Sakana AI

Tokyo AI lab building nature-inspired foundation models

LLMs & Chat Freemium Open Source
Researched · Published · Reviewed
RECATOOLS Score
7.8 / 10
Capability
9
Value for money
6.5
Ease of use
5.5
ASEAN readiness
6.5
API quality
4.5
Founded
2023
HQ
Tokyo, Japan
Users
~300 beta users (Marlin); broader via free Sakana Chat
Launched
July 2023 (founding); Sakana Chat: March 2026
Developer
Independent (Sakana AI, Inc.)

Overview

Tokyo AI lab founded in 2023 by ex-Google Brain's David Ha, Transformer co-author Llion Jones and Ren Ito, pursuing evolutionary approaches to foundation models. Now Japan's most valuable AI startup at $2.65B, shipping commercial products like the Marlin research agent.

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Pricing

Pricing shown for reference only. These figures reflect RECATOOLS research as of 11 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.

Pay-per-use
$66/run
100 credits per Marlin research report
  • 8-hour autonomous research run
  • 60-100 page report + slides
  • Add-on credits ~$0.61 each
Marlin Team
¥400,000/mo (~$2,700)
6,000 credits/month
  • ~60 reports/month
  • Multi-seat access
  • Team collaboration
Enterprise
Custom
Volume + custom data-governance terms
  • Custom credit volume
  • Dedicated support
  • Contractual data-use terms

Use cases

Enterprise strategy teams generating 100-page market entry or competitive analysis reports via Sakana Marlin Japanese financial institutions deploying Namazu LLMs for fraud detection, risk modelling, and regulatory document processing AI researchers using open-source AI Scientist v2 to automate hypothesis generation, experiment execution, and manuscript drafting Developers merging open-source models using Sakana's evolutionary merge recipes to build task-specific LLMs without costly pre-training Corporate R&D labs exploring self-improving AI systems using the Darwin Gödel Machine framework for code optimisation tasks

What you can produce with Sakana AI

  • A 60–100 page strategy research report with 60–80 cited sources and presentation slides (via Sakana Marlin)
  • A fine-tuned Japanese-language LLM merged from open-source models without gradient training (via EvoLLM-JP recipe)
  • An AI-generated scientific manuscript ready for workshop peer review (via AI Scientist v2 pipeline)
  • A self-improving coding agent that iteratively rewrites its own code to hit higher SWE-bench scores (via Darwin Gödel Machine)
  • A multi-model orchestration workflow for complex reasoning or coding tasks beating single-model baselines (via Sakana Fugu / TRINITY)
  • Japanese-language conversational AI responses via the free Sakana Chat web interface
  • Open-source AB-MCTS tree search implementation for custom LLM inference-time scaling (via TreeQuest on GitHub)
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ASEAN Perspective

Sakana AI in Southeast Asia

Sakana AI is a Japan-headquartered company focused on Japanese institutional customers, with ASEAN coverage currently indirect. The company explicitly targets Japan's financial, defence, and manufacturing sectors with its Namazu LLMs and Marlin enterprise agent. A February 2026 partnership with Datadog — initially focused on Japan with plans for global expansion — includes data residency within Datadog's Tokyo data centre, making it relatively well-positioned for Japanese PDPA/APPI compliance. For ASEAN buyers in Singapore, Malaysia, or Indonesia, Sakana's products are largely inaccessible or untranslated today, though its open-source models (EvoLLM-JP, AI Scientist-v2) are freely downloadable and used by researchers across APAC. The $2.65 billion valuation and In-Q-Tel backing signal strategic ambitions beyond Japan, but no concrete ASEAN localisation roadmap has been announced as of mid-2026.

RECATOOLS Verdict

Sakana is one of the more technically credible AI labs outside the US and China -- its AI Scientist paper was the first fully AI-generated research to pass peer review, and the Darwin Godel Machine demonstrated a model that rewrote its own code and doubled its SWE-bench score. That research reputation is real, backed by a $2.65 billion valuation and money from MUFG, Nvidia and Lux Capital.

