Kimi K2.7 Code
Kimi K2.7 Code is a 1T parameter MoE model from Moonshot AI. It features a 262k context window and 30% more efficient reasoning for software engineering.
moonshotKimiJune 12, 2026About Kimi K2.7 Code
Learn about Kimi K2.7 Code's capabilities, features, and how it can help you achieve better results.
Trillion Parameter Mixture of Experts
Kimi K2.7 Code is the latest iteration of Moonshot AI's trillion parameter Mixture of Experts (MoE) model. It is optimized for software engineering and agentic automation. The model activates 32 billion parameters per inference step, which balances high intelligence with operational speed. It introduces a refined reasoning mechanism that uses 30 percent fewer tokens for thinking compared to previous versions. Technical problem solving is faster and more cost-effective for multi-turn conversations.
Native Multimodality and Visual Context
This model is natively multimodal and processes text, image, and video inputs. Its 262,144 token context window handles large codebases and complex stack traces. By releasing the model as open weights, Moonshot AI provides an alternative to proprietary frontier models for developers building autonomous AI agents. It maintains consistency across long-horizon coding tasks and translates visual designs into functional code without needing intermediate text descriptions.
Use Cases
Discover the different ways you can use Kimi K2.7 Code to achieve great results.
Autonomous Agentic Coding
Powering multi-step agents that navigate complex file structures and execute multi-file refactors via terminal access.
Visual-to-Code Translation
Converting complex UI designs or architecture diagrams directly into functional front-end or systems code.
Long-Horizon Debugging
Analyzing entire project histories and stack traces within the 262k context window to identify architectural bugs.
3D Scene Synthesis
Generating high-fidelity interactive 3D environments using Three.js or C++ from natural language descriptions.
Video-Based Quality Assurance
Analyzing recorded screen sessions or video demos to identify visual bugs and inconsistent UI transitions.
Legacy Modernization
Automating the migration of aging codebases to modern frameworks by maintaining a consistent chain of thought.
Strengths
Limitations
API Quick Start
moonshot/kimi-k2.7-code
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: process.env.KIMI_API_KEY,
baseURL: 'https://api.moonshot.cn/v1',
});
const response = await client.chat.completions.create({
model: 'kimi-k2.7-code',
messages: [{ role: 'user', content: 'Generate a 3D WebGL pendulum sim.' }],
stream: true,
extra_body: { preserve_thinking: true }
});
for await (const chunk of response) {
process.stdout.write(chunk.choices[0]?.delta?.content || '');
}Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about Kimi K2.7 Code
“Kimi 2.7 ranked 2nd after Fable 5 and before GPT-5 xhigh... Kimi 2.7 is amazingly good.”
“Kimi K2.7 Code just made Kimi K2.6 painfully outdated... it gave the most realistic rendering of water waves!”
“It is the #1 open weight model on SWE-bench (78.2%) and Terminal-Bench 2.1.”
“Kimi-K2.7-Code is now released and open-sourced! Improved coding & agent performance over K2.6.”
“It handled 50 legal PDFs in one go without breaking a sweat.”
“The price is down from $20/month to $1.5/month with the API. Decent UX.”
Related Videos
Watch tutorials, reviews, and discussions about Kimi K2.7 Code
“It started thinking much more and much longer.”
“2.7 delivered better results, faster, but a little bit more expensive in terms of total tokens used.”
“It went into deeper thinking into longer project implementation until actually succeeding.”
“It doesn't just output code, it plans the architecture first in its thinking tokens.”
“The logic in the Python script was flawless compared to the previous 2.6 version.”
“It has improved token efficiency over Kimi K2.6, reducing thinking token usage by approximately 30%.”
“The reasoning process is much more direct while keeping the high success rate of the model.”
“The gap between the two is not insane when you consider that this model is 12.5 times cheaper than Claude Fable.”
“This model is 12.5 times cheaper than Claude Fable at the current API pricing.”
“Performance on SWE-bench Verified is top-tier for an open-weight release.”
“The 256k context window is incredibly stable for multi-file project generation.”
“It handled the C++ logic without needing external library documentation.”
“The reasoning process is much more linear now without redundant loops.”
“It built the entire project structure in 15 minutes including the backend components.”
“It is the best open-weight model for coding tasks available right now on the market.”
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Pro Tips
Expert tips to help you get the most out of Kimi K2.7 Code and achieve better results.
Preserve Thinking Mode
Always enable preserve_thinking in your API calls to ensure the model uses its optimized reasoning chain for logic.
Multimodal Prompting
Provide screenshots of current bugs or UI mockups alongside text instructions to improve the success rate of code generation.
Manage Context Budget
Keep performance critical instructions at the beginning or end of the prompt for the most reliable instruction following.
CLI Integration
Use the official Kimi Code CLI for local development to use the model's native ability to interact with local environments.
Testimonials
What Our Users Say
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CEO, qannas.pro
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Ben Bressington
CTO, AiChatSolutions
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Head of Growth, ScaleUp Labs
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Founder, DataDriven.io
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Frequently Asked Questions
Find answers to common questions about Kimi K2.7 Code