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sakana · Frontier

Fugu Ultra

Added Jun 22, 2026

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Use in the app

Sakana AI's higher-quality Fugu model. It coordinates a deeper pool of expert agents for hard, high-stakes reasoning and coding tasks.

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

Pricing

USD per 1M tokens
Input
$5.00
Output
$30.00
Cache read
$0.50

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
16.4K
Average output (7 days)
33 tokens
Longer than 2% of 473 models

Side by side

Fugu Ultra compared

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

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Fugu UltrasakanaThis modelGPT 5.5OpenAIRemove
ProvidersakanaOpenAI
Discovery tierFrontierFrontier
AddedJun 22, 2026Apr 23, 2026
Limits
Context window1M1.05MBest
Max output16.38K128KBest
Average output33 tokens490 tokens
Pricing, USD per 1M tokens
Input$5.00$5.00
Output$30.00$30.00
Cache read$0.50$0.50
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—38.4
Coding Index—74.9
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputNoYes
Reasoning efforthigh, xhigh, maxnone, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Fugu Ultra
  • GPT 5.5

Intelligence Index

  • Fugu Ultra—
  • GPT 5.538.4
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Fugu Ultra: —
    • GPT 5.5: 74.9

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Fugu Ultra: —
    • GPT 5.5: 47.3%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Fugu Ultra: —
    • GPT 5.5: 19.6%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Fugu Ultra: —
    • GPT 5.5: 1,136 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Fugu Ultra: —
    • GPT 5.5: 1,351 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Fugu Ultra: —
    • GPT 5.5: 21.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Fugu Ultra: —
    • GPT 5.5: 84.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Fugu Ultra: —
    • GPT 5.5: 28.9%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Fugu Ultra: —
    • GPT 5.5: 45.8%

  • GPQA Diamond

    Graduate-level science questions

    • Fugu Ultra: —
    • GPT 5.5: 93.5%

  • IFBench

    Following precise output instructions

    • Fugu Ultra: —
    • GPT 5.5: 75.9%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Fugu Ultra: —
    • GPT 5.5: 14.6%

  • SciCode

    Scientific computing in Python

    • Fugu Ultra: —
    • GPT 5.5: 55.8%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Fugu Ultra: —
    • GPT 5.5: 60.6%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Fugu Ultra: —
    • GPT 5.5: 93.9%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Fugu Ultra: —
    • GPT 5.5: 84.3%

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

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-ultra",
    "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-ultra",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

sakana

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