// head_to_head

Claude Fable 5 vs Fugu Ultra

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Fugu Ultra is the cheaper of the two; neither can be ranked on quality here.

Fugu Ultra does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

anthropic

Claude Fable 5

Blended / 1M
$20.00
Context
1M
Released
Jun 9, 2026
Overall score
83.0
reasoningtool callingimage inputfile inputprompt caching

sakana

Fugu Ultra

Blended / 1M
$11.25
Context
1M
Released
Jun 24, 2026
Overall score
Not evaluated
reasoningtool callingimage inputprompt caching

Specs and pricing

MetricClaude Fable 5Fugu Ultra
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

83.0
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.7925
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$20.00$11.25win
Input price / 1M$10.00$5.00win
Output price / 1M$50.00$30.00win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$1.00$0.500win
Context window1M1M
Max output tokens128K128K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Fable 5 on top, Fugu Ultra below, both out of 100.

Agentic coding
62.2
Coding
86.0
Reasoning
89.7
Mathematics
96.0
Data analysis
80.5
Language
90.7
Instruction following
75.8

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadClaude Fable 5Fugu Ultra
Support chatbot

1.2K in / 400 out × 200K requests

$5,752/mo$3,276/mo
RAG assistant

8K in / 600 out × 100K requests

$7,400/mo$4,000/mo
Coding agent

40K in / 4K out × 20K requests

$6,960/mo$3,880/mo
Document extraction

20K in / 1.5K out × 50K requests

$13,300/mo$7,025/mo
Bulk classification

500 in / 20 out × 5M requests

$25,500/mo$13,250/mo
Run these two through the cost calculator

Which should you pick?

You are cost-constrained

Fugu Ultra

Cheaper on blended list price at $11.25 per million tokens.

Claude Fable 5 vs Fugu Ultra FAQ

Which is better, Claude Fable 5 or Fugu Ultra?

Fugu Ultra is the cheaper of the two; neither can be ranked on quality here. Fugu Ultra does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.

Is Claude Fable 5 cheaper than Fugu Ultra?

Fugu Ultra is cheaper. On a 3:1 input:output blend, Claude Fable 5 lists at $20.00 per million tokens and Fugu Ultra at $11.25 — Fugu Ultra is 1.8× cheaper. Input and output are priced separately — Claude Fable 5 charges $10.00 in and $50.00 out, Fugu Ultra charges $5.00 and $30.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Does Claude Fable 5 or Fugu Ultra have a bigger context window?

They are effectively the same — 1M for Claude Fable 5 and 1M for Fugu Ultra.

Do Claude Fable 5 and Fugu Ultra support prompt caching?

Both publish a cached-input rate: $1.00 per million for Claude Fable 5 and $0.500 for Fugu Ultra, against full input rates of $10.00 and $5.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • ScoresLiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.