Kimi K2.7 Code
- In Input modalities: TextImageVideo Out Output modalities: Text
- Reasoning
- Tool use
- Structured output
- Open weights
Prices
11 offerings · prices as listedkimi-k2.7-code| Conditions/ 1M tokens | Input | Cache read | Output |
|---|---|---|---|
| Standard | $0.95 | $0.19 | $4.00 |
Prices from Moonshot AI platform pricing (opens in a new tab) (checked Nov 8, 2026)
kimi-k2.7-code| Conditions/ 1M tokens | Input | Cache read | Output |
|---|---|---|---|
| Standard | ¥6.50 | ¥1.30 | ¥27.00 |
Prices from Kimi API 开放平台：模型推理价格 (opens in a new tab) (checked Nov 8, 2026)
kimi-k2.7-code| Conditions/ 1M tokens | Input | Cache read | Cache write | Output |
|---|---|---|---|---|
| Standard | $0.8939 | $0.1788 | — | $3.7131 |
| Explicit cache | — | $0.0894 | $1.1174 | — |
Prices from Kimi K2.7 Code model (opens in a new tab) (checked Nov 9, 2026)
kimi-k2.7-code| Conditions/ 1M tokens | Input | Cache read | Output |
|---|---|---|---|
| Standard | $0.95 | $0.19 | $4.00 |
Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Nov 9, 2026) · Kimi K2.7 Code model (opens in a new tab) (checked Nov 9, 2026)
kimi-k2.7-code| Conditions/ 1M tokens | Input | Output |
|---|---|---|
| Standard | ¥6.50 | ¥27.00 |
Prices from 阿里云百炼：模型价格 (opens in a new tab) (checked Nov 9, 2026)
kimi/kimi-k2.7-code| Conditions/ 1M tokens | Input | Output |
|---|---|---|
| Standard | ¥6.50 | ¥27.00 |
Prices from 阿里云百炼：模型价格 (opens in a new tab) (checked Nov 9, 2026)
kimi-k2.7-code| Conditions/ 1M tokens | Input | Output |
|---|---|---|
| Standard no longer listed since Oct 23, 2026 | $0.894 | $3.713 |
Prices from Alibaba Cloud Model Studio pricing (opens in a new tab) (checked Nov 8, 2026)
Standard: the price a request with no conditions pays (the headline price elsewhere on the site).
Details
- Reasoning can be off
- —
- Knowledge cutoff
- Jan 2025
- Licence
- Modified MIT License
- Family
- kimi-k-code
- Model ID
Not stated on Moonshot AI’s pages: max input, reasoning efforts, max thinking budget, built-in tools, aliases.
Run locally
Weights: moonshotai/Kimi-K2.7-Code- Parameters 1.03T
- Active 32B · 384 experts, 8 per token
- Native precision BF16
- Layers 61
- KV cache per token 69 KB
- Config positions 256K (config.json max_position_embeddings)
- API context 256K (maker’s API)
- Needs
- ≈ 534.0 GB (weights 508.1 + KV cache 0.5 + overhead 25.4)
- Usable
- 21.6 GB (90% of its memory)
- Try
- it’s too large for this hardware; see the smallest that runs it below
| Precision | Weights | Needs at 8K | On your hardware | Longest context |
|---|---|---|---|---|
| BF16 (native)maker’s file | 554.3 GB | ≈ 582.6 | ✕Doesn’t fit | — |
| Q8_0 | 1016.1 GB | ≈ 1067.5 | ✕Doesn’t fit | — |
| FP8 | 956.4 GB | ≈ 1004.7 | ✕Doesn’t fit | — |
| Q6_K | 785.4 GB | ≈ 825.2 | ✕Doesn’t fit | — |
| Q5_K_M | 680.2 GB | ≈ 714.8 | ✕Doesn’t fit | — |
| Q5_0 | 657.5 GB | ≈ 690.9 | ✕Doesn’t fit | — |
| Q4_K_M | 581.0 GB | ≈ 610.6 | ✕Doesn’t fit | — |
| Q4_0 | 538.0 GB | ≈ 565.4 | ✕Doesn’t fit | — |
| IQ4_XS | 508.1 GB | ≈ 534.0 | ✕Doesn’t fit | — |
| Q3_K_Mquality drops | 467.4 GB | ≈ 491.3 | ✕Doesn’t fit | — |
| IQ3_XXSquality drops | 365.8 GB | ≈ 384.6 | ✕Doesn’t fit | — |
| Q2_Kquality drops | 358.6 GB | ≈ 377.1 | ✕Doesn’t fit | — |
| IQ2_XXSquality drops | 246.3 GB | ≈ 259.1 | ✕Doesn’t fit | — |
Click a row to choose a precision. “Maker’s file” sizes are the maker’s own files; others are parameters × bits per weight.
Compared with other hardware
Each at the highest precision it runs. Click one for details.
Smallest that runs it (Q4_K_M, 8K): no single listed machine; about 9 × 80 GB GPUs.
Run commands
The maker publishes no files at this precision. Choose its native precision or one of its own builds, or quantise it yourself with llama.cpp.
Memory to fine-tune
Batch 1, 2,048-token sequences, gradient checkpointing; LoRA and QLoRA train rank-16 adapters on the attention projections.
