Kimi K2.7 Code
A solid pick for self-hosting and full data control.
Overview
Kimi K2.7 Code is Moonshot AI's open MoE model (1 trillion total parameters, 32 billion activated per token), built for long-horizon software engineering and agentic workflows with a 256K-token context window. It improves markedly on coding and tool-use benchmarks over K2.6 while cutting reasoning-token usage by roughly 30%, yet it still trails GPT-5.5 and Claude Opus 4.8 on most tests. Its modified-MIT open weights allow on-premise deployment in Europe, provided you have serious server-grade hardware.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you build with a coding agent
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Long-horizon coding agents
- DevOps workflow automation
- Large codebase refactoring
- MCP tool integration
- Sovereign on-premise deployment
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/kimi-k2-7-code)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
5/8 documented · 2/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 570 GB | 629 GB | Server GPU infra (H100/A100…) |
| Q8 · balanced | 1070 GB | 1179 GB | Server GPU infra (H100/A100…) |
| FP16 · max quality | 2000 GB | 2202 GB | Server GPU infra (H100/A100…) |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve moonshotai/Kimi-K2.7-Code \
--max-model-len 32768 \
--tensor-parallel-size 2 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |