SWE-1.7 Reviews & Ratings

SWE-1.7

5.0 out of 5 stars

Ratings and Reviews 1 Rating

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ease 5 out of 5 stars
features 5 out of 5 stars
design 5 out of 5 stars
support 5 out of 5 stars

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What is SWE-1.7?

SWE-1.7 is a frontier software engineering model from Cognition built for advanced coding agents and long-horizon development workflows. It is designed to deliver strong coding intelligence at a fraction of the cost of some leading frontier alternatives, improving the cost-performance balance for real software engineering work. The model is trained from a Kimi K2.7 base and further improved through Cognition’s reinforcement learning pipeline, showing that additional post-training can still produce major capability gains. SWE-1.7 is optimized for tasks such as bug fixing, feature implementation, code migrations, terminal-based workflows, multilingual software engineering, large codebase navigation, and end-to-end validation. It performs especially well on longer asynchronous tasks where an AI agent needs to gather context, inspect files, test hypotheses, make changes, and verify results over an extended period. Cognition trained the model with infrastructure improvements that preserve entropy, stabilize training, support multi-cluster reinforcement learning, and improve fault tolerance across large distributed runs. The training process also focused heavily on data quality, using automated execution tests, verifier quality checks, reward-hacking prevention, and task filtering to create stronger learning signals. SWE-1.7 includes self-compaction, allowing it to summarize its working state and continue long projects even when tasks exceed the raw context window. It also uses an alternating length penalty to encourage concise reasoning on easier tasks while maintaining deeper exploration when a problem requires it. In practice, the model tends to explore codebases carefully, read relevant files, search for hidden requirements, test edge cases, and experiment before deciding how to implement a fix. Available in Devin across web, desktop, and CLI via Cerebras, SWE-1.7 gives engineering teams a powerful model for running scalable, cost-efficient coding agents.

What is Merrymake?

Merrymake, located in Aarhus, Denmark, serves as a European think tank and research laboratory focused on artificial intelligence. Our goal is to promote a secure and independent shift towards Artificial Superintelligence (ASI). The primary aim of our organization is to make sure that advancements in superhuman intelligence positively impact all forms of life, humanity as a whole, and each individual person. Our team operates at the cutting edge of technology, treating both the development of AI capabilities and the alignment of those capabilities as interconnected engineering challenges that can be addressed. Our research encompasses the entire spectrum of advanced AI development. We are committed to crafting a future that is truly worthwhile, where technological progress enhances human abilities while consistently upholding values such as well-being, dignity, and individual autonomy. By envisioning this tomorrow, we strive to create a world that empowers everyone to thrive.

Media

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Integrations Supported

.NET
C
C#
Cerebras
Devin
Devin Desktop
HTML
Kotlin
Kubernetes
MATLAB
Objective-C
PHP
PowerShell
Python
SQL
Solidity
Terraform
TypeScript
XML
YAML

Integrations Supported

API Availability

API Availability

Pricing Information

$20/month
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Not specified

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

Company Facts

Organization Name

Cognition

Date Founded

2023

Company Location

United States

Company Website

cognition.com

Company Facts

Organization Name

Merrymake

Date Founded

2021

Company Location

Denmark

Company Website

merrymake.eu

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Categories and Features

AI Models

Not specified

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