Estimates; facts from the maker’s Hugging Face repositories, as of 2026-11-11. How it’s estimated
Cited scores
From third-party leaderboards, not measured by us- Epoch Capabilities Index 151.2
#55 of 118 listed models · CI 149.1–153.3
- Epoch AI · Chess Puzzles 14%
#72 of 85 listed models · ±3.49
- Epoch AI · FrontierMath Tier 4 v2 12.2%
#53 of 65 listed models · ±5.17
- Epoch AI · FrontierMath Tiers 1–3 54%
#49 of 84 listed models · ±2.96
- Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · License · as of Oct 28 – Nov 5, 2026
- LiveBench (White et al.), release 2026-06-25, livebench.ai, licensed CC BY-SA 4.0. This board, adapted from it, is shared under CC BY-SA 4.0. · License · as of Jun 25, 2026
- LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · License · as of Nov 8, 2026
- changes we made
Provider-reported scores
Published by Moonshot AI, under the conditions it states| Benchmark | Setting | Score |
|---|---|---|
| MCP-Atlas | official config, 100 tool-call budget, 32k max tokens per step, avg of 3 runs, thinking | 76% |
News
All news →- Oct 10, 2026Pricing Alibaba Qwen changes Kimi K2.7 Code prices Kimi K2.7 Code on Alibaba Cloud Model Studio (USD): input $0.894 → $0.8939, output $3.713 → $3.7131 per 1M tokens.
Changes
All changes →- Available → Deprecated
- Now priced on Alibaba Cloud Model Studio, China Input ¥6.50 · Output ¥27.00
- Now priced on Alibaba Cloud Model Studio, China Input ¥6.50 · Output ¥27.00
- Now priced on Cloudflare Workers AI Input $0.95 · Cache read $0.19 · Output $4.00
- Output price raised 0% $3.713 → $3.7131 / 1M tokens · Alibaba Cloud Model Studio
- Input price cut 0% $0.894 → $0.8939 / 1M tokens · Alibaba Cloud Model Studio
- 1 new price on Alibaba Cloud Model Studio, International Cache read $0.19
Sources
- moonshotai/Kimi-K2.7-Code model card (opens in a new tab) huggingface.co · checked Nov 6, 2026
- Moonshot AI platform pricing (opens in a new tab) platform.moonshot.ai · checked Nov 8, 2026
- Kimi API 开放平台：模型推理价格in Chinese (opens in a new tab) platform.moonshot.cn · checked Nov 8, 2026
- 硅基流动：模型价格in Chinese (opens in a new tab) siliconflow.cn · checked Nov 8, 2026
- Kimi API models overview (opens in a new tab) platform.kimi.ai · checked Nov 8, 2026
- Kimi API structured output (opens in a new tab) platform.kimi.ai · checked Nov 8, 2026
- Kimi API responses reference (opens in a new tab) platform.kimi.ai · checked Nov 8, 2026
- Kimi platform changelog (opens in a new tab) platform.kimi.ai · checked Nov 9, 2026
- models.dev: kimi-k2.7-code (community data, MIT) (opens in a new tab) github.com · checked Sep 30, 2026
- Kimi K2.7 Code - Kimi API Platform (opens in a new tab) platform.kimi.ai · checked Nov 6, 2026
- Kimi K2.7 Code model (opens in a new tab) alibabacloud.com · checked Nov 9, 2026
- Explore Qwen3, Wan2.6 & Latest Models on Model Studio - Alibaba Cloud Model Studio - Alibaba Cloud_Alibaba Cloud Model Studio(Model Studio) - Alibaba Cloud Model Studio - Alibaba Cloud Documentation Center (opens in a new tab) alibabacloud.com · checked Nov 11, 2026
- Cloudflare Workers AI pricing (opens in a new tab) developers.cloudflare.com · checked Nov 7, 2026
- 阿里云百炼：模型价格in Chinese (opens in a new tab) help.aliyun.com · checked Nov 9, 2026
- 模型上下架与更新-大模型服务平台百炼-阿里云_大模型服务平台百炼(Model Studio)-大模型服务平台百炼(Model Studio)-阿里云帮助中心in Chinese (opens in a new tab) help.aliyun.com · checked Nov 4, 2026
- moonshotai/Kimi-K2.7-Code model card (opens in a new tab) huggingface.co · checked Nov 5, 2026
- moonshotai/Kimi-K2.7-Code config.json (opens in a new tab) huggingface.co · checked Nov 6, 2026
- moonshotai/Kimi-K2.7-Code files (opens in a new tab) huggingface.co · checked Nov 6, 2026
- moonshotai/Kimi-K2.7-Code README.md (opens in a new tab) huggingface.co · checked Nov 4, 2026
- Alibaba Cloud Model Studio pricing (opens in a new tab) alibabacloud.com · checked Nov 8, 2026
- Text generation models - Qwen, DeepSeek, GLM | Model Studio - Alibaba Cloud Model Studio - Alibaba Cloud Documentation Center (opens in a new tab) docs.modelstudio.console.alibabacloud.com · checked Nov 1, 2026
- Alibaba Cloud Model Studio pricing (opens in a new tab) alibabacloud.com · checked Nov 9, 2026
Moonshot AI’s official policies: data use, privacy, terms →
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