The commercial side is much younger. Sakana Marlin, its enterprise "virtual CSO" research agent, only launched in June 2026 off a roughly 300-person closed beta -- real-world reliability at scale is still unproven. Pricing runs enterprise-only (single runs from $66, Pro around $1,000/month), the product is Japanese-market-first, and there's no public API for third-party developers. A serious bet for organizations wanting Japan-local AI, not yet a drop-in general tool.

Independent AI-assisted assessment by RECATOOLS.

What people say

The AI research community rates Sakana highly, and for good reason: AI Scientist v2 became the first fully AI-authored paper to clear peer review at an ICLR workshop, and the Darwin Godel Machine's self-rewriting, self-improving code loop is the kind of result labs twice its size haven't shipped. None of that shows up on G2 or Capterra, because Sakana barely sells anything a typical SaaS buyer would review yet.

That's changing. Sakana Marlin, launched June 15, 2026, is the company's first real commercial product -- an autonomous research agent that runs for up to eight hours and returns a 60-100 page strategy report with cited sources and slides, built on Sakana's own AB-MCTS search algorithm. Pricing is steep and enterprise-shaped: pay-per-run starts around $66, a Pro subscription runs roughly ¥150,000 ($1,000) a month, Team tiers run higher, and Enterprise is quote-only. Tech coverage from TechCrunch, VentureBeat and MarkTechPost has been positive on the technical ambition but consistently flags that Marlin is fresh out of a roughly 300-person closed beta, with production reliability and support at scale still to be proven.

Sakana's pitch to Japanese finance, consulting and defense clients -- cultural alignment, no training on customer data by default, Tokyo-local infrastructure -- is a genuine differentiator regional buyers care about. But competing deep-research agents from OpenAI and Perplexity are more mature, cheaper per run, and globally supported. Sakana reads as an exciting, technically serious bet on AI sovereignty for Japan specifically, not yet a mature product for anyone evaluating on track record alone.

Summary of public user & expert reviews, compiled by RECATOOLS.

Notable facts

  • Llion Jones, CTO and co-founder, was one of eight authors of the 2017 'Attention Is All You Need' paper that introduced the Transformer architecture underpinning all modern LLMs.
  • Sakana means 'fish' in Japanese — the company logo and philosophy are inspired by how a school of fish collectively forms intelligent behaviour from simple individual rules with no central coordinator.
  • Sakana AI's Darwin Gödel Machine improved its own SWE-bench score from 20% to 50% simply by rewriting its own code — no human intervention required.
  • At its $2.65 billion November 2025 valuation, Sakana became Japan's most valuable private AI startup, surpassing rivals despite having fewer than 200 employees.

Frequently asked questions

Is Sakana AI free to use?
Partially. Sakana Chat is a free Japanese-language chatbot. Many research models (EvoLLM-JP, AI Scientist, Darwin Gödel Machine) are open-sourced on GitHub and Hugging Face. The enterprise product Sakana Marlin is paid-only, starting at ¥9,800 per run or ¥150,000 per month for the Pro plan.
Does Sakana AI offer an API for developers?
No public developer API is currently available (as of mid-2026). Sakana AI's commercial products are delivered as SaaS platforms rather than raw API endpoints. The underlying AB-MCTS algorithm powering Marlin has been open-sourced as the TreeQuest library (Apache 2.0), which developers can self-host.
How does Sakana AI's evolutionary model merging work?
Rather than training a new model from scratch, Sakana's evolutionary approach treats existing open-source models as 'genes'. An evolutionary algorithm breeds combinations of these models — merging weights and architectures — then evaluates offspring on downstream benchmarks, iterating toward high-performing hybrids. This dramatically reduces compute compared to full pre-training.

About this listing

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This entry was compiled from publicly available data including Sakana AI's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Sakana AI unless explicitly stated.

Data accuracy

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