Ratings and Reviews 1 Rating

Total
ease
features
design

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

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 M.1?

Laguna M.1 is recognized as Poolside's premier model for agentic coding, meticulously designed in-house to optimize software development processes. This sophisticated model incorporates 225 billion parameters and employs a Mixture of Experts architecture with 23 billion parameters activated, all trained on a colossal dataset of 30 trillion tokens using a network of 6,144 NVIDIA H200 GPUs. Poolside committed to developing Laguna M.1 from the ground up, utilizing proprietary data, a specialized training codebase, and an asynchronous on-policy reinforcement learning strategy within its agent framework, all specifically oriented towards agentic coding applications. The model's architecture is crafted to deliver top-tier performance within Poolside's coding agent, empowering it to adeptly reason through programming tasks, engage with an array of tools, modify code, run tests, and support extensive autonomous development sessions. Tailored for developers and teams facing complex coding obstacles, Laguna M.1 boasts enhanced capabilities in reasoning, understanding architecture, managing terminal actions, and executing multi-step processes, far exceeding the abilities of lighter models. Overall, its comprehensive feature set establishes it as an indispensable tool for professionals immersed in high-stakes software projects, making it a vital component in the landscape of agentic coding solutions.

Media

Media

Integrations Supported

C#
C++
Devin Desktop
Go
HTML
Hermes Agent
IntelliJ IDEA
JSON
Kubernetes
MATLAB
OpenClaw
OpenCode
OpenRouter
PHP
Poolside
R
Rust
SQL
Terraform
YAML

Integrations Supported

C#
C++
Devin Desktop
Go
HTML
Hermes Agent
IntelliJ IDEA
JSON
Kubernetes
MATLAB
OpenClaw
OpenCode
OpenRouter
PHP
Poolside
R
Rust
SQL
Terraform
YAML

API Availability

Has API

API Availability

Has API

Pricing Information

$20/month
Free Version
Free Trial Offered?

Pricing Information

Free
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

www.poolside.ai/models

Categories and Features

Categories and Features

Popular Alternatives

Popular Alternatives

GLM-5.2 Reviews & Ratings

GLM-5.2

Z.ai
Claude Opus 4.8 Reviews & Ratings

Claude Opus 4.8

Anthropic
GPT-5.6 Sol Reviews & Ratings

GPT-5.6 Sol

OpenAI
SWE-1.7 Reviews & Ratings

SWE-1.7

Cognition
Laguna XS.2 Reviews & Ratings

Laguna XS.2

Poolside