Kimi K2 Thinking: Deep Reasoning AI with Extended Context
A trillion-parameter MoE model designed for deep multi-step reasoning and extended context understanding. With 256K token context window and native thinking mode, Kimi K2 Thinking delivers state-of-the-art performance on complex reasoning tasks while maintaining cost efficiency. Fully open-source under Modified MIT license.
What Developers Are Saying About Kimi K2 Thinking
Watch technical reviews and hands-on demonstrations from AI researchers, developers, and tech experts exploring Kimi K2 Thinking's capabilities
Kimi K2 Thinking is CRAZY... (HUGE UPDATE)
now waiting for a 20B distillation
Kimi K2 Thinking Is The BEST Open Source Model - First Look & Testing
Kimi's writing is always so good. It's human-like and rarely detected in AI detector.
Kimi K2 explained in 5 minutes
Quick correction: the recommended hardware on MoonShot AI's site for running the k2-base is 8 units of h100 for the quantized version so the cost is at least 8x than what i calculated here. It's still a bit behind in feasibility but the point remains that the gap will change. I apologize for the miscalculation!
Performance Benchmark Comparison
See how Kimi K2 Thinking performs against leading AI models across key reasoning, coding, and agentic benchmarks.
Performance Across Key Categories
Comprehensive performance comparison across Agentic & Competitive Coding, Tool Use, and Math & STEM benchmarks
Coding Tasks
Software engineering and competitive programming benchmarks
| Benchmark | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 | 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 |
| LiveCodeBench v6 (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) | 47.1 | 43.8 | 51.0 | 44.5 | 37.7 |
Reasoning Tasks
Multi-step reasoning, mathematics, and STEM problem-solving
| Benchmark | K2 Thinking | GPT-5 (High) | Claude Sonnet 4.5 | K2 0905 | DeepSeek-V3.2 | Grok-4 |
|---|---|---|---|---|---|---|
| HLE (w/ tools) | 44.9 | 41.7* | 32.0* | 21.7 | 20.3* | 41.0 |
| AIME25 (w/ python) | 99.1 | 99.6 | 100.0 | 75.2 | 58.1* | 98.8 |
| HMMT25 (w/ python) | 95.1 | 96.7 | 88.8* | 70.4 | 49.5* | 93.9 |
| GPQA (no tools) | 84.5 | 85.7 | 83.4 | 74.2 | 79.9 | 87.5 |
* indicates values from third-party reports or unofficial sources
Data source: Official Kimi K2 Thinking Model Card
Quick Start Guide
Deploy Kimi K2 Thinking on your infrastructure using vLLM. Simple 5-step setup for production-ready inference.
Hardware Requirements
Minimum setup for deploying Kimi K2 Thinking:
- •8x GPUs with Tensor Parallel (NVIDIA H200 recommended)
- •Supports INT4 quantized weights with 256k context length
Install vLLM
Install vLLM inference framework:
pip install vllmDownload Model
Download the model from Hugging Face:
huggingface-cli download moonshotai/Kimi-K2-Thinking --local-dir ./kimi-k2-thinkingLaunch vLLM Server
Start the inference server with essential parameters:
vllm serve moonshotai/Kimi-K2-Thinking \
--tensor-parallel-size 8 \
--tool-call-parser kimi_k2 \
--reasoning-parser kimi_k2 \
--max-num-batched-tokens 32768Test Deployment
Verify the deployment is working:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/Kimi-K2-Thinking",
"messages": [
{"role": "user", "content": "Hello, what is 1+1?"}
]
}'For complete deployment guide including SGLang and KTransformers:
Official Deployment GuideKey Capabilities of Kimi K2 Thinking
Discover the powerful features that make Kimi K2 Thinking ideal for complex reasoning and development workflows.
Deep Chain-of-Thought Reasoning
End-to-end trained for multi-step reasoning with native thinking mode. Maintains coherent logic across 200-300 sequential tool calls for complex problem-solving.
Extended Context Understanding
Industry-leading 256K token context window enables processing entire codebases, lengthy documents, and multi-file projects while preserving full context throughout.
Trillion-Parameter MoE Architecture
1 trillion parameter Mixture-of-Experts design with 32B active parameters per forward pass, delivering exceptional performance with efficient computational cost.
Superior Coding & Agent Capabilities
Achieves 71.3% on SWE-bench Verified and 83.1% on LiveCodeBench v6. Excels at agentic tasks with 60.2% on BrowseComp and 44.9% on Humanity's Last Exam.
Native INT4 Quantization
Quantization-aware training enables 2x inference acceleration with INT4 precision while maintaining model quality for production deployment.
Open-Source & Cost-Effective
Released under Modified MIT License with API pricing at $0.60/M input tokens ($0.15 with cache) and $2.50/M output - 60-80% cheaper than GPT-4 and Claude.