Model overview
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. **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 |
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。