Kimi K2 0905 Preview

Kimi K2 0905 API Overview

Kimi K2 0905 Preview is an advanced update of the Kimi K2 model, engineered for high-performance in intelligent agent creation, multi-turn conversational AI, and complex analytical tasks. This version extends the context window to 262,144 tokens and integrates enhanced request caching, delivering unprecedented efficiency and depth in natural language understanding and reasoning. It is tailored for corporate assistants, agent-based workflows, and advanced reasoning applications requiring extensive context and memory.

Technical Specifications

• Model type: Large-scale Transformer-based language model
• Context window: 262,144 tokens (expanded from previous versions)
• Architecture: Hybrid architecture optimized for long context retention and efficient memory usage
• Training data: Diverse, high-quality corpora with focus on dialogue, reasoning, and enterprise texts
• Supported tasks: Natural language understanding, reasoning, multi-turn dialogue, text summarization, analytics
• Max output tokens per request: 8192 tokens

Performance Benchmarks

Across five distinct evaluations, including SWE-bench Verified, Multilingual, and SWE-Dev, it achieves higher average scores than both Kimi K2-0711 and Claude Sonnet 4. Each score represents the average of five rigorous test runs, ensuring statistical reliability.

Key Features

• Ultra-long context processing: Handles documents and conversations with up to 262K tokens seamlessly
• Enhanced caching mechanism: Improves throughput and latency in multi-turn sessions and repetitive queries
• Multiturn dialogue specialization: Maintains context coherency over long conversations, ideal for virtual assistants
• Intelligent agent capabilities: Supports autonomous decision-making and complex task execution
• Advanced reasoning: Excels in analytic queries involving sustained logic and inference chains

Kimi K2 0905 API Pricing

• Input: $0.1575 / 1M tokens
• Output: $2.625 / 1M tokens

Code Sample

Comparison with Other Models

vs GPT-4 Turbo: Kimi-K2-0905 offers double the context length (262K vs. 128K) and superior caching for repetitive enterprise queries. While GPT-4 excels in general creativity, Kimi-K2-0905 is optimized for structured reasoning and agent reliability.

vs Claude 3.5 Sonnet: Both deliver strong analytical performance, but Kimi-K2-0905 provides faster inference on long contexts and native support for stateful agent memory. Claude favors conversational fluency; Kimi prioritizes task completion.

vs Llama 3 70B: Llama 3 is ideal for customization, but lacks built-in long-context optimization and enterprise tooling. Kimi-K2-0905 delivers out-of-the-box performance with managed infrastructure, caching, and compliance.

vs Gemini 1.5 Pro: Gemini matches Kimi in context length, but Kimi-K2-0905 shows lower latency in cached scenarios and better tool-integration for agentic loops. Gemini leads in multimodal tasks; Kimi dominates in text-centric enterprise reasoning.