The Next Generation Open-Source Reasoning AI Model with Trillion Parameters
Kimi K2 Thinking is the latest generation open-source reasoning AI model developed by Moonshot AI, officially released in November 2025. As the pinnacle of the Kimi model series, Kimi K2 Thinking is specifically designed for complex reasoning tasks and serves as an intelligent agent with "thinking" capabilities.
The Kimi K2 Thinking model extends the K2 series into long-horizon, agentic reasoning, meaning the model can sustain a multi-step chain-of-thought over very lengthy interactions.
Moonshot AI was founded in 2023 by Yang Zhilin and colleagues, headquartered in Beijing. As one of China's "AI Unicorn" companies, it focuses on large language model development.
The Kimi series evolution: Initial release (late 2023) → K1.5 (early 2025) → K2 (July 2025) → K2-Instruct (September 2025) → Kimi K2 Thinking (November 2025).
1 trillion total parameters, 32 billion active per inference
15.5 trillion tokens of high-quality data
Custom Muon optimizer for stable training
Sparse MoE architecture with 1T total parameters and 32B activated per inference for optimal performance
Process up to 200,000+ words in a single context, far exceeding traditional LLM capabilities
Native 4-bit quantization doubles inference speed without performance loss
Fully open weights and code under modified open-source license for research and commercial use
Invoke external tools and functions during reasoning process for enhanced capabilities
Multi-step chain-of-thought reasoning for complex problem-solving tasks
Maintains complete model performance at INT4 precision with quantization-aware training
Significantly reduces deployment memory requirements for cost-effective scaling
Zero instabilities during pre-training with custom Muon optimizer
Complete processing of technical documents and entire codebases
| Benchmark | Kimi K2 Thinking | GPT-4 | Claude-3 |
|---|---|---|---|
| Math Reasoning (GSM8K) | 94.2% | 92.0% | 90.1% |
| Code Generation (HumanEval) | 89.7% | 87.3% | 85.9% |
| Logical Reasoning (HellaSwag) | 96.1% | 95.3% | 94.8% |
| Chinese Understanding (C-Eval) | 91.8% | 86.4% | 84.2% |
| Feature | Kimi K2 Thinking | Llama 3.1 405B | Qwen2.5 72B |
|---|---|---|---|
| Parameter Scale | 1T (32B active) | 405B | 72B |
| Context Length | 256K | 128K | 32K |
| Open Source License | Modified Open Source | Llama License | Apache 2.0 |
| Chinese Capability | Excellent | Good | Excellent |
| Reasoning Ability | Excellent | Good | Good |
Download Kimi K2 Thinking model weights from official sources
pip install transformers torchSet up your environment with required hardware specifications
Deploy Kimi K2 Thinking using your preferred method
# Load Kimi K2 Thinking model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("moonshot-ai/kimi-k2-thinking")
tokenizer = AutoTokenizer.from_pretrained("moonshot-ai/kimi-k2-thinking")
# Inference example
inputs = tokenizer("Analyze this complex problem:", return_tensors="pt")
outputs = model.generate(**inputs, max_length=1000)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)Kimi K2 Thinking is a trillion-parameter open-source reasoning AI model developed by Moonshot AI, designed for complex reasoning tasks with 256K token context window and tool calling capabilities. It represents the most advanced version in the Kimi model series.
Minimum requirements include 80GB GPU memory (such as A100 or H100), 256GB system memory, and 2TB SSD storage space. For optimal performance, H100 or newer GPUs are recommended.
As an open-source model, Kimi K2 Thinking itself is free. Users only need to bear hardware deployment costs ($50,000-200,000 initial investment) and operating costs ($1,000-5,000/month). Commercial use must comply with open-source license terms.
Kimi K2 Thinking features a unique combination of trillion-parameter MoE architecture, 256K context window, native INT4 quantization, and advanced reasoning capabilities. It excels in Chinese processing and long-horizon reasoning tasks compared to other models.
Kimi K2 Thinking supports mainstream programming languages including Python, JavaScript, Java, C++, Go, Rust, and more, with excellent performance in code generation and understanding tasks.
With INT4 quantization, Kimi K2 Thinking achieves 100% speed improvement compared to FP16 inference, processing thousands of tokens per second on A100 GPUs without performance degradation.
Yes, Kimi K2 Thinking supports model parallelism and tensor parallelism, allowing deployment across multiple GPUs for effective compute resource utilization and improved performance.
Kimi K2 Thinking is particularly suitable for finance, legal, medical, education, and research industries requiring complex reasoning. It excels in document analysis, decision support, and knowledge Q&A scenarios.
Kimi K2 Thinking supports local deployment, keeping data within enterprise networks. It provides data encryption, access control, and other privacy protection features, complying with major privacy regulations.
Kimi K2 Thinking resources are available through official channels including GitHub repositories, Hugging Face Model Hub, technical documentation, and community forums. Visit the official Moonshot AI website for complete access information.
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