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sakana · Frontier

Fugu Max

Added Sep 11, 2026

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Use in the app

Sakana AI's cost-performance Fugu model uses learned multi-agent orchestration to route tasks across expert models for reasoning, coding, and tool use.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
Input
Text, Images
Output
Text
Reasoning effort
high · xhigh · max
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$2.00
Output
$6.00
Cache read
$0.25

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
128K
Average output (7 days)
2.2K tokens
Longer than 92% of 473 models

Side by side

Fugu Max compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Fugu MaxsakanaThis modelGemini 4 ArgonGoogleRemove
ProvidersakanaGoogle
Discovery tierFrontierFrontier
AddedSep 11, 2026Oct 13, 2026
Limits
Context window1M1.05MBest
Max output128KBest65.54K
Average output2.2K tokens1.6K tokens
Pricing, USD per 1M tokens
Input$2.00$2.00
Output$6.00Best$10.00
Cache read$0.25$0.10Best
Cache write—$0.19
Performance
Throughput—66.1 tok/s
Time to first token—6.9 s
Request success (24h)100.0%Best98.5%
Benchmark indexes
Intelligence Index—52.6
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputYesYes
Audio inputNoYes
Video inputNoYes
Reasoning efforthigh, xhigh, maxlow, medium, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Fugu Max
  • Gemini 4 Argon

Intelligence Index

  • Fugu Max—
  • Gemini 4 Argon52.6
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Fugu Max: —
    • Gemini 4 Argon: 77.5%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Fugu Max: —
    • Gemini 4 Argon: 53.1%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Fugu Max: —
    • Gemini 4 Argon: 3.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Fugu Max: —
    • Gemini 4 Argon: 1,486 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Fugu Max: —
    • Gemini 4 Argon: 1,624 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Fugu Max: —
    • Gemini 4 Argon: 21.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Fugu Max: —
    • Gemini 4 Argon: 79.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Fugu Max: —
    • Gemini 4 Argon: 13.9%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Fugu Max: —
    • Gemini 4 Argon: 57.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Fugu Max: —
    • Gemini 4 Argon: 57.1%

  • SciCode

    Scientific computing in Python

    • Fugu Max: —
    • Gemini 4 Argon: 61.8%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Fugu Max: —
    • Gemini 4 Argon: 79.7%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sakana/fugu-max",
    "reasoning_effort": "high",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="sakana/fugu-max",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

sakana

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