Meet Kimi K2: The 1-Trillion-Parameter AI That’s Redefining Coding, Reasoning, and Product Creation
TL;DR – Moonshot AI just open-sourced Kimi K2, a 1-trillion-parameter mixture-of-experts (MoE) model that beats GPT-4.1, Claude Opus 4, and Gemini 2.5 on coding, math, and tool-use benchmarks. Below you’ll find the key specs, benchmark scores, and a step-by-step guide to deploy it for free on vLLM or SGLang—plus a design hack that will make your AI-powered apps look as smart as they perform.
🔥 Why Kimi K2 Matters in 2025
Every week a new “state-of-the-art” model drops—but Kimi K2 is different. Trained on 15.5 T tokens with zero training instability, it unlocks 65.8 % single-attempt accuracy on SWE-bench Verified, crushing DeepSeek-V3 by +27 points and GPT-4.1 by +11 points.
Whether you’re a startup shipping production code, a researcher chasing SOTA, or a product designer prototyping AI agents, K2 gives you 32 B activated parameters of pure firepower—without the enterprise price tag.
🧠 Model Snapshot
| Spec | Value |
|---|---|
| Total Parameters | 1 Trillion (MoE) |
| Activated / Token | 32 Billion |
| Context Length | 128 k |
| Vocabulary | 160 k |
| Attention Heads | 64 (MLA) |
| Experts | 384 total, 8 active + 1 shared |
| Optimizer | Muon (MuonClip) |
| License | Modified MIT |
🏆 Benchmark Dominance
Coding
- LiveCodeBench v6 – 53.7 % (SOTA)
- SWE-bench Verified (Agentic) – 65.8 % single-try, 71.6 % with test-time compute
Math & STEM
- AIME 2024 – 69.6 / 64 (beats Claude Opus 4 by +26.2)
- MATH-500 – 97.4 % accuracy
- GPQA-Diamond – 75.1 % (research-grade science Q&A)
Tool Use
- Tau2 retail – 70.6 % average on 4-shot tasks
- AceBench – 76.5 %, topping Gemini 2.5 Flash
Full leaderboard: Kimi-K2 GitHub
🚀 5-Minute Local Deployment Guide
Option A – vLLM (single GPU)
pip install vllm
vllm serve moonshotai/Kimi-K2-Instruct \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--gpu-memory-utilization 0.9
Option B – SGLang (multi-GPU)
pip install sglang
python -m sglang.launch_server \
--model moonshotai/Kimi-K2-Instruct \
--tp 2 \
--trust-remote-code
Both engines support OpenAI-compatible endpoints, so you can drop K2 into existing apps with zero code changes.
🛠️ Quick Start Snippets
Chat
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="Kimi-K2-Instruct",
messages=[{"role":"user","content":"Write a React hook for real-time search."}],
temperature=0.6,
max_tokens=1024
)
print(response.choices[0].message.content)
Tool Calling
K2 supports native function-calling—perfect for agents that hit APIs, run code, or query databases. Example in the repo shows how to wire a weather tool in <30 lines.
🎨 Make Your AI App Look as Smart as K2 Thinks
Even the smartest model feels clunky in an ugly UI. Before you ship, level-up your design game with Mobbin—a curated library of 500 k+ mobile & web screens from Apple, Airbnb, Spotify, and more. Copy any flow to Figma in one click, remix the interactions, and launch interfaces your users will actually love.
👉 Start browsing for free today—no credit card, endless inspiration.
📈 From Prototype to Production
- Prototype fast – Grab a ready-made design pattern on Mobbin.
- Code faster – Deploy Kimi K2 locally in 5 minutes with the snippets above.
- Ship smarter – Use K2’s 65.8 % SWE-bench score to auto-generate pull requests, tests, and documentation.
🎯 Ready to Build?
- Get the weights: Kimi-K2 on Hugging Face
- Join the community: Issues & PRs welcome on GitHub
- Need help? Drop a line to support@moonshot.cn
Stop waiting for the next closed-source miracle. Spin up Kimi K2 tonight and ship the future before breakfast.
