// head_to_head
GPT-4 Turbo vs GPT-5.5
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.
GPT-5.5 is the cheaper of the two; neither can be ranked on quality here.
GPT-4 Turbo 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-4 Turbo
- Blended / 1M
- $15.00
- Context
- 128K
- Released
- Apr 9, 2024
- Overall score
- Not evaluated
openai
GPT-5.5
- Blended / 1M
- $11.25
- Context
- 1.1M
- Released
- Apr 24, 2026
- Overall score
- 80.2
Specs and pricing
| Metric | GPT-4 Turbo | GPT-5.5 |
|---|---|---|
| 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. | $15.00 | $11.25win |
| Input price / 1M | $10.00 | $5.00win |
| 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 |
| Context window | 128K | 1.1Mwin |
| Max output tokens | 4K | 128Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-4 Turbo on top, GPT-5.5 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 | GPT-4 Turbo | GPT-5.5 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $4,800/mo | $3,276/mo |
| RAG assistant 8K in / 600 out × 100K requests | $9,800/mo | $4,000/mo |
| Coding agent 40K in / 4K out × 20K requests | $10,400/mo | $3,880/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $12,250/mo | $7,025/mo |
| Bulk classification 500 in / 20 out × 5M requests | $28,000/mo | $13,250/mo |
Which should you pick?
You need to fit large documents in one call
GPT-5.5
Wider context window — 1.1M against 128K.
GPT-4 Turbo vs GPT-5.5 FAQ
Which is better, GPT-4 Turbo or GPT-5.5?
GPT-5.5 is the cheaper of the two; neither can be ranked on quality here. GPT-4 Turbo 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-4 Turbo cheaper than GPT-5.5?
GPT-5.5 is cheaper. On a 3:1 input:output blend, GPT-4 Turbo lists at $15.00 per million tokens and GPT-5.5 at $11.25 — GPT-5.5 is 33% cheaper. Input and output are priced separately — GPT-4 Turbo charges $10.00 in and $30.00 out, GPT-5.5 charges $5.00 and $30.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does GPT-4 Turbo or GPT-5.5 have a bigger context window?
GPT-5.5 has the larger context window: 128K for GPT-4 Turbo against 1.1M for GPT-5.5. 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 GPT-4 Turbo and GPT-5.5 support prompt caching?
GPT-5.5 publishes a cached-input rate of $0.500 per million tokens against a full input rate of $5.00. The catalogue lists no separate cached rate for GPT-4 Turbo, 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.