Kimi K2 combines expert-driven architecture with robust reasoning and coding skills, offering reliable autonomy for complex, real-world challenges.
Moonshot AI’s Kimi K2 is a next-generation Chinese large language model powered by an open trillion-parameter architecture, optimized for versatility, real-world tool use, and robust multilingual interaction.
With a 131K-token context window and direct integration of tool learning into the training pipeline across numerous synthetic and real environments, Kimi K2 excels in tasks requiring dynamic, agentic behaviors and automated workflows.
Kimi K2 is a state-of-the-art, open-source, agentic LLM—built for scale, speed, and real-world tool use.
• Context Window: 131K tokens
• Performance Benchmarks: LiveCodeBench: 53.7%, MATH 500: 97%+ (near GPT-4.1 levels)
Kimi K2 demonstrates adaptability across diverse scenarios while maintaining stability in repeatable conditions. The upward performance trend suggests improved efficiency in handling complex tasks.
• Input: $0.195 per million tokens
• Output: $3.25 per million tokens
• Vs. Gemini 2.5 Flash: Kimi K2 surpasses Gemini 2.5 Flash in structured programming and tool-driven workflow benchmarks, achieving 65.8% on SWE-bench and 53.7% on LiveCodeBench compared to Gemini’s 63.8%, making it the better choice for coding accuracy and complex automation.
• Vs. GPT-4.1: While Kimi K2’s LiveCodeBench score (53.7%) is strong, GPT-4.1 remains the leading general-purpose model, but Kimi-K2 is a competitive open alternative for users prioritizing coding precision and open-source flexibility in production environments
• Vs. Claude 4 Sonnet: Kimi K2 outperforms Claude 4 Sonnet in zero-shot code generation and agentic, tool-use scenarios, offering higher coding fidelity and deeper integration for real-world automation needs.
• No Fine-Tuning Support: The current API offering does not support model fine-tuning for end users.
• Limited to Text-Based Tasks: The model is optimized for text generation, coding, and reasoning, but not for audio, vision, or multimodal workflows.
• Open-Source Considerations: While open-source brings flexibility, it also requires more technical expertise for deployment and customization compared to fully managed cloud APIs.
Accessible via AI/ML API. Documentation: available here.
Moonshot AI’s Kimi K2 is a next-generation Chinese large language model powered by an open trillion-parameter architecture, optimized for versatility, real-world tool use, and robust multilingual interaction.
With a 131K-token context window and direct integration of tool learning into the training pipeline across numerous synthetic and real environments, Kimi K2 excels in tasks requiring dynamic, agentic behaviors and automated workflows.
Kimi K2 is a state-of-the-art, open-source, agentic LLM—built for scale, speed, and real-world tool use.
• Context Window: 131K tokens
• Performance Benchmarks: LiveCodeBench: 53.7%, MATH 500: 97%+ (near GPT-4.1 levels)
Kimi K2 demonstrates adaptability across diverse scenarios while maintaining stability in repeatable conditions. The upward performance trend suggests improved efficiency in handling complex tasks.
• Input: $0.195 per million tokens
• Output: $3.25 per million tokens
• Vs. Gemini 2.5 Flash: Kimi K2 surpasses Gemini 2.5 Flash in structured programming and tool-driven workflow benchmarks, achieving 65.8% on SWE-bench and 53.7% on LiveCodeBench compared to Gemini’s 63.8%, making it the better choice for coding accuracy and complex automation.
• Vs. GPT-4.1: While Kimi K2’s LiveCodeBench score (53.7%) is strong, GPT-4.1 remains the leading general-purpose model, but Kimi-K2 is a competitive open alternative for users prioritizing coding precision and open-source flexibility in production environments
• Vs. Claude 4 Sonnet: Kimi K2 outperforms Claude 4 Sonnet in zero-shot code generation and agentic, tool-use scenarios, offering higher coding fidelity and deeper integration for real-world automation needs.
• No Fine-Tuning Support: The current API offering does not support model fine-tuning for end users.
• Limited to Text-Based Tasks: The model is optimized for text generation, coding, and reasoning, but not for audio, vision, or multimodal workflows.
• Open-Source Considerations: While open-source brings flexibility, it also requires more technical expertise for deployment and customization compared to fully managed cloud APIs.
Accessible via AI/ML API. Documentation: available here.
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