Ratings and Reviews 1 Rating

Total
ease
features
design

Ratings and Reviews 1 Rating

Total
ease
features
design

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
  • Innoslate Reviews & Ratings
    93 Ratings
    Company Website
  • All in One Accessibility Reviews & Ratings
    36 Ratings
    Company Website
  • Checksum.ai Reviews & Ratings
    1 Rating
    Company Website
  • Coevera Reviews & Ratings
    752 Ratings
    Company Website
  • Epsilon3 Reviews & Ratings
    265 Ratings
    Company Website
  • NINJIO Reviews & Ratings
    416 Ratings
    Company Website
  • dbt Reviews & Ratings
    263 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 Laguna S 2.1?

Laguna S 2.1 represents a state-of-the-art open weight coding model that focuses on the completion of long-term projects and demonstrates exceptional reasoning abilities. With a Mixture-of-Experts architecture comprising 118 billion parameters, it engages 8 billion parameters per token and supports a context window of up to one million tokens in both cognitive and non-cognitive modes. The model’s optimized active size enables it to execute complex tasks on local systems while remaining competitive with much larger models across a variety of benchmarks, such as terminal usage, software development, codebase question answering, and tool application. Built for durability, Laguna S 2.1 is adept at addressing demanding challenges with an emphasis on thorough verification and a willingness to backtrack when necessary, rather than hastily claiming victory. In real-world scenarios, it has successfully engineered a browser rendering engine from the ground up, improved an agent harness for faster execution and lower memory requirements, and conducted comprehensive mathematical investigations using the tools available in its environment, showcasing its adaptability and proficiency. This remarkable array of capabilities positions Laguna S 2.1 as an invaluable asset for developers in search of cutting-edge solutions, making it a top choice in the ever-evolving landscape of coding models.

Media

Media

Integrations Supported

.NET
C
C++
CSS
Cerebras
Claude Code
Devin
Hermes Agent
Hugging Face
JSON
Kilo Code
Lua
OpenAI Codex
OpenRouter
PowerShell
R
Solidity
Terraform
Visual Studio Code
Zed

Integrations Supported

.NET
C
C++
CSS
Cerebras
Claude Code
Devin
Hermes Agent
Hugging Face
JSON
Kilo Code
Lua
OpenAI Codex
OpenRouter
PowerShell
R
Solidity
Terraform
Visual Studio Code
Zed

API Availability

Has API

API Availability

Has API

Pricing Information

$20/month
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Cognition

Date Founded

2023

Company Location

United States

Company Website

cognition.com

Company Facts

Organization Name

Poolside

Date Founded

2023

Company Location

United States

Company Website

poolside.ai/blog/introducing-laguna-s-2-1

Categories and Features

Categories and Features

Popular Alternatives

Popular Alternatives

GPT-5.6 Sol Reviews & Ratings

GPT-5.6 Sol

OpenAI
Claude Opus 5 Reviews & Ratings

Claude Opus 5

Anthropic
SWE-1.7 Reviews & Ratings

SWE-1.7

Cognition
Laguna XS 2.1 Reviews & Ratings

Laguna XS 2.1

Poolside