SWE-1.7

SWE-1.7

Cognition
+
+

Related Products

  • Google AI Studio
    30 Ratings
    Visit Website
  • Retool
    584 Ratings
    Visit Website
  • JetBrains Junie
    12 Ratings
    Visit Website
  • Parasoft
    148 Ratings
    Visit Website
  • Passwork
    117 Ratings
    Visit Website
  • Planview AdaptiveWork
    714 Ratings
    Visit Website
  • Planview ProjectAdvantage
    121 Ratings
    Visit Website
  • AuctionMethod
    43 Ratings
    Visit Website
  • Haast
    4 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website

About

Relace offers a suite of specialized AI models purpose-built for coding workflows. Its retrieval, embedding, code-reranker, and “Instant Apply” models are designed to integrate into existing development environments and accelerate code production, merging changes at speeds over 2,500 tokens per second and handling large codebases (million-line scale) in under 2 seconds. The platform supports hosted API access and self-hosted or VPC-isolated deployments, so teams have full control of data and infrastructure. Its code-oriented embedding and reranking models identify the most relevant files for a given developer query and filter out irrelevant context, reducing prompt bloat and improving accuracy. The Instant Apply model merges AI-generated snippets into existing codebases with high reliability and low error rate, streamlining pull-request reviews, CI/CD workflows, and automated fixes.

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

Engineering teams and startups wanting a tool for building AI-code-generation products and coding agents

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

$0.80 per million tokens
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

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

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

Relace
United States
www.relace.ai/

Company Information

Cognition
Founded: 2023
United States
cognition.com

Alternatives

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic
Claude Code

Claude Code

Anthropic
Claude Mythos 5

Claude Mythos 5

Anthropic
Claude Opus 5

Claude Opus 5

Anthropic
Cline

Cline

Cline AI Coding Agent
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI
GLM-5

GLM-5

Zhipu AI

Categories

Categories

Integrations

C
C#
Cerebras
Devin
Devin Desktop
Go
HTML
JSON
JavaScript
Kotlin
Kubernetes
Lua
Objective-C
PHP
PowerShell
Rust
SQL
Solidity
Swift
YAML

Integrations

C
C#
Cerebras
Devin
Devin Desktop
Go
HTML
JSON
JavaScript
Kotlin
Kubernetes
Lua
Objective-C
PHP
PowerShell
Rust
SQL
Solidity
Swift
YAML
Claim Relace and update features and information
Claim Relace and update features and information
Claim SWE-1.7 and update features and information
Claim SWE-1.7 and update features and information