Kimi K2.6
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
53.9
AA Index
Coding
47.1
AA Index
Agentic
66.0
AA Index
Intelligence Index - Kimi K2.6 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-06-07.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 87.1 |
| GSM8K | 96.4 |
| GPQA | 91.1 |
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve moonshotai/Kimi-K2.6New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Kimi K2.6
Kimi K2.6 is Moonshot AI's 1-trillion parameter Mixture-of-Experts model that ranks #1 among all open-weight models on the Artificial Analysis Intelligence Index. With a score of 54, it sits at #4 overall - behind only the latest Anthropic, Google, and OpenAI flagships (all at 57). No open model has ever been this close to the closed frontier.
What makes Kimi K2.6 special
- #1 open model overall - AA Index score 54, #4 across ALL models (open + closed)
- 1T total / 32B active MoE - 384 experts, 8 selected per token
- 384 experts with 8 shared - unique architecture for specialized agentic work
- 256K context - full-document and large-codebase support
- Native vision - MoonViT encoder, 400M vision parameters
- Agent swarm - scales to 300 sub-agents, 4,000 coordinated steps
- Modified MIT license - open weights with attribution requirement
Benchmarks
| Benchmark | Kimi K2.6 | GPT-5.4 xHigh | Claude Opus 4.6 Max | Gemini 3.1 Pro High |
|---|---|---|---|---|
| AA Intelligence Index | 54 | - | 57 | 57 |
| AIME 2026 | 96.4 | 99.2 | 96.7 | 98.3 |
| GPQA-Diamond | 90.5 | 92.8 | 91.3 | 94.3 |
| SWE-Bench Verified | 80.2 | - | 80.8 | 80.6 |
| SWE-Bench Pro | 58.6 | 57.7 | 53.4 | 54.2 |
| LiveCodeBench v6 | 89.6 | - | 88.8 | 91.7 |
| MMMU-Pro (Vision) | 79.4 | 81.2 | 73.9 | 83.0 |
| MCPMark | 55.9 | 62.5 | 56.7 | 55.9 |
| OSWorld-Verified | 73.1 | 75.0 | 72.7 | - |
Sources: Moonshot AI model card, Artificial Analysis Intelligence Index (independent composite of 10 benchmarks).
VRAM requirements
| Precision | VRAM | Recommended Hardware |
|---|---|---|
| FP8 | ~500 GB | NVIDIA L40S (x8), server cluster |
| Q4 (GGUF) | ~220 GB | NVIDIA A6000 (x4) |
Kimi K2.6 is a data-center scale model. The 32B active parameters make inference efficient relative to its 1T total size, but you still need enterprise GPU clusters to load all weights.
How to run
# Via vLLM
vllm serve moonshotai/Kimi-K2.6 --port 8010 --tensor-parallel-size 8
# Via SGLang
sglang.launch_server --model moonshotai/Kimi-K2.6 --port 8010 --tp 8Community quotes
"Kimi K2.6 is the best all-round open model right now. Scores 54 on AA Index - that's #4 overall, behind only the latest Anthropic, Google and OpenAI."
- r/LocalLLaMA, 445 upvotes
How it compares
Kimi K2.6 leads the open-weight pack by the broadest neutral measure. It edges DeepSeek V4 Pro (AA Index 52) and GLM-5.1 (AA Index 51) on composite intelligence while adding native vision capabilities neither DeepSeek variant offers.
Compared to DeepSeek V4 Pro: K2.6 has stronger general knowledge and vision, while V4 Pro wins on coding-specific benchmarks like Codeforces (3206 vs - ) and has a slightly cleaner MIT license.
Use it with
When to use something else
If your priority is pure coding performance, DeepSeek V4 Pro may serve you better. If license cleanliness is critical, GLM-5.1 offers a standard MIT license. If you need local deployment on consumer hardware, neither of these models fits - look at Qwen3.6 27B or Gemma 4 31B instead.
Frequently asked
Quick answers to common questions
How much VRAM does Kimi K2.6 need?
Kimi K2.6 with 1000B parameters needs approximately 2000 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Kimi K2.6 better than other Kimi models?
Kimi K2.6 scores 87.1 on MMLU. It has 1000B parameters with 262,144 context - a strong choice for agentic-coding, multi-agent, vision.
What license is Kimi K2.6 under?
Kimi K2.6 is released under the Modified MIT license, making it suitable for most commercial and personal projects.
What hardware runs Kimi K2.6 well?
With 1000B parameters, Kimi K2.6 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Kimi K2.6?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Step up to Q5_K_M or Q8_0 only if you have spare VRAM. Use our VRAM calculator to compare.
How long can Kimi K2.6's context window handle?
Kimi K2.6 supports a 262,144-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with Kimi K2.6?
Kimi K2.6 competes with other 500B–1500B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
Similar models and recommended hardware
Related models
Recommended hardware
Nearby options
Similar models and compatible hardware by spec
Comments coming soon
Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.