SWE-1.7

SWE-1.7

Cognition
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About

Factory Router is an automatic model-selection system for autonomous software engineering workflows, designed to deliver frontier performance at lower cost and with higher reliability. Instead of expecting engineers to manually choose the best model for every task, Factory Router automatically selects the right model for each Droid session, drawing from a diverse pool of frontier and efficient models. Simple questions, mechanical refactors, documentation updates, small bug fixes, search-heavy investigations, and other routine work can be handled by efficient models, while harder work that genuinely needs deeper reasoning can stay on frontier models. If the selected model struggles to complete a task, Factory Router can move the session to a more capable model to reliably preserve high-quality outcomes. It also routes across models, providers, and capacity sources when endpoints degrade, rate limits hit, or capacity becomes constrained, helping Droid sessions keep working.

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.

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

Enterprise engineering teams using AI coding agents that need automatic model routing to balance performance, reliability, governance, and AI infrastructure cost across software development workflows

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.

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
Free Version
Free Trial

Pricing

$20/month
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

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

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Factory Router
Founded: 2023
United States
factory.ai/news/factory-router

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic
Claude Mythos 5

Claude Mythos 5

Anthropic
Claude Opus 5

Claude Opus 5

Anthropic
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
GLM-5

GLM-5

Zhipu AI

Categories

Categories

Integrations

C#
C++
Cerebras
Claude Opus 3
Devin
Devin Desktop
JavaScript
Kotlin
Lua
MATLAB
PHP
Python
R
Ruby
Rust
SQL
Scala
Terraform
TypeScript
XML

Integrations

C#
C++
Cerebras
Claude Opus 3
Devin
Devin Desktop
JavaScript
Kotlin
Lua
MATLAB
PHP
Python
R
Ruby
Rust
SQL
Scala
Terraform
TypeScript
XML
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