Kimi-K2 - AI Language Models Tool

Overview

Kimi‑K2 is a trillion‑parameter mixture‑of‑experts (MoE) language model series from Moonshot AI, engineered for agentic workflows, long‑context reasoning, coding, and tool use. The public GitHub repository and official documentation describe a 1T total‑parameter MoE with 32B parameters activated per forward pass, trained on ~15.5T tokens and supporting a 128K token context window — designed to give high representational capacity while keeping per‑token compute closer to 32B rather than 1T. ([github.com](https://github.com/MoonshotAI/Kimi-K2)) Kimi‑K2 ships in variants (Base and Instruct) and is distributed with open weights and deployment guidance; Moonshot provides an OpenAI/Anthropic‑compatible API surface and recommends inference engines such as vLLM, SGLang, KTransformers, and TensorRT‑LLM for production use. The model is positioned for multi‑step autonomous tasks (agentic tool calling, chained workflows, code execution) and reports state‑of‑the‑art results on several coding, reasoning, and agentic benchmarks. Its release and open‑source positioning were covered in major outlets when Moonshot published the model publicly. ([github.com](https://github.com/MoonshotAI/Kimi-K2)) Community interest is visible on the official repo (several thousand stars and active issues) and broader discussion on public forums reflects rapidly evolving adoption, praise for code/agentic performance, and ongoing debate about reliability and safety for long‑horizon autonomous use. ([github.com](https://github.com/MoonshotAI/Kimi-K2))

GitHub Statistics

• Stars: 9,801
• Forks: 719
• Contributors: 8
• License: NOASSERTION
• Last Updated: 2025-10-31T03:23:46Z

Key Features

• Trillion‑parameter Mixture‑of‑Experts backbone (1T total, 32B activated per token).
• Extended 128K token context window for long documents and multi‑step reasoning.
• Agentic tool calling: autonomous tool invocation and chained workflows for complex tasks.
• Optimized training with Muon/MuonClip on ~15.5T tokens for stability at scale.
• Open weights and OpenAI/Anthropic‑compatible API; recommended engines: vLLM, SGLang, TensorRT‑LLM.

Example Usage

Example (python):

# Example adapted from the Kimi‑K2 GitHub README. Use your Moonshot/OpenAI-compatible client instance.
# See: https://github.com/MoonshotAI/Kimi-K2

def simple_chat(client, model_name: str):
    messages = [
        {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
        {"role": "user", "content": [{"type": "text", "text": "Please give a brief self-introduction."}]},
    ]

    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        temperature=0.6,
        max_tokens=256
    )

    print(response.choices[0].message.content)

# Usage (pseudo):
# from some_openai_compatible_client import OpenAI
# client = OpenAI(api_key="YOUR_KEY", base_url="https://platform.moonshot.ai")
# simple_chat(client, "kimi-k2-instruct")

Benchmarks

Total parameters: 1 trillion (Source: https://github.com/MoonshotAI/Kimi-K2)

Activated parameters (per inference): 32 billion (Source: https://github.com/MoonshotAI/Kimi-K2)

Training tokens reported: 15.5 trillion tokens (Source: https://github.com/MoonshotAI/Kimi-K2)

Context window: 128K tokens (Source: https://github.com/MoonshotAI/Kimi-K2)

LiveCodeBench v6 (Pass@1, Kimi‑K2‑Instruct): 53.7 (Source: https://github.com/MoonshotAI/Kimi-K2)

Key Information

• Category: Language Models
• Type: AI Language Models Tool