Kimi K3
Moonshot AI · released Jul 16, 2026
Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...
Specification
- Context window
- 1.05M
- Max output
- —
- Knowledge cutoff
- Not stated
- Parameters
- 2779.9B
- Licence
- other
- Serving providers
- 9
- Moderated
- No
- Uptime
- 100.0%
Intelligence
59.7
99th percentile
Coding
76.2
Coding Index
Agentic
54.3
Agentic Index
Output speed
71 t/s
Median across providers
Latency
1.15s
Time to first token
Cost per task
$1.35
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- GPQA Diamond93.5%
- AA-LCR (long context)82.7%
- SciCode58.7%
- Humanity's Last Exam46.9%
- τ²-bench (Telecom)46.0%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| GPQA Diamond | 93.5% |
| AA-LCR (long context) | 82.7% |
| SciCode | 58.7% |
| Humanity's Last Exam | 46.9% |
| τ²-bench (Telecom) | 46.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Claude Opus 5Anthropic63.1
- Claude Fable 5Anthropic62.1
- GPT-5.6 SolOpenAI60.9
- Kimi K3Moonshot AI59.7
- Qwen3.8 MaxQwen58.1
- Claude Opus 4.8Anthropic57.3
- Muse Spark 1.2Meta56.8
- GPT-5.6 TerraOpenAI56.6
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Claude Opus 5 | 63.1 |
| Claude Fable 5 | 62.1 |
| GPT-5.6 Sol | 60.9 |
| Kimi K3 | 59.7 |
| Qwen3.8 Max | 58.1 |
| Claude Opus 4.8 | 57.3 |
| Muse Spark 1.2 | 56.8 |
| GPT-5.6 Terra | 56.6 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $3
- Output / 1M tokens
- $15
- Cached input / 1M
- $0.3
- Blended 3:1
- $6
One task, estimated
$1.35
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,455
- Win rate
- 69.4%
- Strongest at
- 3D scenes
- Tournaments
- 2,608
Elo by category
Dot position on a 1,375–1,475 scale · Elo has no meaningful zero
- 1,455
- 1,394
- 1,381
- 1,378
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| 3D scenes | 1455 | 69.4% |
| UI components | 1394 | 64.5% |
| Data visualisation | 1381 | 65.4% |
| Websites | 1378 | 63.5% |