Kimi-K2-Thinking - AI Language Models Tool

Overview

Kimi‑K2‑Thinking is Moonshot AI’s open‑weight “thinking” LLM built for step‑by‑step reasoning, long‑horizon agentic workflows, and native tool integration. The model uses a sparse Mixture‑of‑Experts backbone (≈1 trillion total parameters, ≈32B activated at inference) and a 256K token context window so it can hold entire codebases, multi‑document research contexts, or long multi‑step plans in memory. Moonshot designed K2 Thinking to interleave explicit chain‑of‑thought traces with function/tool calls, enabling autonomous workflows that can run hundreds of sequential tool invocations without human steering. (See the model card for usage examples and chat/tool‑calling snippets.) ([huggingface.co](https://huggingface.co/moonshotai/Kimi-K2-Thinking)) K2 Thinking is optimized for production inference with Quantization‑Aware Training (INT4 QAT) on MoE components and compressed‑tensor checkpoints, trading precision for large speed/memory savings while retaining benchmarked performance. The model card reports strong results on multi‑step reasoning and agentic browsing benchmarks (for example, Humanity’s Last Exam (HLE) and BrowseComp) and Moonshot provides an OpenAI/Anthropic‑compatible API and third‑party host integrations for immediate use. Community feedback shows enthusiasm for the capabilities and some early integration issues (see community threads). ([huggingface.co](https://huggingface.co/moonshotai/Kimi-K2-Thinking))

Model Statistics

• Downloads: 280,849
• Likes: 1599
• Pipeline: text-generation

License: other

Model Details

Architecture and scale: Kimi‑K2‑Thinking is a Mixture‑of‑Experts (MoE) transformer family model with ~1T total parameters and ~32B activated parameters per forward pass. The published model summary lists 61 layers, 384 experts, and 8 experts selected per token; attention hidden dimension = 7168, MoE hidden dim per expert = 2048, 64 attention heads, and a 160K token vocabulary. The model uses a Multi‑Head Latent/MLA attention variant and SwiGLU activations. ([huggingface.co](https://huggingface.co/moonshotai/Kimi-K2-Thinking)) Long context & agentic design: K2 Thinking supports a 256,000‑token context window (256K) and is explicitly trained to interleave internal reasoning traces (auxiliary reasoning_content) with tool calls (search, code interpreter, web browsing). Moonshot documents stability across 200–300 sequential tool invocations and provides a Heavy Mode that rolls out multiple trajectories and aggregates results for harder queries. These capabilities make it suitable for autonomous research workflows, multi‑step code generation and repair, and longform synthesis that requires persistent internal state. ([huggingface.co](https://huggingface.co/moonshotai/Kimi-K2-Thinking)) Quantization & deployment: To make inference practical, Moonshot applied Quantization‑Aware Training (QAT) to enable native INT4 weight‑only inference on the MoE components and ships checkpoints in compressed‑tensors format. The model card and partner system cards recommend inference engines such as vLLM, SGLang and KTransformers and note TPU/GPU targets for production. Moonshot also exposes OpenAI/Anthropic‑compatible API endpoints for hosted usage. ([huggingface.co](https://huggingface.co/moonshotai/Kimi-K2-Thinking))

Key Features

• Mixture‑of‑Experts (MoE) design: ~1T params, ~32B activated for efficient high-capacity inference.
• 256K token context window for entire codebases, books, and multi-file projects.
• Native INT4 Quantization (QAT) for ~2× inference speed and lower GPU memory footprint.
• Agentic tool orchestration: trained to interleave chain‑of‑thought with function/tool calls.
• Stable long‑horizon agency: documented operation across 200–300 consecutive tool calls.

Example Usage

Example (python):

from openai import OpenAI

# Example: OpenAI-compatible chat completion (model card sample adapted)
# Set OPENAI_API_KEY to your Moonshot API key (platform.moonshot.ai is OpenAI-compatible)

client = OpenAI(api_key="YOUR_API_KEY")
model_name = "moonshotai/Kimi-K2-Thinking"

messages = [
    {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
    {"role": "user", "content": "Plan a multi-step research workflow to summarize recent results on battery recycling."}
]

response = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=1.0,
    max_tokens=2048,
    stream=False
)

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

# For tool-calling examples and agentic flows, consult the model card 'Tool Calling' section on Hugging Face.
# Source: Hugging Face model card usage examples. ([huggingface.co](https://huggingface.co/moonshotai/Kimi-K2-Thinking))

Pricing

Moonshot operates an OpenAI/Anthropic‑compatible hosted API (platform.moonshot.ai). Following a November 6, 2025 update, Moonshot published reduced input token rates (cache‑hit and cache‑miss tiers) and lists an output token rate around $2.50 per million tokens; several third‑party hosts show similar tiered pricing (input cache‑hit as low as $0.15/1M, cache‑miss higher). Pricing varies by provider, cache behavior, and endpoint (standard vs turbo). Check platform.moonshot.ai or your chosen host for current, region‑specific rates and enterprise terms. ([platform.moonshot.ai](https://platform.moonshot.ai/blog/posts/Kimi_API_Newsletter?utm_source=openai))

Benchmarks

Humanity's Last Exam (HLE) — with tools: 44.9% (Source: https://huggingface.co/moonshotai/Kimi-K2-Thinking)

BrowseComp (agentic web‑search + reasoning): 60.2% (Source: https://huggingface.co/moonshotai/Kimi-K2-Thinking)

Model scale (total / activated parameters): ≈1T total, ≈32B activated (Source: https://huggingface.co/moonshotai/Kimi-K2-Thinking)

Context window (maximum): 256,000 tokens (Source: https://huggingface.co/moonshotai/Kimi-K2-Thinking)

Native INT4 quantization speedup (reported): ≈2× generation speed improvement (INT4 QAT) (Source: https://huggingface.co/moonshotai/Kimi-K2-Thinking)

Key Information

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