// head_to_head
Claude Opus 5.5 vs GPT-5 Pro
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.
Claude Opus 5.5 is the cheaper of the two; neither can be ranked on quality here.
GPT-5 Pro 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 Opus 5.5
- Blended / 1M
- $8.00
- Context
- 1M
- Released
- Sep 22, 2026
- Overall score
- 83.2
openai
GPT-5 Pro
- Blended / 1M
- $41.25
- Context
- 400K
- Released
- Oct 6, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Claude Opus 5.5 | GPT-5 Pro |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 83.2 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.4474 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $8.00win | $41.25 |
| Input price / 1M | $4.00win | $15.00 |
| Output price / 1M | $20.00win | $120.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | — |
| Context window | 1Mwin | 400K |
| Max output tokens | 128K | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Opus 5.5 on top, GPT-5 Pro below, both out of 100.
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.
| Workload | Claude Opus 5.5 | GPT-5 Pro |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $2,286/mo | $13,200/mo |
| RAG assistant 8K in / 600 out × 100K requests | $2,880/mo | $19,200/mo |
| Coding agent 40K in / 4K out × 20K requests | $2,672/mo | $21,600/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $5,310/mo | $24,000/mo |
| Bulk classification 500 in / 20 out × 5M requests | $10,100/mo | $49,500/mo |
Which should you pick?
You need to fit large documents in one call
Claude Opus 5.5
Wider context window — 1M against 400K.
Claude Opus 5.5 vs GPT-5 Pro FAQ
Which is better, Claude Opus 5.5 or GPT-5 Pro?
Claude Opus 5.5 is the cheaper of the two; neither can be ranked on quality here. GPT-5 Pro 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 Opus 5.5 cheaper than GPT-5 Pro?
Claude Opus 5.5 is cheaper. On a 3:1 input:output blend, Claude Opus 5.5 lists at $8.00 per million tokens and GPT-5 Pro at $41.25 — Claude Opus 5.5 is 5.2× cheaper. Input and output are priced separately — Claude Opus 5.5 charges $4.00 in and $20.00 out, GPT-5 Pro charges $15.00 and $120.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Claude Opus 5.5 or GPT-5 Pro have a bigger context window?
Claude Opus 5.5 has the larger context window: 1M for Claude Opus 5.5 against 400K for GPT-5 Pro. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Claude Opus 5.5 and GPT-5 Pro support prompt caching?
Claude Opus 5.5 publishes a cached-input rate of $0.200 per million tokens against a full input rate of $4.00. The catalogue lists no separate cached rate for GPT-5 Pro, which means the provider does not price it separately here — not that caching is unavailable.
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.
- Scores — LiveBench 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.