SWE-1.7

SWE-1.7

Cognition
SWE-2

SWE-2

Cognition
+
+

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About

SWE-1.7 is Cognition’s frontier software engineering model designed to deliver high intelligence at a lower rollout cost. The model is optimized for long-horizon agentic coding tasks, including debugging, feature implementation, codebase exploration, migrations, terminal workflows, and multilingual software engineering. SWE-1.7 was trained from a Kimi K2.7 base using large-scale reinforcement learning improvements across infrastructure, data quality, training stability, self-compaction, and long-running task execution. It is built to explore codebases thoroughly, probe edge cases, identify hidden requirements, and produce more complete end-to-end solutions. The model is available in Devin across web, desktop, and CLI through Cerebras at very high serving speeds. SWE-1.7 is positioned for developers and engineering teams that need cost-efficient frontier-level coding intelligence for complex real-world software work.

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

Software engineers, AI coding agent builders, engineering teams, DevOps teams, research labs, and companies using Devin that need cost-efficient frontier coding intelligence for debugging, feature development, migrations, terminal tasks, and long-horizon software engineering workflows.

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

$20/month
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

  • SWE-1.7 is really impressive from a developer’s perspective because it feels focused on actual software engineering, not just generic code completion. I like that it is built for agentic coding workflows where the model needs to understand a repo, make changes, chase bugs, and keep context across multiple steps. The biggest selling point is the cost-performance angle. Cognition is positioning SWE-1.7 as frontier-level intelligence at a much lower cost, which matters a lot if you are using coding agents heavily instead of just asking the occasional question. It also helps that Devin’s docs describe SWE-1.7 Lightning as a faster version with the same intelligence and lower latency, because speed becomes a big deal when an agent is editing, searching, testing, and iterating over and over.

Cons

  • It is still new, so I would want to test it hard on real repos before trusting it blindly. Coding benchmarks and launch claims are useful, but the real test is whether it can handle messy architecture, weird dependencies, incomplete docs, flaky tests, and multi-file changes without getting stuck. I also think developers still need to stay involved. SWE-1.7 may be strong, but agentic coding is not “set it and forget it” yet. You still need code review, tests, security checks, and good prompts to make sure the output is actually production-ready.

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

Cognition
Founded: 2023
United States
cognition.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
SWE-2

SWE-2

Cognition
SWE-1.7

SWE-1.7

Cognition
SWE-1.6

SWE-1.6

Cognition

Categories

Categories

Integrations

.NET
C
C++
CSS
Cerebras
Dart
Devin
Devin Desktop
JSON
JavaScript
Lua
PHP
PowerShell
Python
R
Rust
SQL
Solidity
Swift
XML

Integrations

.NET
C
C++
CSS
Cerebras
Dart
Devin
Devin Desktop
JSON
JavaScript
Lua
PHP
PowerShell
Python
R
Rust
SQL
Solidity
Swift
XML
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