Moonshot AI 推出的 Kimi K2.7 Code 是一款面向本地私有化部署与代码生成的开源权重编程大模型，具备 262K 上下文窗口、75 Token/秒生成速度与 89/100 综合智能指数（免费开源权重）。
Technical Specifications
- Provider
- Moonshot AI
- Category
- open-source
- Release Date
- 2026-06
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- kimi-k2-7-code
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 930 GB VRAM (4-bit Quantized) · 3225 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Kimi K2.7 Code Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Kimi K2.7 Code:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 62%
- GPQA Diamond
- 73%
- MMLU-Pro
- 85%
- HumanEval
- 95.5%
- LiveCodeBench
- 74%
- Chatbot Arena Elo
- 1415pts
Strengths
- 提供 262K 上下文窗口与最高 32K 输出容量，核心基准表现优异（SWE-bench Verified 62% · GPQA Diamond 73%）
- 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 480 毫秒
- 开放模型权重 (1.5T total (40B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Limitations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Editorial Assessment
Kimi K2.7 Code 凭借 89/100 的智能得分、262K 上下文容量与 75 Token/秒的响应速度，在 Moonshot AI 模型矩阵中提供了极佳的性能与成本平衡。
Best for: 面向本地私有化部署与代码生成的开源权重编程任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。
Compare Kimi K2.7 Code
Frequently Asked Questions about Kimi K2.7 Code
How much does the Kimi K2.7 Code API cost per 1M tokens?+
Kimi K2.7 Code by Moonshot AI is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Kimi K2.7 Code pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Kimi K2.7 Code's context window?+
Kimi K2.7 Code features a 262K tokens context window, which equals approximately 196,608 English words or ~524 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 32,000 tokens.
Can I run Kimi K2.7 Code locally on my own GPU (Open Weights)?+
Yes, Kimi K2.7 Code (1.5T total (40B active MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Moonshot AI train on data sent to the Kimi K2.7 Code API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Kimi K2.7 Code perform on coding and reasoning benchmarks?+
Kimi K2.7 Code achieves a composite Intelligence Index of 89/100, scoring 62% on SWE-bench Verified, 73% on GPQA Diamond, 85% on MMLU-Pro, and 1415 pts on Chatbot Arena Elo.
What is Kimi K2.7 Code best used for?+
Kimi K2.7 Code is best suited for: 面向本地私有化部署与代码生成的开源权重编程任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。. Assessment: Kimi K2.7 Code 凭借 89/100 的智能得分、262K 上下文容量与 75 Token/秒的响应速度，在 Moonshot AI 模型矩阵中提供了极佳的性能与成本平衡。
Moonshot AI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Moonshot AI's flagship reasoning tier:
Kimi K3
Kimi K2.6
Kimi K2.5
Kimi K2 Thinking
Kimi K2 Thinking Turbo
Kimi K2
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Kimi K2.7 Code:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.