Kimi K2.6
Moonshot AI · released Apr 20, 2026
Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...
Specification
- Context window
- 262K
- Max output
- 262K
- Knowledge cutoff
- Not stated
- Parameters
- 1058.6B
- Licence
- other
- Serving providers
- 21
- Moderated
- No
- Uptime
- 100.0%
Intelligence
45.1
93th percentile
Coding
61.8
Coding Index
Agentic
31.2
Agentic Index
Output speed
89 t/s
Median across providers
Latency
337ms
Time to first token
Cost per task
$0.23
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
- τ²-bench (Telecom)95.9%
- GPQA Diamond91.1%
- AA-LCR (long context)76.7%
- IFBench76.0%
- SciCode53.5%
- Terminal-Bench Hard43.9%
- Humanity's Last Exam37.5%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| τ²-bench (Telecom) | 95.9% |
| GPQA Diamond | 91.1% |
| AA-LCR (long context) | 76.7% |
| IFBench | 76.0% |
| SciCode | 53.5% |
| Terminal-Bench Hard | 43.9% |
| Humanity's Last Exam | 37.5% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Qwen3.7 MaxQwen46.7
- GPT-5.3-CodexOpenAI45.5
- MiniMax M3MiniMax45.4
- V4 ProDeepSeek45.3
- Kimi K2.6Moonshot AI45.1
- GPT-5.2OpenAI43.3
- Kimi K2.7 CodeMoonshot AI43.0
- MiMo-V2.5-ProXiaomi42.9
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Qwen3.7 Max | 46.7 |
| GPT-5.3-Codex | 45.5 |
| MiniMax M3 | 45.4 |
| V4 Pro | 45.3 |
| Kimi K2.6 | 45.1 |
| GPT-5.2 | 43.3 |
| Kimi K2.7 Code | 43.0 |
| MiMo-V2.5-Pro | 42.9 |
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
- $0.589
- Output / 1M tokens
- $2.48
- Cached input / 1M
- $0.099
- Blended 3:1
- $1.06
One task, estimated
$0.23
- 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
- Chutes
- Baidu
- Decart
- Inceptron
- CoreWeave
- StreamLake
- Crusoe
- Parasail
- Venice
- DeepInfra
- DigitalOcean
- SiliconFlow
- Novita
- AtlasCloud
- Moonshot AI
- Cloudflare
- Sail Research
- Phala
- Together
- BaseTen
- Fireworks
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
- 1,329
- Win rate
- 60.3%
- Strongest at
- 3D scenes
- Tournaments
- 10,809
Elo by category
Dot position on a 1,175–1,350 scale · Elo has no meaningful zero
- 1,329
- 1,299
- 1,297
- 1,290
- 1,286
- 1,225
- 1,221
- 1,198
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 | 1329 | 60.3% |
| UI components | 1299 | 56.0% |
| Websites | 1297 | 55.5% |
| Game development | 1290 | 56.0% |
| Data visualisation | 1286 | 56.1% |
| SVG | 1225 | 51.5% |
| Mobile apps | 1221 | 54.9% |
| Full-stack apps | 1198 | 54.8% |