SWE-2

SWE-2

Cognition
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About

Muse Wonder Full is an advanced artificial intelligence foundation model developed by Meta for complex reasoning, autonomous agent workflows, software engineering, and professional knowledge work. As part of the Muse Wonder model family, it is designed to handle demanding tasks that require sustained reasoning, contextual understanding, and multistep execution. The model incorporates Active Memory to retain important information across conversations and retrieve relevant context when needed. Its Active Focus capabilities support long-running assignments by exploring alternative approaches, resolving obstacles, and adapting to changing requirements. Muse Wonder Full also supports software development, legal and financial analysis, business workflow automation, and agentic interactions with external tools. The model is available to Muse Code subscribers and has been evaluated on benchmarks covering coding, memory, professional tasks, and general problem-solving capabilities.

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 Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

AI developers, software engineers, researchers, technical professionals, enterprise development teams, and knowledge workers requiring advanced reasoning, long-running agent workflows, complex software development, and contextual assistance with professional tasks

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 Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$50/month
Free Version Not Supported
Free Trial Not Supported

Pricing

$20/month
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 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

  • I can give it a big coding or research task and it does a better job staying focused, recovering when something goes wrong, and actually finishing the job instead of stopping halfway through.

Cons

  • The downside is access. Right now Wonder Full is limited to Muse Code subscribers rather than being broadly available through the Meta Model API, so it is harder to plug directly into custom apps and agent stacks.

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 Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Meta
Founded: 2004
United States
meta.ai/

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

SWE-1.7

SWE-1.7

Cognition
SWE-1.6

SWE-1.6

Cognition

Categories

AI Coding Models Supported
AI Models Supported
Foundation Models Supported
Multimodal Models Supported

Categories

AI Coding Models Supported
AI Models Supported

Integrations

.NET Supported
C# Supported
CSS Supported
Go Supported
HTML Supported
JavaScript Supported
Kotlin Supported
Objective-C Supported
PHP Supported
PowerShell Supported
Python Supported
R Supported
Ruby Supported
Rust Supported
SQL Supported
Scala Supported
Swift Supported
TypeScript Supported
XML Supported
YAML Supported

Integrations

.NET Supported
C# Supported
CSS Supported
Go Supported
HTML Supported
JavaScript Supported
Kotlin Supported
Objective-C Supported
PHP Supported
PowerShell Supported
Python Supported
R Supported
Ruby Supported
Rust Supported
SQL Supported
Scala Supported
Swift Supported
TypeScript Supported
XML Supported
YAML Supported
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Claim SWE-2 and update features and information