Модели

Claude Opus 4.6

Anthropic's most capable model in the Claude 4 family, excelling at complex analysis, extended reasoning, scientific research, and advanced code generation. Features significantly improved accuracy and reduced hallucinations.

Claude Sonnet 4.6

Anthropic's balanced model offering strong performance at lower cost and latency than Opus. Excellent for everyday coding, analysis, and content generation tasks with good reasoning capabilities.

Devstral 2

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.

GigaChat 3.1 Lightning

Sber's compact Mixture-of-Experts model with 10B total parameters and 1.8B active. Designed for fast multilingual assistant workloads, reasoning, code, function calling, and product-style deployment on edge devices.

Mistral Large 3

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.

GigaChat 3.1 Ultra

Sber's flagship large-scale Mixture-of-Experts model with 702B total parameters and 36B active. Designed for multilingual assistant workloads, reasoning, code generation, tool use, and large-cluster deployment. Open-weight release.

Grok 4.1 Fast

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.

Claude Sonnet 4.5

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

GLM-4.7

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.

Claude Haiku 4.5

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

AlemLLM

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.

Trendyol LLM 8B T1

Turkish-optimized 8B chat model developed by Trendyol, Turkey's largest e-commerce platform. Built on Qwen3-8B and fine-tuned on large-scale Turkish e-commerce datasets. Features advanced chain-of-thought reasoning in Turkish with dual operation modes (/think and /no_think), strong instruction following, summarization, coding, and attribute extraction for catalogue enrichment. English reasoning capabilities are preserved alongside Turkish.

Gemini 2.5 Pro

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.

Gemini 2.5 Flash

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

GPT-5

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

YandexGPT 5 Lite

Yandex's compact 8B parameter language model trained on 15T tokens of primarily Russian and English text. Features 32K context window with strong performance on web, code, and mathematics tasks. Open-weight release.

Nemotron Nano 9B v2

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.

Kumru 7B

Turkish large language model developed by VNGRS, pre-trained from scratch on 500 GB of Turkish corpora (300B tokens). Decoder-only architecture with a custom tokenizer optimized for Turkish, supporting code, math, and chat. Outperforms significantly larger multilingual models on the Cetvel Turkish benchmark. Available for on-premise enterprise deployment.

Llama 4 Maverick

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.

Llama 4 Scout

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.

Qwen3 32B

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

Qwen3 235B

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

Qwen3 Coder

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

Gemma 3 4B

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.