Moonshot AI 开源 Kimi K2.7-Code：编程模型在 Kimi Code Bench v2 上较 K2.6 提升 21.8%
2026-06-13 12:57·19天前·Asif Razzaq
AI 摘要
Moonshot AI 以 Modified MIT 许可证开源了 Kimi K2.7-Code，一款专注编程的智能体模型，基于 Kimi K2.6，拥有 256K 上下文窗口，推理 token 使用量降低约 30%。在六项基准测试中均优于 K2.6，其中在 Kimi Code Bench v2 上提升 21.8%。该模型可通过 Kimi API 和 Kimi Code 获取。
原文 · 未翻译
This week, Moonshot AI released Kimi K2.7-Code. It is a coding-focused, agentic model. The model weights ship on Hugging Face under a Modified MIT license. You can also reach it through the Kimi API and Kimi Code.
K2.7-Code targets long-horizon software engineering, not general chat. It plans, edits, runs tools, and debugs across many steps. Moonshot pairs the model with a subscription coding platform around it.
Kimi K2.7-Code
K2.7-Code is a Mixture-of-Experts model. It holds 1T total parameters and activates 32B per token. The design uses 384 experts, with 8 selected per token and 1 shared. It has 61 layers, including 1 dense layer.
Attention uses MLA, and the feed-forward path uses SwiGLU. A MoonViT vision encoder adds 400M parameters for image and video input. The model ships with native INT4 quantization. The context window is 256K tokens (262,144).
Two constraints matters: Thinking mode is mandatory; disabling it returns an API error. Sampling is fixed: temperature 1.0, top_p 0.95, n 1, penalties 0.0. Default max output is 32,768 tokens.
You can self-host with vLLM, SGLang, or KTransformers. The Hugging Face repository is large, roughly 595 GB on disk. This is a server-class deployment target, not a laptop model.
Benchmark
Moonshot team published six benchmark rows. They compare K2.7-Code against K2.6, GPT-5.5, and Claude Opus 4.8. K2.7-Code beats K2.6 on every row. The largest coding jump is Kimi Code Bench v2, from 50.9 to 62.0.
BenchmarkKimi K2.6Kimi K2.7-CodeGPT-5.5Claude Opus 4.8K2.7 vs K2.6
Moonshot AI 以 Modified MIT 许可证开源了 Kimi K2.7-Code，一款专注编程的智能体模型，基于 Kimi K2.6，拥有 256K 上下文窗口，推理 token 使用量降低约 30%。在六项基准测试中均优于 K2.6，其中在 Kimi Code Bench v2 上提升 21.8%。该模型可通过 Kimi API 和 Kimi Code 获取。
原文 · 保持原样，未翻译
This week, Moonshot AI released Kimi K2.7-Code. It is a coding-focused, agentic model. The model weights ship on Hugging Face under a Modified MIT license. You can also reach it through the Kimi API and Kimi Code.
K2.7-Code targets long-horizon software engineering, not general chat. It plans, edits, runs tools, and debugs across many steps. Moonshot pairs the model with a subscription coding platform around it.
Kimi K2.7-Code
K2.7-Code is a Mixture-of-Experts model. It holds 1T total parameters and activates 32B per token. The design uses 384 experts, with 8 selected per token and 1 shared. It has 61 layers, including 1 dense layer.
MCP Mark Verified72.881.192.976.4+11.4%
K2.7-Code does beat Opus 4.8 on MCP Mark Verified, 81.1 versus 76.4. It also lands close to GPT-5.5 on MLS Bench Lite. K2.7-Code ran in Kimi Code CLI, GPT-5.5 in Codex xhigh, and Opus 4.8 in Claude Code xhigh.
Reasoning-Token Efficiency: A Cost Claim, Not Just Quality
Moonshot team reports about 30% lower reasoning-token usage than K2.6. It frames this as ‘less overthinking.’
Reasoning tokens bill as output tokens on most price cards. Agentic coding runs hundreds or thousands of steps. Each plan, retry, and verification pays the thinking cost again. A 30% cut compounds across a long run.
The effect lands in three places at once. First, lower output-token cost per task. Second, faster steps, which helps interactive CLI sessions. Third, more steps before hitting context limits.
Use Cases With Examples
Repo-scale refactors are the main use case. Point the agent at a failing test suite. It reads files, edits across modules, then reruns tests until green.
Code review is a second fit. Feed a pull request diff and ask for risk analysis. The 256K window holds large diffs, logs, and related files together.
MCP tool-use workflows are a third fit. K2.7-Code scored 81.1 on MCP Mark Verified. That suite tests correct tool invocation through the Model Context Protocol. Think CI checks, ticket updates, and file edits in one loop.
Long-context analysis is a fourth fit. The model accepts text, image, and video input. Documentation, screenshots, and a recorded repro can share one prompt.
Marktechpost’s Interactive Explorer
Kimi K2.7-Code — Interactive Explorer
Company-reported benchmarks and official API pricing. Released June 12, 2026. Verified June 12, 2026.
