NOUMENOpen app

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 modelFugu MaxsakanaRemove
Providersakanasakana
Discovery tierFrontierFrontier
AddedJun 22, 2026Sep 11, 2026
Limits
Context window1M1M
Max output16.38K128KBest
Average output33 tokens2.2K tokens
Pricing, USD per 1M tokens
Input$5.00$2.00Best
Output$30.00$6.00Best
Cache read$0.50$0.25Best
Performance
Request success (24h)—100.0%
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesYes
PDF inputNoYes
Reasoning efforthigh, xhigh, maxhigh, xhigh, max

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

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)

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