SWE-2 Description

SWE-2 is a software engineering model from Cognition built for agentic coding tasks that require strong performance at lower computational and monetary cost. It is post-trained from the Kimi K3 base model and extends Cognition’s earlier SWE-1.7 training approach with a new reinforcement learning method for jointly optimizing multiple reasoning-effort settings. Medium, high, and maximum effort modes provide different tradeoffs between speed, cost, exploration, and verification depending on task complexity. The model is trained to inspect only the parts of a codebase that are likely to matter, helping it reach implementation faster and reduce unnecessary exploration. SWE-2 can generate and modify code, run tests, analyze repositories, work through terminal tasks, and verify whether implementations satisfy user requirements. Cognition also reports improvements in end-to-end test creation, regression detection, instruction following, and re-deriving conclusions when challenged. Its training process incorporates cost-aware rewards, length-weighted reward baselines, expanded reinforcement learning environments, and hardened verifiers intended to improve both efficiency and reliability. SWE-2 is positioned as a cost-efficient alternative to larger frontier coding models while remaining competitive on software engineering benchmarks such as FrontierCode, DeepSWE, and Terminal-Bench. The model is available in Devin Desktop and Devin CLI and is being introduced to additional Cognition products including Devin Web and Fusion.

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Pricing

Pricing Starts At:
$20/month
Free Version:
Yes

Integrations

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Reviews - 1 Verified Review

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Company Details

Company:
Cognition
Year Founded:
2023
Headquarters:
United States
Website:
cognition.com

Media

SWE-2 Screenshot 1
SWE-2 Screenshot 1 SWE-2 Screenshot 2
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support

SWE-2 Features and Options

SWE-2 User Reviews

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  • Name: Anonymous (Verified)
    Job Title: Software Engineer
    Length of product use: Less than 6 months
    Used How Often?: Daily
    Role: User
    Organization Size: 500 - 999
    Features
    Design
    Ease
    Pricing
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Really good for coding

    Date: Sep 11 2026

    Summary: Overall, SWE-2 feels like a serious upgrade for developers using AI coding agents. It may not be the absolute top model on every metric, but the mix of coding strength, agent performance, and lower cost makes it one of the most practical models to watch right now.

    Positive: 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.

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

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