moonshotai/Kimi-K2-Thinking

📰 Tech Blog

1. Model Introduction

Kimi K2 Thinking is the latest, most capable version of open-source thinking model. Starting with Kimi K2, we built it as a thinking agent that reasons step-by-step while dynamically invoking tools. It sets a new state-of-the-art on Humanity's Last Exam (HLE), BrowseComp, and other benchmarks by dramatically scaling multi-step reasoning depth and maintaining stable tool-use across 200–300 sequential calls. At the same time, K2 Thinking is a native INT4 quantization model with 256k context window, achieving lossless reductions in inference latency and GPU memory usage.

Key Features

• Deep Thinking & Tool Orchestration: End-to-end trained to interleave chain-of-thought reasoning with function calls, enabling autonomous research, coding, and writing workflows that last hundreds of steps without drift.
• Native INT4 Quantization: Quantization-Aware Training (QAT) is employed in post-training stage to achieve lossless 2x speed-up in low-latency mode.
• Stable Long-Horizon Agency: Maintains coherent goal-directed behavior across up to 200–300 consecutive tool invocations, surpassing prior models that degrade after 30–50 steps.

2. Model Summary

3. Evaluation Results

Reasoning Tasks | Benchmark | Setting | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 (Thinking) | K2 0905 | DeepSeek-V3.2 | Grok-4 | |:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:|:-------:| | HLE (Text-only) | no tools | 23.9 | 26.3 | 19.8* | 7.9 | 19.8 | 25.4 | | | w/ tools | 44.9 | 41.7* | 32.0* | 21.7 | 20.3* | 41.0 | | | heavy | 51.0 | 42.0 | - | - | - | 50.7 | | AIME25 | no tools | 94.5 | 94.6 | 87.0 | 51.0 | 89.3 | 91.7 | | | w/ python | 99.1 | 99.6 | 100.0 | 75.2 | 58.1* | 98.8 | | | heavy | 100.0 | 100.0 | - | - | - | 100.0 | | HMMT25 | no tools | 89.4 | 93.3 | 74.6* | 38.8 | 83.6 | 90.0 | | | w/ python | 95.1 | 96.7 | 88.8* | 70.4 | 49.5* | 93.9 | | | heavy | 97.5 | 100.0 | - | - | - | 96.7 | | IMO-AnswerBench | no tools | 78.6 | 76.0* | 65.9* | 45.8 | 76.0* | 73.1 | | GPQA | no tools | 84.5 | 85.7 | 83.4 | 74.2 | 79.9 | 87.5 |

General Tasks | Benchmark | Setting | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 (Thinking) | K2 0905 | DeepSeek-V3.2 | |:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:| | MMLU-Pro | no tools | 84.6 | 87.1 | 87.5 | 81.9 | 85.0 | | MMLU-Redux | no tools | 94.4 | 95.3 | 95.6 | 92.7 | 93.7 | | Longform Writing | no tools | 73.8 | 71.4 | 79.8 | 62.8 | 72.5 | | HealthBench | no tools | 58.0 | 67.2 | 44.2 | 43.8 | 46.9 |

Agentic Search Tasks | Benchmark | Setting | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 (Thinking) | K2 0905 | DeepSeek-V3.2 | |:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:| | BrowseComp | w/ tools | 60.2 | 54.9 | 24.1 | 7.4 | 40.1 | | BrowseComp-ZH | w/ tools | 62.3 | 63.0* | 42.4* | 22.2 | 47.9 | | Seal-0 | w/ tools | 56.3 | 51.4* | 53.4* | 25.2 | 38.5* | | FinSearchComp-T3 | w/ tools | 47.4 | 48.5* | 44.0* | 10.4 | 27.0* | | Frames | w/ tools | 87.0 | 86.0* | 85.0* | 58.1 | 80.2* |

Coding Tasks | Benchmark | Setting | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 (Thinking) | K2 0905 | DeepSeek-V3.2 | |:----------:|:--------:|:------------:|:------:|:----------------------------:|:--------:|:--------------:| | SWE-bench Verified | w/ tools | 71.3 | 74.9 | 77.2 | 69.2 | 67.8 | | SWE-bench Multilingual | w/ tools | 61.1 | 55.3* | 68.0 | 55.9 | 57.9 | | Multi-SWE-bench | w/ tools | 41.9 | 39.3* | 44.3 | 33.5 | 30.6 | | SciCode | no tools | 44.8 | 42.9 | 44.7 | 30.7 | 37.7 | | LiveCodeBenchV6 | no tools | 83.1 | 87.0* | 64.0* | 56.1* | 74.1 | | OJ-Bench (cpp) | no tools | 48.7 | 56.2* | 30.4* | 25.5* | 38.2* | | Terminal-Bench | w/ simulated tools (JSON) | 47.1 | 43.8 | 51.0 | 44.5 | 37.7 |

Footnotes

1. To ensure a fast, lightweight experience, we selectively employ a subset of tools and reduce the number of tool call steps under the chat mode on kimi.com. As a result, chatting on kimi.com may not reproduce our benchmark scores. Our agentic mode will be updated soon to reflect the full capabilities of K2 Thinking.

2. Testing Details: 2.1. All benchmarks were evaluated at temperature = 1.0 and 256 k context length for K2 Thinking, except for SciCode, for which we followed the official temperature setting of 0.0. 2.2. HLE (no tools), AIME25, HMMT25, and GPQA were capped at a 96k thinking-token budget, while IMO-Answer Bench, LiveCodeBench and OJ-Bench were capped at a 128k thinking-token budget. Longform Writing was capped at a 32k completion-token budget. 2.3. For AIME and HMMT (no tools), we report the average of 32 runs (avg@32). For AIME and HMMT (with Python), we report the average of 16 runs (avg@16). For IMO-AnswerBench, we report the average of 8 runs (avg@8).

3. Baselines: 3.1 GPT-5, Claude-4.5-sonnet, Grok-4 results and DeepSeek-V3.2 results are quoted from the GPT-5 post, GPT-5 for Developers post, GPT-5 system card, claude-sonnet-4-5 post, grok-4 post, deepseek-v3.2 post, the public Terminal-Bench leaderboard (Terminus-2), the public Vals AI leaderboard and artificialanalysis. Benchmarks for which no available public scores were re-tested under the same conditions used for k2 thinking and are marked with an asterisk(*). For the GPT-5 test, we set the reasoning effort to high. 3.2 The GPT-5 and Grok-4 on the HLE full set with tools are 35.2 and 38.6 from the official posts. In our internal evaluation on the HLE text-only subset, GPT-5 scores 41.7 and Grok-4 scores 38.6 (Grok-4’s launch cited 41.0 on the text-only subset). For GPT-5's HLE text-only w/o tool, we use score from Scale.ai. The official GPT5 HLE full set w/o tool is 24.8. 3.3 For IMO-AnswerBench: GPT-5 scored 65.6 in the benchmark paper. We re-evaluated GPT-5 with official API and obtained a score of 76.

4. For HLE (w/ tools) and the agentic-search benchmarks: 4.1.…