SWE-2 Reviews & Ratings

SWE-2

5.0 out of 5 stars
Muse Glimmer Reviews & Ratings

Muse Glimmer

5.0 out of 5 stars

Ratings and Reviews 1 Rating

Total 5 out of 5 stars
ease 5 out of 5 stars
features 5 out of 5 stars
design 5 out of 5 stars

Ratings and Reviews 1 Rating

Total 5 out of 5 stars

Alternatives to Consider

  • SearchUnify Reviews & Ratings
    4.5 out of 5 stars 150 Ratings
    Company Website for SearchUnify
  • TrustInSoft Analyzer Reviews & Ratings
    4.3 out of 5 stars 6 Ratings
    Company Website for TrustInSoft Analyzer
  • Interfacing Integrated Management System (IMS) Reviews & Ratings
    4.6 out of 5 stars 66 Ratings
    Company Website for Interfacing Integrated Management System (IMS)
  • All in One Accessibility Reviews & Ratings
    4.8 out of 5 stars 41 Ratings
    Company Website for All in One Accessibility
  • Flagsmith Reviews & Ratings
    4.8 out of 5 stars 43 Ratings
    Company Website for Flagsmith
  • Innoslate Reviews & Ratings
    4.5 out of 5 stars 99 Ratings
    Company Website for Innoslate
  • Checksum.ai Reviews & Ratings
    5.0 out of 5 stars 2 Ratings
    Company Website for Checksum.ai
  • Epsilon3 Reviews & Ratings
    4.2 out of 5 stars 265 Ratings
    Company Website for Epsilon3
  • NINJIO Reviews & Ratings
    4.8 out of 5 stars 416 Ratings
    Company Website for NINJIO
  • Cadebill Reviews & Ratings
    5.0 out of 5 stars 1 Rating
    Company Website for Cadebill

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 Muse Glimmer?

Muse Glimmer is a cutting-edge model boasting 30 billion parameters, crafted by Meta Superintelligence Labs, specifically optimized for seamless local agent functionality. Its streamlined architecture enables operation on standard Mac or PC systems with a single consumer GPU, making it suitable for a range of applications, including local agent management, programming tasks, function invocation, and evaluations within LLM-as-a-judge scenarios, all without needing cloud services or an internet connection. This groundbreaking model features sophisticated abilities like long-horizon execution, precise tool invocation, multimodal understanding, expanded memory for contextual awareness, and proficient instruction adherence. It excels in performing comprehensive tasks as an agent, adeptly navigates complex multi-step reasoning across extensive workflows, and can recover effectively from unexpected tool interactions. Additionally, it interprets interleaved text and images through a specialized perception encoder tailored for analyzing screenshots, graphs, and various document types. Beyond its primary functions, Muse Glimmer is designed to work harmoniously with OpenClaw and other orchestration frameworks, allowing for customizable reasoning capabilities and has been trained on a rich dataset that spans over 100 languages. The adaptability of this model not only enhances its effectiveness across different fields but also positions it as a significant asset in the evolving landscape of AI applications. Its innovative features and user-friendly deployment make it a versatile choice for professionals seeking to leverage AI for complex problem-solving.

Media

Media

Integrations Supported

C#
CSS
Devin
JSON
JavaScript
Kubernetes
Lua
Objective-C
PowerShell
R
SQL
Scala
Terraform
TypeScript
YAML

Integrations Supported

ExecuTorch
Hugging Face
Meta AI
Muse Code
OpenCode

API Availability

API Availability

Has API

Pricing Information

$20/month
Free Version

Pricing Information

Free
Free Trial Offered?

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

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.ai/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

Popular Alternatives

Popular Alternatives

Grok 4.6 Reviews & Ratings

Grok 4.6

SpaceXSI
5.0 out of 5 stars
GPT-6.1 Terra Reviews & Ratings

GPT-6.1 Terra

OpenAI
5.0 out of 5 stars
Kimi K3.1 Pro Reviews & Ratings

Kimi K3.1 Pro

Moonshot AI
5.0 out of 5 stars
GPT-6.1 Sol Reviews & Ratings

GPT-6.1 Sol

OpenAI
5.0 out of 5 stars
Muse Spark Open Reviews & Ratings

Muse Spark Open

Meta
5.0 out of 5 stars
SWE-1.7 Reviews & Ratings

SWE-1.7

Cognition
5.0 out of 5 stars
MiniMax M3 Reviews & Ratings

MiniMax M3

MiniMax
5.0 out of 5 stars
SWE-2 Flash Reviews & Ratings

SWE-2 Flash

Cognition
5.0 out of 5 stars