SWE-1.7

SWE-1.7

Cognition
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About

Arcee AI is a US-based open intelligence lab focused on building high-performance, open-weight AI models for developers and enterprises. It develops frontier AI systems designed for reasoning, scalability, and real-world applications. The company is known for its Trinity model family, which delivers advanced capabilities while remaining transparent and accessible. Arcee AI emphasizes continuous improvement through techniques like online reinforcement learning, allowing models to evolve after deployment. Its approach prioritizes cost efficiency, enabling powerful AI performance without excessive infrastructure costs. The platform supports developers with tools, APIs, and open-source resources to build intelligent applications. Overall, Arcee AI aims to make cutting-edge AI more accessible, practical, and scalable for a wide range of use cases.

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

AI developers, enterprises, and research teams seeking scalable, open-weight AI models and tools for building advanced, cost-efficient intelligent applications

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

No information available.
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

Arcee AI
Founded: 2023
United States
www.arcee.ai/

Company Information

Cognition
Founded: 2023
United States
cognition.com

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Categories

Categories

Integrations

.NET
C
C#
C++
CSS
Cerebras
Dart
Devin Desktop
HTML
JSON
JavaScript
Kotlin
Lua
MATLAB
Objective-C
Python
SQL
Scala
Solidity
Terraform

Integrations

.NET
C
C#
C++
CSS
Cerebras
Dart
Devin Desktop
HTML
JSON
JavaScript
Kotlin
Lua
MATLAB
Objective-C
Python
SQL
Scala
Solidity
Terraform
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