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Moonshot · Open weights

Kimi K2 Thinking

Added Nov 6, 2025Open weights

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Use in the app

Moonshot AI's distinct Kimi K2 Thinking checkpoint is a mandatory-reasoning model for long-horizon agentic workflows and multi-step tool use.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.60
Output
$2.50
Cache read
$0.15

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
98.3K
Average output (7 days)
1.9K tokens
Longer than 91% of 465 models

Side by side

Kimi K2 Thinking compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Kimi K2 ThinkingMoonshotThis modelGemini 3.5 Flash LiteGoogleRemove
ProviderMoonshotGoogle
Discovery tierOpen weightsEfficient
AddedNov 6, 2025Jul 21, 2026
Limits
Context window262.14K1.05MBest
Max output98.3KBest65.54K
Average output1.9K tokens178 tokens
Pricing, USD per 1M tokens
Input$0.60$0.30Best
Output$2.50$2.50
Cache read$0.15$0.03Best
Cache write—$0.0833
Performance
Throughput48.0 tok/s237.4 tok/sBest
Time to first token2.6 s1.4 sBest
Request success (24h)99.6%99.9%Best
Benchmark indexes
Intelligence Index22.022.2Best
Coding Index—49.3
Math Index94.7—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Kimi K2 Thinking
  • Gemini 3.5 Flash Lite

Intelligence Index

  • Kimi K2 Thinking22.0
  • Gemini 3.5 Flash Lite22.2

Kimi K2 Thinking: Better than 53% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 49.3

  • Math Index

    Composite of the math evaluations

    • Kimi K2 Thinking: 94.7
    • Gemini 3.5 Flash Lite: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 25.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 3.3%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 650 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 985 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 13.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Kimi K2 Thinking: 72.0%
    • Gemini 3.5 Flash Lite: 76.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 10.6%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Kimi K2 Thinking: 23.8%Top
    • Gemini 3.5 Flash Lite: 18.8%

  • GPQA Diamond

    Graduate-level science questions

    • Kimi K2 Thinking: 83.8%Top
    • Gemini 3.5 Flash Lite: 83.8%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Kimi K2 Thinking: 84.8%
    • Gemini 3.5 Flash Lite: —

  • IFBench

    Following precise output instructions

    • Kimi K2 Thinking: 68.1%
    • Gemini 3.5 Flash Lite: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 1.0%

  • SciCode

    Scientific computing in Python

    • Kimi K2 Thinking: —
    • Gemini 3.5 Flash Lite: 41.3%

  • LiveCodeBench

    Recent competitive programming problems

    • Kimi K2 Thinking: 85.3%
    • Gemini 3.5 Flash Lite: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Kimi K2 Thinking: 94.7%
    • Gemini 3.5 Flash Lite: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Kimi K2 Thinking: 31.1%
    • Gemini 3.5 Flash Lite: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Kimi K2 Thinking: 93.0%
    • Gemini 3.5 Flash Lite: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Kimi K2 Thinking: 72.0%
    • Gemini 3.5 Flash Lite: 76.0%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
48.0 tok/s
Faster than 36% of 250 models
Time to first token
2.6 s
Quicker than 64% of 250 models
Prompt cache hits
27.5%
About $0.48 per 1M input tokens with caching
Request success (24h)
99.6%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 95.4%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k2-thinking",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="moonshotai/kimi-k2-thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Moonshot

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