Overview

Kimi K2 Thinking represents Moonshot AI's latest advancement in open-source reasoning models, building on the capabilities of its predecessor with an enhanced deep-thinking architecture. The model combines step-by-step reasoning with dynamic tool invocation, creating an agent-like interface designed for complex problem-solving tasks that require sustained cognitive processing.

Released under a Modified MIT License, Kimi K2 Thinking supports both commercial and research applications, making advanced reasoning capabilities accessible to a wide range of users and organizations.

Key Features

Advanced Reasoning Architecture

Kimi K2 Thinking interleaves chain-of-thought reasoning with function calls, enabling autonomous workflows that can span hundreds of sequential steps without performance degradation. This architecture allows the model to maintain coherent behavior across 200-300 consecutive tool invocations, substantially exceeding earlier models that typically degrade after 30-50 calls.

Optimized Performance Through Quantization

The model features native INT4 quantization achieved through Quantization-Aware Training (QAT), providing approximately 2x faster generation speed without sacrificing performance quality. This optimization makes the model more efficient while maintaining the accuracy and reliability required for complex reasoning tasks.

Mixture-of-Experts Architecture

Built on a Mixture-of-Experts (MoE) architecture, Kimi K2 Thinking employs 1 trillion total parameters with 32 billion active parameters per inference. The model utilizes 384 experts, selecting 8 per token, distributed across 61 layers including one dense layer. This efficient design enables powerful reasoning capabilities while maintaining computational efficiency.

Extended Context Understanding

With a context window of 256,000 tokens and a vocabulary of 160,000 tokens, Kimi K2 Thinking can process and reason over extensive documents, long-form content, and complex multi-turn conversations. The model uses Multi-head Latent Attention (MLA) mechanisms to effectively manage this large context window.

Benchmark Performance

Reasoning Tasks

Kimi K2 Thinking demonstrates exceptional performance on challenging reasoning benchmarks:

• HLE (with tools): Achieves scores ranging from 44.9 to 51.0, showcasing strong logical reasoning capabilities when augmented with tool access
• AIME25 (with Python): Scores between 99.1 and 100.0 on this advanced mathematics competition benchmark
• HMMT25 (with Python): Achieves 95.1 to 97.5 on the Harvard-MIT Mathematics Tournament problems

Agentic Search Performance

The model excels at autonomous search and information retrieval tasks:

• BrowseComp: 60.2 score on English web browsing comprehension
• BrowseComp-ZH: 62.3 score on Chinese web browsing comprehension
• Seal-0: 56.3 on search-enhanced language tasks

Coding Capabilities

Kimi K2 Thinking shows strong performance on software engineering benchmarks:

• SWE-bench Verified: 71.3 score on real-world software engineering tasks
• LiveCodeBenchV6: 83.1 on live coding challenges

Use Cases

Autonomous Research

The model's ability to maintain coherent reasoning across hundreds of sequential steps makes it ideal for autonomous research tasks that require iterative information gathering, analysis, and synthesis. The extended agency duration allows it to conduct comprehensive investigations without losing track of the overall objective.

Complex Coding Projects

With strong performance on software engineering benchmarks, Kimi K2 Thinking excels at understanding codebases, debugging complex issues, and implementing multi-step solutions. The model's reasoning capabilities enable it to break down complex programming challenges into manageable steps.

Extended Writing Projects

The large context window and sustained reasoning capabilities make the model well-suited for long-form content creation, technical documentation, and structured writing projects that require maintaining consistency and coherence across thousands of tokens.

Problem-Solving with Tool Integration

The model's architecture enables it to seamlessly integrate reasoning with tool calls, making it effective for tasks that require both analytical thinking and practical execution. This includes data analysis workflows, computational problem-solving, and tasks requiring web search or API interactions.

Training Approach

Kimi K2 Thinking incorporates Quantization-Aware Training (QAT) directly into its training process, enabling native INT4 quantization without the quality degradation typically associated with post-training quantization. This approach allows the model to maintain high performance while operating with improved efficiency.

The model's training focused on developing extended reasoning chains and tool integration capabilities, enabling the agent-like behavior that distinguishes it from traditional language models. The recommended operating temperature for inference is 1.0, optimizing the balance between creativity and consistency in the model's outputs.