Ratings and Reviews 1 Rating

Total
ease
features
design

Ratings and Reviews 1 Rating

Total
features

Alternatives to Consider

  • TrustInSoft Analyzer Reviews & Ratings
    6 Ratings
    Company Website
  • Interfacing Integrated Management System (IMS) Reviews & Ratings
    66 Ratings
    Company Website
  • Flagsmith Reviews & Ratings
    42 Ratings
    Company Website
  • All in One Accessibility Reviews & Ratings
    36 Ratings
    Company Website
  • Innoslate Reviews & Ratings
    93 Ratings
    Company Website
  • JetBrains Junie Reviews & Ratings
    12 Ratings
    Company Website
  • Checksum.ai Reviews & Ratings
    1 Rating
    Company Website
  • Epsilon3 Reviews & Ratings
    265 Ratings
    Company Website
  • NINJIO Reviews & Ratings
    416 Ratings
    Company Website
  • Cadebill Reviews & Ratings
    1 Rating
    Company Website

What is SWE-2?

SWE-2 is Cognition’s coding model for software engineering agents, developed to improve the balance between capability, reasoning cost, and execution efficiency. The model is post-trained from Kimi K3, a multi-trillion-parameter model that had already received extensive reinforcement learning for agentic coding. Cognition further trained SWE-2 with a reinforcement learning algorithm that optimizes several reasoning-effort levels during a single training run. These effort levels let users trade off speed and cost against deeper planning, codebase exploration, and verification for more difficult assignments. SWE-2 is designed to reduce the over-exploration seen in earlier models by identifying relevant files and implementation paths more quickly. Its software engineering abilities include repository analysis, code writing and editing, debugging, testing, build and lint workflows, terminal tasks, and verification of completed work. The model places additional emphasis on writing end-to-end tests, catching edge cases and regressions, and gathering evidence instead of simply accepting assumptions in a prompt. Cognition’s training approach also uses cost penalties tied to the model’s performance frontier, length-weighted reward baselines, speculative decoding improvements, low-precision inference techniques, and expanded reinforcement learning data. Training data includes more diverse repositories, additional instruction-following requirements, and iterative verifier improvements designed to reduce reward hacking and false validation. SWE-2 is benchmarked against models such as GPT-6 Astra, GPT-5.6 Sol, Fable 5.1, Grok 4.6, and Kimi K3, with Cognition positioning it around strong coding performance at substantially lower cost. SWE-2 is intended for use across Cognition’s Devin ecosystem, including Desktop and CLI, with rollout to Devin Web and Fusion.

What is MAI-Code-1.1-Flash?

MAI-Code-1.1-Flash is a streamlined and powerful coding model designed to boost both the speed and quality of code development specifically for engineering teams. Currently utilized in GitHub Copilot and seamlessly integrated into VS Code, it aligns with the everyday workflows of developers by particularly enhancing command-line functions and .NET operations based on user interactions. In comparison to the version revealed at Microsoft Build in June, this model demonstrates notable advancements in code quality, achieved through lower token consumption and faster streaming responses. Microsoft reports a 22% improvement on Terminal-Bench 2.1 for GitHub Copilot CLI, as well as a 15% enhancement in .NET task performance. Furthermore, production metrics reveal a 4% increase in code survival rates and a 9% rise in user retention on the platform. Impressively, within GitHub Copilot, tokens are streamed 25% more quickly, and the model utilizes 25% fewer tokens to complete tasks, which results in faster responses, shortened wait times, and heightened productivity from each token processed. These improvements arise from refined training approaches and enhanced operational efficiencies, with particular emphasis on practical application in real-world contexts. Ultimately, MAI-Code-1.1-Flash signifies a remarkable advancement in coding assistance technology, paving the way for more efficient development practices. With its emphasis on user experience and real-time feedback, this model is set to redefine how developers interact with coding tools.

Media

Media

Integrations Supported

.NET
C
C#
CSS
Cerebras
Devin Desktop
Go
JSON
JavaScript
Kotlin
Objective-C
PHP
Python
Rust
SQL
Scala
Swift
Terraform
YAML

Integrations Supported

.NET
Microsoft Azure

API Availability

API Availability

Pricing Information

$20/month
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Cognition

Date Founded

2023

Company Location

United States

Company Website

cognition.com

Company Facts

Organization Name

Microsoft AI

Date Founded

2024

Company Location

United States

Company Website

microsoft.ai/news/mai-code-1-1-flash-br-better-faster-at-a-quarter-of-the-cost/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Popular Alternatives

Popular Alternatives

GPT-5.6 Sol Reviews & Ratings

GPT-5.6 Sol

OpenAI
SWE-1.7 Reviews & Ratings

SWE-1.7

Cognition
MAI-Code-1-Flash Reviews & Ratings

MAI-Code-1-Flash

Microsoft AI