// head_to_head
GPT-5.3-Codex vs GPT-5.4
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-5.3-Codex is the cheaper of the two; neither can be ranked on quality here.
GPT-5.3-Codex 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-5.3-Codex
- Blended / 1M
- $4.81
- Context
- 400K
- Released
- Feb 24, 2026
- Overall score
- Not evaluated
openai
GPT-5.4
- Blended / 1M
- $5.63
- Context
- 1.1M
- Released
- Mar 5, 2026
- Overall score
- 78.0
Specs and pricing
| Metric | GPT-5.3-Codex | GPT-5.4 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 78.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.2198 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.81win | $5.63 |
| Input price / 1M | $1.75win | $2.50 |
| Output price / 1M | $14.00 | $15.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.175win | $0.250 |
| Context window | 400K | 1.1Mwin |
| 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 — GPT-5.3-Codex on top, GPT-5.4 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-5.3-Codex | GPT-5.4 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,427/mo | $1,638/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,610/mo | $2,000/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,638/mo | $1,940/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,721/mo | $3,513/mo |
| Bulk classification 500 in / 20 out × 5M requests | $4,988/mo | $6,625/mo |
Which should you pick?
You need to fit large documents in one call
GPT-5.4
Wider context window — 1.1M against 400K.
You are cost-constrained
GPT-5.3-Codex
Cheaper on blended list price at $4.81 per million tokens.
GPT-5.3-Codex vs GPT-5.4 FAQ
Which is better, GPT-5.3-Codex or GPT-5.4?
GPT-5.3-Codex is the cheaper of the two; neither can be ranked on quality here. GPT-5.3-Codex 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-5.3-Codex cheaper than GPT-5.4?
GPT-5.3-Codex is cheaper. On a 3:1 input:output blend, GPT-5.3-Codex lists at $4.81 per million tokens and GPT-5.4 at $5.63 — GPT-5.3-Codex is 17% cheaper. Input and output are priced separately — GPT-5.3-Codex charges $1.75 in and $14.00 out, GPT-5.4 charges $2.50 and $15.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does GPT-5.3-Codex or GPT-5.4 have a bigger context window?
GPT-5.4 has the larger context window: 400K for GPT-5.3-Codex against 1.1M for GPT-5.4. 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-5.3-Codex and GPT-5.4 support prompt caching?
Both publish a cached-input rate: $0.175 per million for GPT-5.3-Codex and $0.250 for GPT-5.4, against full input rates of $1.75 and $2.50. 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.
- 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.