Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
SWE-2

SWE-2

Cognition
+
+

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About

Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.

About

SWE-2 is Cognition’s advanced coding model designed to improve software engineering performance while reducing the cost of agentic coding workflows. The model is post-trained from Kimi K3 and uses reinforcement learning to optimize multiple reasoning-effort levels within a single training run. SWE-2 is designed to explore codebases more selectively, begin implementation sooner, and complete tasks with fewer redundant reads and reasoning steps than earlier Cognition models. Its capabilities include code generation, debugging, test creation, verification, repository analysis, and complex terminal-based software engineering tasks. The model also emphasizes stronger engineering judgment, end-to-end test coverage, instruction following, and evidence-based verification of user assumptions. SWE-2 is available through Devin Desktop and Devin CLI, with broader rollout planned across Devin Web and Fusion.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Kimi K2.7 Code is best suited for software developers, engineering teams, AI coding tool builders, open-source model users, DevOps teams, and organizations that need long-context code generation, debugging, repository analysis, and agentic software engineering support

Audience

Software developers, engineering teams, AI coding agent users, DevOps professionals, and organizations that need capable agentic software engineering with lower execution cost and more efficient reasoning

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

Pricing

$20/month
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5

Pros & Cons from Real Users

Pros

  • Kimi K2.7 Code has been a great model for coding and AI agent development. It handles technical prompts well, understands developer workflows, and gives clear, useful responses for real coding tasks. I use it for debugging, writing scripts, improving code structure, and planning agent workflows. It is especially helpful when I need clean reasoning, practical suggestions, and code that is easy to adapt. For AI agents, Kimi K2.7 Code feels reliable and capable. It does a strong job following instructions, working through multi-step tasks, and producing structured outputs that fit automation and agent-based use cases.

Cons

  • It still works best when the prompt includes enough context. For very complex projects or large codebases, I sometimes need to provide extra details to get the best results. I also prefer to review important code before using it, especially for production work, since small mistakes can still happen.

Pros & Cons from Real Users

Pros

  • The biggest thing that stands out is the cost-performance balance. SWE-2 is not just trying to top one benchmark; it is trying to get very close to frontier coding performance at a much lower cost. For developers, that matters a lot. Coding agents can burn through tokens quickly when they are reading files, making edits, running tests, and iterating. A model that performs near the top while being meaningfully cheaper is much easier to use every day. I also like that SWE-2 seems built for real software engineering workflows, not just isolated code snippets. The strong DeepSWE and Terminal-Bench results make it especially interesting for repo-level tasks, debugging, tool use, and longer agent runs.

Cons

  • Benchmarks are useful, but real projects bring messy architecture, flaky tests, undocumented behavior, and weird edge cases.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Moonshot AI
Founded: 2023
China
www.kimi.com

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
Kimi K3

Kimi K3

Moonshot AI
SWE-1.7

SWE-1.7

Cognition
Kimi K2.5

Kimi K2.5

Moonshot AI
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

.NET
C
Cerebras
Dart
Go
HTML
JavaScript
Kotlin
Kubernetes
Lua
Objective-C
PHP
PowerShell
Python
R
Ruby
Rust
Scala
Solidity
XML

Integrations

.NET
C
Cerebras
Dart
Go
HTML
JavaScript
Kotlin
Kubernetes
Lua
Objective-C
PHP
PowerShell
Python
R
Ruby
Rust
Scala
Solidity
XML
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