Kimi K2 ThinkingKimi K2 Thinking
New🚀 1T Parameter Open-Source Model - 256K Context, Deep Reasoning Mode

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.

Reviews

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)

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 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

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

Kimi K2 Thinking Benchmark Comparison - Agentic Coding, Tool Use, Math & STEM

Comprehensive performance comparison across Agentic & Competitive Coding, Tool Use, and Math & STEM benchmarks

Coding Tasks

Software engineering and competitive programming benchmarks

BenchmarkK2 ThinkingGPT-5 (High)Claude Sonnet 4.5K2 0905DeepSeek-V3.2
SWE-bench Verified (w/ tools)71.374.977.269.267.8
SWE-bench Multilingual (w/ tools)61.155.3*68.055.957.9
LiveCodeBench v6 (no tools)83.187.0*64.0*56.1*74.1
OJ-Bench (cpp) (no tools)48.756.2*30.4*25.5*38.2*
Terminal-Bench (w/ simulated tools)47.143.851.044.537.7

Reasoning Tasks

Multi-step reasoning, mathematics, and STEM problem-solving

BenchmarkK2 ThinkingGPT-5 (High)Claude Sonnet 4.5K2 0905DeepSeek-V3.2Grok-4
HLE (w/ tools)44.941.7*32.0*21.720.3*41.0
AIME25 (w/ python)99.199.6100.075.258.1*98.8
HMMT25 (w/ python)95.196.788.8*70.449.5*93.9
GPQA (no tools)84.585.783.474.279.987.5

* 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.

1

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
2

Install vLLM

Install vLLM inference framework:

bash
pip install vllm
3

Download Model

Download the model from Hugging Face:

bash
huggingface-cli download moonshotai/Kimi-K2-Thinking --local-dir ./kimi-k2-thinking
4

Launch vLLM Server

Start the inference server with essential parameters:

vLLM Deployment
bash
vllm serve moonshotai/Kimi-K2-Thinking \
  --tensor-parallel-size 8 \
  --tool-call-parser kimi_k2 \
  --reasoning-parser kimi_k2 \
  --max-num-batched-tokens 32768
5

Test Deployment

Verify the deployment is working:

Test API
bash
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 Guide

Key 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.

Community Reactions on X

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