MAI-Code-1.1-Flash

MAI-Code-1.1-Flash

Microsoft AI
SWE-1.7

SWE-1.7

Cognition
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About

MAI-Code-1.1-Flash is a small, efficient coding model designed to help engineering teams write better code faster. Now in production in GitHub Copilot and built into VS Code, it focuses on real-world developer workflows, with particular improvements for command-line tasks and .NET development based on developer feedback. Compared with the version introduced at Microsoft Build in June, the model produces higher-quality code while using fewer tokens and streaming responses faster. Microsoft reports a 22% improvement on Terminal-Bench 2.1 in GitHub Copilot CLI and a 15% improvement on .NET tasks. Production results also showed a 4% increase in code survival and a 9% increase in return visits. In GitHub Copilot, tokens stream 25% faster and the model uses 25% fewer tokens to complete a task, aiming to deliver faster answers, less waiting, and more useful work from every token. Its gains come from improved training and serving efficiency, with optimization centered on real-world use.

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

Software engineering teams and developers seeking to write and complete code faster with an efficient AI coding model integrated into their development workflow

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 4.0 / 5
features 4.0 / 5

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

  • The agentic coding angle is the best part. It can plan, reason, and execute across coding tasks, which makes it useful beyond simple autocomplete. I also like the screenshot-to-prototype feature. Being able to understand screenshots, diagrams, and designs could save a lot of time when turning UI ideas into working code.

Cons

  • The main downside is that I would still review everything carefully. Even a strong coding model can make bad assumptions, miss edge cases, or produce code that looks right but fails in a real project.

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

Microsoft AI
Founded: 2024
United States
microsoft.ai/news/mai-code-1-1-flash-br-better-faster-at-a-quarter-of-the-cost/

Company Information

Cognition
Founded: 2023
United States
cognition.com

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MAI-Code-1-Flash

MAI-Code-1-Flash

Microsoft AI
Kimi K2.7 Code

Kimi K2.7 Code

Moonshot AI

Categories

Categories

Integrations

.NET
C
CSS
Dart
Devin
Devin Desktop
GitHub Copilot
HTML
JSON
Kubernetes
MATLAB
Microsoft Azure
Objective-C
PHP
PowerShell
SQL
Scala
Solidity
XML
YAML

Integrations

.NET
C
CSS
Dart
Devin
Devin Desktop
GitHub Copilot
HTML
JSON
Kubernetes
MATLAB
Microsoft Azure
Objective-C
PHP
PowerShell
SQL
Scala
Solidity
XML
YAML
Claim MAI-Code-1.1-Flash and update features and information
Claim MAI-Code-1.1-Flash and update features and information
Claim SWE-1.7 and update features and information
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