Moonshot · Open weights
Kimi K3
Kimi K3 is Moonshot AI's open-weight, always-thinking multimodal model for long-context reasoning, coding, tool use, and native image/video understanding.
- Reasoning
- Tools
- Vision
- Structured output
- Video input
- Input
- Text, Images, Video
- Output
- Text
- Reasoning effort
- low · high · max
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $1.20
- Output
- $5.50
- Cache read
- $0.20
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- —
- Average output (7 days)
- 1.1K tokens
- Longer than 75% of 473 models
Side by side
Compare models
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Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
43.6
Tested at max reasoning effort
Better than 89% of 326 scored models
Indexes
Coding Index
76.2Composite of the coding evaluations
Better than 91% of 168 scored models
Agentic work
AutomationBench-AA
58.3%Workflow automation, penalized for guardrail violations
Better than 75% of 173 scored models
AutomationBench-AA tasks completed
24.8%Workflows finished end to end without a guardrail violation
Better than 72% of 173 scored models
Harvey LAB-AAv1.1
5.3%Legal tasks passed in full, with no material hallucination
Better than 82% of 29 scored models
AA-Briefcasev1.1
1,499 EloAgentic knowledge work, rated head to head
Better than 83% of 166 scored models
GDPval-AAv2.1
1,534 EloEconomically valuable occupational tasks, rated head to head
Better than 80% of 187 scored models
Document reasoning
GDP.pdf
22.0%Professional reasoning over PDF documents, all-pass rate
Better than 80% of 171 scored models
AA-LCRv1.1
88.7%Reasoning across long documents
Better than 99% of 309 scored models
MLCR-AA
21.7%Long-context reasoning over medical records
Better than 84% of 121 scored models
Reasoning and knowledge
Humanity’s Last Exam
46.9%Expert-level questions across academic fields
Better than 90% of 323 scored models
GPQA Diamond
93.5%Graduate-level science questions
Better than 92% of 291 scored models
Coding
Terminal-Benchv4.0
12.6%Practical software engineering in a terminal
Better than 65% of 171 scored models
SciCode
59.5%Scientific computing in Python
Better than 92% of 174 scored models
Legacy evaluations
AA-LCR (unversioned)
88.7%Long-context reasoning, earlier grading
Better than 99% of 309 scored models
Scores from Artificial Analysis, updated Oct 26, 2026. Kimi K3 was tested at max reasoning effort; compare scores measured at the same effort.Artificial Analysis
- #3Roleplay59.6%
- #11Creative writing32.3%
- #8Coding28.6%
- #17Math3.8%
Ranks among Noumen catalog models on Use cases leaderboards. Only top-20 placements are listed.
- #14Overall1487.4
- #9Coding1539.1
- #17Math1498.7
- #10Longer Query1502.5
- #12Instruction Following1489.1
- #10Hard Prompts1516.1
- #10Chinese1541.5
- #11English1494.4
- #15Exclude Ties1501.3
- #10Expert1532.0
- #13French1506.9
- #3German1507.5
- #8Hard Prompts English1517.3
- #12Industry Business And Management And Financial Operations1487.1
- #15Industry Entertainment And Sports And Media1460.7
- #7Industry Legal And Government1509.2
- #5Industry Life And Physical And Social Science1523.8
- #13Industry Mathematical1507.6
- #7Industry Medicine And Healthcare1513.3
- #15Industry Software And It Services1522.9
- #13Industry Writing And Literature And Language1475.5
- #5Japanese1510.6
- #7Korean1472.4
- #9Multi Turn1501.8
- #15Non English1476.3
- #14Russian1495.6
- #14Spanish1482.0
- #11WebDev Overall1654.9
- #11WebDev · Webdev1654.9
- #12WebDev · Webdev-html1642.2
- #11WebDev · Webdev-react1669.8
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
- #15FrontierCode Main44.2%
- #15FrontierCode Extended58.2%
Ranks among Noumen catalog models on FrontierCode leaderboards. Only top-20 placements are listed.FrontierCode
- #18Epoch Capabilities Index157.9
Ranks among Noumen catalog models on Epoch AI leaderboards. Only top-20 placements are listed.Epoch AI
- #19ARC-AGI-194.5%
Ranks among Noumen catalog models on ARC Prize leaderboards. Only top-20 placements are listed.ARC Prize
- #4EQ-Bench 41318
- #5Creative Writing v32084
- #18Longform Writing79.6
Ranks among Noumen catalog models on EQ-Bench leaderboards. Only top-20 placements are listed.EQ-Bench
- #10Game Development1361
- #4Website1335
- #9UI Component1339
- #3Data Visualization1350
- #8SVG1301
- #103D1384
- #4Agentic Game Dev1218
- #7Web Apps1295
- #3Full Stack1315
- #3Native Apps1254
- #4Mobile Apps1267
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
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
- 121.5 tok/s
- Faster than 69% of 260 models
- Time to first token
- 2.3 s
- Quicker than 69% of 260 models
- Prompt cache hits
- 81.0%
- About $0.39 per 1M input tokens with caching
- Request success (24h)
- 99.9%
- 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.
- 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.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/kimi-k3",
"reasoning_effort": "low",
"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-k3",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Moonshot
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