Ratings and Reviews 1 Rating

Total
ease
features
design

Ratings and Reviews 1 Rating

Total
features

Alternatives to Consider

  • SearchUnify Reviews & Ratings
    150 Ratings
    Company Website
  • TrustInSoft Analyzer Reviews & Ratings
    6 Ratings
    Company Website
  • Interfacing Integrated Management System (IMS) Reviews & Ratings
    66 Ratings
    Company Website
  • Flagsmith Reviews & Ratings
    43 Ratings
    Company Website
  • All in One Accessibility Reviews & Ratings
    41 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

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 GPT-6 Sol?

GPT-6 Sol is an advanced OpenAI model positioned between the cost-efficient GPT-6 Luna and the higher-capability GPT-6 Astra for demanding professional and agentic workloads. The model is designed for coding, knowledge work, business automation, computer use, research, and other tasks that require sustained reasoning across multiple steps. It inherits advances from the GPT-6 generation while emphasizing a balance of intelligence, speed, and operating cost for applications that need to run at scale. GPT-6 Sol supports multiple reasoning-effort levels so applications can spend more computation on difficult tasks and reduce effort for straightforward requests. In software development, it can handle complex real-codebase tasks, generate merge-ready changes, debug software, work through terminal workflows, and operate as part of coding agents. Its professional-work capabilities support multi-application processes spanning functions such as finance, operations, sales, marketing, customer support, and human resources. Computer-use abilities allow agents powered by GPT-6 Sol to interact with graphical interfaces and complete long-horizon workflows involving everyday and professional software. OpenAI has also improved the model’s factual reliability, communication style, and alignment compared with GPT-5.6 Sol, including lower rates of misleading claims in challenging coding evaluations. GPT-6 prompt caching provides higher cache-hit rates, supports changing reasoning effort or available tools without invalidating earlier cached context, and offers substantial discounts for cached input tokens. Developers can monitor caching behavior, configure prompt-cache breakpoints, and incorporate Sol into persistent agents that repeatedly reuse large amounts of context. GPT-6 Sol is accessible through ChatGPT Work, Codex, and the OpenAI API under the gpt-6-sol model identifier.

Media

Media

Integrations Supported

.NET
C
C#
CSS
Go
JavaScript
Kubernetes
Objective-C
PHP
PowerShell
Python
R
Ruby
Rust
Scala
Solidity
Swift
TypeScript
XML
YAML

Integrations Supported

.NET
C
C#
CSS
Go
JavaScript
Kubernetes
Objective-C
PHP
PowerShell
Python
R
Ruby
Rust
Scala
Solidity
Swift
TypeScript
XML
YAML

API Availability

API Availability

Has API

Pricing Information

$20/month
Free Version

Pricing Information

$2 per 1M tokens (input)
Input: $2 per 1 million tokens
Output: $10 per 1 million 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

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com

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

AI Vision 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
GPT-6 Luna Reviews & Ratings

GPT-6 Luna

OpenAI