Kimi K2 0905
Moonshot AI · released Sep 4, 2025
Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32...
Specification
- Context window
- 262K
- Max output
- 100K
- Knowledge cutoff
- Dec 31, 2024
- Parameters
- 1026.5B
- Licence
- other
- Serving providers
- 1
- Moderated
- No
- Uptime
- 100.0%
Intelligence
24.0
66th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
19 t/s
Median across providers
Latency
861ms
Time to first token
Cost per task
$0.09
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
- MMLU-Pro81.9%
- GPQA Diamond76.7%
- τ²-bench (Telecom)73.4%
- LiveCodeBench61.0%
- AIME 202557.3%
- AA-LCR (long context)53.7%
- IFBench41.7%
- SciCode30.7%
- Terminal-Bench Hard23.5%
- Humanity's Last Exam6.4%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 81.9% |
| GPQA Diamond | 76.7% |
| τ²-bench (Telecom) | 73.4% |
| LiveCodeBench | 61.0% |
| AIME 2025 | 57.3% |
| AA-LCR (long context) | 53.7% |
| IFBench | 41.7% |
| SciCode | 30.7% |
| Terminal-Bench Hard | 23.5% |
| Humanity's Last Exam | 6.4% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Qwen3 MaxQwen24.5
- Ling 3.0 Tiny (free)InclusionAI24.3
- Claude Haiku 4.5Anthropic24.1
- gpt-oss-120bOpenAI24.1
- Kimi K2 0905Moonshot AI24.0
- o1OpenAI23.9
- GLM 4.6Z.ai23.4
- GLM 4.7 FlashZ.ai23.3
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Qwen3 Max | 24.5 |
| Ling 3.0 Tiny (free) | 24.3 |
| Claude Haiku 4.5 | 24.1 |
| gpt-oss-120b | 24.1 |
| Kimi K2 0905 | 24.0 |
| o1 | 23.9 |
| GLM 4.6 | 23.4 |
| GLM 4.7 Flash | 23.3 |
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
- $0.6
- Output / 1M tokens
- $2.5
- Cached input / 1M
- Not offered
- Blended 3:1
- $1.07
One task, estimated
$0.09
- Input tokens
- 50,000
- Output tokens
- 25,000
- Profile
- Standard
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.