Ratings and Reviews 1 Rating

Total
ease
features
design

Ratings and Reviews 1 Rating

Total
ease
features

Alternatives to Consider

  • TrustInSoft Analyzer Reviews & Ratings
    6 Ratings
    Company Website
  • Interfacing Integrated Management System (IMS) Reviews & Ratings
    66 Ratings
    Company Website
  • All in One Accessibility Reviews & Ratings
    41 Ratings
    Company Website
  • Flagsmith Reviews & Ratings
    43 Ratings
    Company Website
  • Innoslate Reviews & Ratings
    98 Ratings
    Company Website
  • Checksum.ai Reviews & Ratings
    2 Ratings
    Company Website
  • Epsilon3 Reviews & Ratings
    265 Ratings
    Company Website
  • NINJIO Reviews & Ratings
    416 Ratings
    Company Website
  • Cadebill Reviews & Ratings
    1 Rating
    Company Website
  • AnalyticsCreator Reviews & Ratings
    46 Ratings
    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 Muse Wonder?

Muse Wonder is a frontier artificial intelligence model developed by Meta for advanced reasoning, autonomous agent workflows, personal assistance, and professional applications. Released in October 2026, the model belongs to the Muse Wonder family alongside Muse Wonder Full, a larger variant designed for computationally demanding tasks. The model incorporates Active Memory, a capability that preserves important contextual information across conversations and retrieves relevant details to support personalized assistance. Its Active Focus functionality enables sustained execution of complex objectives by exploring alternative solutions, adapting to new information, and working through obstacles. Muse Wonder can request clarification when instructions are incomplete and seek user approval before performing consequential actions. The model supports professional tasks involving legal analysis, financial information, health-related questions, research, and business process automation. Its agentic capabilities allow it to interact with connected applications, navigate browser-based workflows, and use external tools to complete multistep assignments. For software development, Muse Wonder can generate code, build applications, analyze existing projects, modify source files, and perform iterative testing and debugging. Meta trained the Muse Wonder series on diverse long-running tasks to improve performance on software engineering projects and unfamiliar problem-solving scenarios. The model has been evaluated using benchmarks such as MemoryBench, FocusBench, AutomationBench-AA, SWE-Marathon, and DeepSWE, with additional safety evaluations addressing privacy protection, adversarial instructions, and misuse prevention. Muse Wonder is available through the Meta Model API and Muse Code, while Muse Wonder Full is available to Muse Code subscribers, with broader integration into Meta's Muse assistant planned.

Media

Media

Integrations Supported

.NET
C
C#
C++
CSS
HTML
JavaScript
Kotlin
Kubernetes
Objective-C
PHP
PowerShell
R
Ruby
Rust
SQL
Solidity
TypeScript
XML
YAML

Integrations Supported

.NET
C
C#
C++
CSS
HTML
JavaScript
Kotlin
Kubernetes
Objective-C
PHP
PowerShell
R
Ruby
Rust
SQL
Solidity
TypeScript
XML
YAML

API Availability

API Availability

Has API

Pricing Information

$20/month
Free Version

Pricing Information

$4 per 1M tokens (input)
Input: $4 per 1M tokens
Output: $20 per 1M tokens

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

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Popular Alternatives

Popular Alternatives

SWE-1.7 Reviews & Ratings

SWE-1.7

Cognition