest model with near-frontier intelligence. Optimized for high-throughput, low-latency applications requiring quick responses at minimal cost. Supports extended thinking.

Kazakhstan's flagship Mixture-of-Experts language model developed by Astana Hub with technical support from 01.AI. Features 247B total parameters with 22B active per token, achieving state-of-the-art results on Kazakh, Russian, and English benchmarks. Outperforms GPT-4o on Kazakh language tasks.

Google's high-capability reasoning model with adaptive thinking for complex agentic and multimodal challenges. Features 1M token context window and strong performance on coding and scientific tasks.

Google's cost-effective model optimized for high throughput tasks. Balances speed and intelligence with strong multimodal capabilities and 1M token context window.

NVIDIA's compact 9B parameter model trained from scratch for both reasoning and non-reasoning tasks. Generates reasoning traces before final responses. Efficient for edge and on-device deployment.

OpenAI's fifth-generation flagship model with significant improvements in reasoning, multimodal understanding, and code generation. Features enhanced instruction following and expanded context window.

Meta's efficient MoE model with 17B active parameters (109B total, 16 experts). Supports up to 10M token context — the longest of any production model. Strong performance on reasoning and multilingual tasks.

Meta's quality-focused MoE model with 17B active parameters (400B total, 128 experts). Targets quality-critical tasks with benchmark scores competitive with GPT-4o and Gemini 2.5 Pro.

Alibaba's Qwen3 32B dense language model with strong reasoning and multilingual capabilities, supporting function calling and code generation across diverse tasks.

Alibaba's Qwen3 235B mixture-of-experts model delivering frontier-level performance with advanced reasoning, function calling, and code generation capabilities at massive scale.

Alibaba's Qwen3 Coder model optimized for software development tasks including code generation, debugging, code review, and technical documentation with strong multilingual programming support.

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.

Google's compact open-weight model with 4 billion parameters from the Gemma 3 family. Supports multimodal inputs including text and images with a 128K context window. Balances efficiency and capability for vision and language tasks.

Google's mid-size open-weight model with 12 billion parameters from the Gemma 3 family. Supports multimodal inputs including text and images with a 128K context window. Strong performance on reasoning and code generation tasks at moderate compute cost.

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.

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.

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.

Модель DeepSeek, ориентированная на рассуждение и обученная с подкреплением для сложных многоэтапных задач. Особенно хорошо справляется с математикой, естественными науками и программированием, где требуется последовательное рассуждение.