Benchmarks
Cost Calculator
Specs
Source: Moonshot AI Kimi K2.7-Code model card. K2.7-Code ran in Kimi Code CLI; GPT-5.5 in Codex xhigh; Claude Opus 4.8 in Claude Code xhigh. First-party numbers, not an independent leaderboard.
Input tokens / run: 50,000
Output tokens / run: 8,000
Cache hit rate: 50%
Runs / month: 1,000
Reasoning share of output: 40%
Input cost$0.00
Output cost$0.00
Est. monthly total$0.00
$0.00
Rates: cached input $0.19 / 1M, cache-miss input $0.95 / 1M, output $4.00 / 1M (official Kimi pricing). Savings line illustrates K2.7-Code’s reported ~30% lower reasoning-token usage vs K2.6, applied to the reasoning share of output. Estimate only.
Source: Kimi K2.7-Code Hugging Face model card and Kimi API docs.
A Minimal Quickstart
The Kimi API is OpenAI-compatible. The model string is kimi-k2.7-code. Do not override the fixed sampling parameters, or the request errors.
Copy CodeCopiedUse a different Browser
import os from openai import OpenAI
Base URL and key per the Kimi API docs at platform.moonshot.ai client = OpenAI( api_key=os.environ.get("MOONSHOT_API_KEY"), base_url="https://api.moonshot.ai/v1", )
messages = [ {"role": "system", "content": "You are a coding agent."}, {"role": "user", "content": "Refactor utils.py to remove duplicate code."}, ]
resp = client.chat.completions.create( model="kimi-k2.7-code", messages=messages, max_tokens=32768, # default cap; also the maximum # thinking is enabled by default and cannot be disabled. # temperature (1.0), top_p (0.95), n (1), and penalties (0.0) are # fixed server-side. Passing any other value returns an error. )
msg = resp.choices[0].message print(msg.content)
Multi-step tool calls: append the full assistant message so that # reasoning_content is preserved. Dropping it errors on the next turn. # messages.append(msg.model_dump())
Two tool-use rules come from the docs. Keep reasoning_content from the current turn in context. And set tool_choice to only "auto" or "none".
How K2.7-Code Compares
ModelLicenseParamsContextAPI price (in / out per 1M)
Kimi K2.7-CodeModified MIT (open)1T total / 32B active256K$0.95 / $4.00
Kimi K2.6Open-weight1T-class MoE256K~$0.67–0.95 / ~$3.39–4.00
GPT-5.5ClosedNot disclosed—Not in Moonshot table
Claude Opus 4.8ClosedNot disclosed1M$5.00 / $25.00
Qwen3-Coder-480B-A35BOpen (Qwen license)480B / 35B active256K nativeVaries by host
K2.7-Code lists $0.19 per 1M for cached input.
Strengths and Weaknesses
Strengths:
Open weights under Modified MIT, with a real self-host path.
Broad, consistent gains over K2.6 on coding and agent evals.
Low API pricing relative to closed frontier models.
Beats Opus 4.8 on the MCP Mark Verified benchmark (company-reported).
Weaknesses:
All headline numbers are first-party at launch.
Thinking mode cannot be disabled.
Sampling controls are locked to fixed values.
Multi-step tool calls must preserve reasoning_content.
595 GB weights make self-hosting a serious commitment.
Key Takeaways
All headline benchmarks are vendor-run; independent results are pending.
K2.7-Code is open-weight, coding-specialized, and built on Kimi K2.6.
Moonshot reports +21.8% on Kimi Code Bench v2 over K2.6.
The model uses roughly 30% fewer reasoning tokens than K2.6.
Check out the Kimi Code and API. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
The post Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6 appeared first on MarkTechPost.
Attention uses MLA, and the feed-forward path uses SwiGLU. A MoonViT vision encoder adds 400M parameters for image and video input. The model ships with native INT4 quantization. The context window is 256K tokens (262,144).
Two constraints matters: Thinking mode is mandatory; disabling it returns an API error. Sampling is fixed: temperature 1.0, top_p 0.95, n 1, penalties 0.0. Default max output is 32,768 tokens.
You can self-host with vLLM, SGLang, or KTransformers. The Hugging Face repository is large, roughly 595 GB on disk. This is a server-class deployment target, not a laptop model.
Benchmark
Moonshot team published six benchmark rows. They compare K2.7-Code against K2.6, GPT-5.5, and Claude Opus 4.8. K2.7-Code beats K2.6 on every row. The largest coding jump is Kimi Code Bench v2, from 50.9 to 62.0.
BenchmarkKimi K2.6Kimi K2.7-CodeGPT-5.5Claude Opus 4.8K2.7 vs K2.6
Kimi Code Bench v250.962.069.067.4+21.8%
Program Bench48.353.669.163.8+11.0%
MLS Bench Lite26.735.135.542.8+31.5%
Kimi Claw 24/7 Bench42.946.952.850.4+9.3%
MCP Atlas69.476.079.481.3+9.5%
MCP Mark Verified72.881.192.976.4+11.4%
K2.7-Code does beat Opus 4.8 on MCP Mark Verified, 81.1 versus 76.4. It also lands close to GPT-5.5 on MLS Bench Lite. K2.7-Code ran in Kimi Code CLI, GPT-5.5 in Codex xhigh, and Opus 4.8 in Claude Code xhigh.
