// head_to_head

GPT-5.5 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

GPT-5.5 and Fugu Ultra are priced within ~10% of each other.

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.

openai

GPT-5.5

Blended / 1M
$11.25
Context
1.1M
Released
Apr 24, 2026
Overall score
80.2
reasoningtool callingfile inputimage 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

MetricGPT-5.5Fugu Ultra
LiveBench overall

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

80.2
Cost per point

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

$0.2417
Blended price / 1M

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

$11.25$11.25
Input price / 1M$5.00$5.00
Output price / 1M$30.00$30.00
Cached input / 1M

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

$0.500$0.500
Context window1.1M1M
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 — GPT-5.5 on top, Fugu Ultra below, both out of 100.

Agentic coding
54.0
Coding
82.1
Reasoning
89.7
Mathematics
95.9
Data analysis
81.6
Language
87.4
Instruction following
70.7

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.

WorkloadGPT-5.5Fugu Ultra
Support chatbot

1.2K in / 400 out × 200K requests

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

8K in / 600 out × 100K requests

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

40K in / 4K out × 20K requests

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

20K in / 1.5K out × 50K requests

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

500 in / 20 out × 5M requests

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

GPT-5.5 vs Fugu Ultra FAQ

Which is better, GPT-5.5 or Fugu Ultra?

GPT-5.5 and Fugu Ultra are priced within ~10% of each other. 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 GPT-5.5 cheaper than Fugu Ultra?

They cost about the same. Both land near $11.25 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.

Does GPT-5.5 or Fugu Ultra have a bigger context window?

They are effectively the same — 1.1M for GPT-5.5 and 1M for Fugu Ultra.

Do GPT-5.5 and Fugu Ultra support prompt caching?

Both publish a cached-input rate: $0.500 per million for GPT-5.5 and $0.500 for Fugu Ultra, against full input rates of $5.00 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.