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

Ratings and Reviews 1 Rating

Total
ease
features
design

Alternatives to Consider

  • Gemini Enterprise Agent Platform Reviews & Ratings
    999 Ratings
    Company Website
  • BAND Reviews & Ratings
    3 Ratings
    Company Website
  • kama.ai Reviews & Ratings
    9 Ratings
  • JetBrains Junie Reviews & Ratings
    12 Ratings
    Company Website
  • Google AI Studio Reviews & Ratings
    41 Ratings
    Company Website
  • LM-Kit.NET Reviews & Ratings
    29 Ratings
    Company Website
  • Forethought Reviews & Ratings
    166 Ratings
    Company Website
  • Creatio Reviews & Ratings
    586 Ratings
    Company Website
  • Sendbird Reviews & Ratings
    166 Ratings
    Company Website
  • Admin By Request Endpoint Privilege Management Reviews & Ratings
    105 Ratings
    Company Website

What is Sakana Fugu?

Sakana Fugu is a multi-agent AI system that operates like one model while coordinating many underlying expert models behind a single API. The platform is designed to deliver frontier-level performance without forcing users to depend on one model provider or manually manage several separate AI tools. Fugu dynamically chooses which agents should participate in each task and coordinates them through learned collaboration patterns. This approach allows the system to handle complex work such as coding, reasoning, scientific problem solving, code review, security assessment, literature analysis, patent research, and autonomous research workflows. Sakana Fugu is grounded in research on learned orchestration, including TRINITY and the Conductor, which explore how AI systems can route tasks, assign roles, and coordinate communication among multiple agents. Users can access the system through an OpenAI-compatible API and choose between Fugu and Fugu Ultra depending on their workload. Fugu is built for everyday coding, chatbot, review, and productivity use cases where strong performance and lower latency are both important. Fugu Ultra uses a deeper pool of expert agents to improve quality on harder tasks such as Kaggle competitions, paper reproduction, cybersecurity analysis, and technical investigations. Organizations can control which agents, providers, or models are allowed in the pool to meet privacy, data handling, compliance, and procurement needs. The platform offers pay-as-you-go and subscription pricing options, with Fugu Ultra priced separately for input, output, and cached input tokens. Sakana Fugu gives developers, researchers, and enterprises a way to plug multi-agent intelligence into existing workflows while maintaining flexibility, control, and stronger performance on demanding tasks.

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.

Media

Media

Integrations Supported

Claude Code
OpenRouter
Sakana Fugu Ultra

Integrations Supported

.NET
C#
CSS
Go
HTML
JavaScript
Kotlin
Kubernetes
MATLAB
PHP
Python
R
SQL
Solidity
Swift
XML
YAML

API Availability

Has API

API Availability

Pricing Information

$20/month
API:
$5 per 1M tokens (input)
$30 per 1M tokens (output)
$0.50 per 1M tokens (cached input)

Pricing Information

$20/month
Free Version

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

Sakana AI

Date Founded

2023

Company Location

Japan

Company Website

sakana.ai/fugu/

Company Facts

Organization Name

Cognition

Date Founded

2023

Company Location

United States

Company Website

cognition.com

Categories and Features

Agentic AI

Not specified

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Popular Alternatives

Popular Alternatives

SWE-1.7 Reviews & Ratings

SWE-1.7

Cognition