Reasoning-Token Efficiency: A Cost Claim, Not Just Quality
Moonshot team reports about 30% lower reasoning-token usage than K2.6. It frames this as ‘less overthinking.’
Reasoning tokens bill as output tokens on most price cards. Agentic coding runs hundreds or thousands of steps. Each plan, retry, and verification pays the thinking cost again. A 30% cut compounds across a long run.
The effect lands in three places at once. First, lower output-token cost per task. Second, faster steps, which helps interactive CLI sessions. Third, more steps before hitting context limits.
Use Cases With Examples
Repo-scale refactors are the main use case. Point the agent at a failing test suite. It reads files, edits across modules, then reruns tests until green.
Code review is a second fit. Feed a pull request diff and ask for risk analysis. The 256K window holds large diffs, logs, and related files together.
MCP tool-use workflows are a third fit. K2.7-Code scored 81.1 on MCP Mark Verified. That suite tests correct tool invocation through the Model Context Protocol. Think CI checks, ticket updates, and file edits in one loop.
Long-context analysis is a fourth fit. The model accepts text, image, and video input. Documentation, screenshots, and a recorded repro can share one prompt.
Marktechpost’s Interactive Explorer
Kimi K2.7-Code — Interactive Explorer
Company-reported benchmarks and official API pricing. Released June 12, 2026. Verified June 12, 2026.
Benchmarks
Cost Calculator
Specs
Source: Moonshot AI Kimi K2.7-Code model card. K2.7-Code ran in Kimi Code CLI; GPT-5.5 in Codex xhigh; Claude Opus 4.8 in Claude Code xhigh. First-party numbers, not an independent leaderboard.
Input tokens / run: 50,000
Output tokens / run: 8,000
Cache hit rate: 50%
Runs / month: 1,000
Reasoning share of output: 40%
Input cost$0.00
Output cost$0.00
Est. monthly total$0.00
$0.00
Rates: cached input $0.19 / 1M, cache-miss input $0.95 / 1M, output $4.00 / 1M (official Kimi pricing). Savings line illustrates K2.7-Code’s reported ~30% lower reasoning-token usage vs K2.6, applied to the reasoning share of output. Estimate only.
Source: Kimi K2.7-Code Hugging Face model card and Kimi API docs.
A Minimal Quickstart
The Kimi API is OpenAI-compatible. The model string is kimi-k2.7-code. Do not override the fixed sampling parameters, or the request errors.
Copy CodeCopiedUse a different Browser
import os from openai import OpenAI
Base URL and key per the Kimi API docs at platform.moonshot.ai client = OpenAI( api_key=os.environ.get("MOONSHOT_API_KEY"), base_url="https://api.moonshot.ai/v1", )
messages = [ {"role": "system", "content": "You are a coding agent."}, {"role": "user", "content": "Refactor utils.py to remove duplicate code."}, ]
resp = client.chat.completions.create( model="kimi-k2.7-code", messages=messages, max_tokens=32768, # default cap; also the maximum # thinking is enabled by default and cannot be disabled. # temperature (1.0), top_p (0.95), n (1), and penalties (0.0) are # fixed server-side. Passing any other value returns an error. )
msg = resp.choices[0].message print(msg.content)
Multi-step tool calls: append the full assistant message so that # reasoning_content is preserved. Dropping it errors on the next turn. # messages.append(msg.model_dump())
Two tool-use rules come from the docs. Keep reasoning_content from the current turn in context. And set tool_choice to only "auto" or "none".
How K2.7-Code Compares
ModelLicenseParamsContextAPI price (in / out per 1M)
Kimi K2.7-CodeModified MIT (open)1T total / 32B active256K$0.95 / $4.00
Kimi K2.6Open-weight1T-class MoE256K~$0.67–0.95 / ~$3.39–4.00
GPT-5.5ClosedNot disclosed—Not in Moonshot table
Claude Opus 4.8ClosedNot disclosed1M$5.00 / $25.00
Qwen3-Coder-480B-A35BOpen (Qwen license)480B / 35B active256K nativeVaries by host
K2.7-Code lists $0.19 per 1M for cached input.
Strengths and Weaknesses
Strengths:
Open weights under Modified MIT, with a real self-host path.
Broad, consistent gains over K2.6 on coding and agent evals.
Low API pricing relative to closed frontier models.
Beats Opus 4.8 on the MCP Mark Verified benchmark (company-reported).
Weaknesses:
All headline numbers are first-party at launch.
Thinking mode cannot be disabled.
Sampling controls are locked to fixed values.
Multi-step tool calls must preserve reasoning_content.
595 GB weights make self-hosting a serious commitment.
Key Takeaways
All headline benchmarks are vendor-run; independent results are pending.
K2.7-Code is open-weight, coding-specialized, and built on Kimi K2.6.
Moonshot reports +21.8% on Kimi Code Bench v2 over K2.6.
The model uses roughly 30% fewer reasoning tokens than K2.6.
Check out the Kimi Code and API. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
The post Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6 appeared first on MarkTechPost.