// head_to_head
GPT-5.4 Nano vs Ox Alpha
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.
Effectively the same quality — Ox Alpha is the cheaper way to get it.
The two are within 0.3 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. When quality ties, cost is the whole decision. Ox Alpha also leads on measured cost per point of capability, at $0.0000 per point.
openai
GPT-5.4 Nano
- Blended / 1M
- $0.463
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 69.6
stealth
Ox Alpha
- Blended / 1M
- Free
- Context
- 1.0M
- Released
- Aug 20, 2026
- Overall score
- 69.2
Specs and pricing
| Metric | GPT-5.4 Nano | Ox Alpha |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 69.6 | 69.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0500 | $0.0000win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.463 | Freewin |
| Input price / 1M | $0.200 | Freewin |
| Output price / 1M | $1.25 | Freewin |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020 | — |
| Context window | 400K | 1.0Mwin |
| Max output tokens | 128K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.4 Nano on top, Ox Alpha 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-5.4 Nano | Ox Alpha |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $135.04/mo | $0/mo |
| RAG assistant 8K in / 600 out × 100K requests | $163.00/mo | $0/mo |
| Coding agent 40K in / 4K out × 20K requests | $159.20/mo | $0/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $284.75/mo | $0/mo |
| Bulk classification 500 in / 20 out × 5M requests | $535.00/mo | $0/mo |
Which should you pick?
You are running this at volume
Ox Alpha
Lowest measured cost per point of capability at $0.0000 per point — the gap compounds with every request.
The workload is coding or agentic work
Ox Alpha
Leads on agentic coding — 52.6 against 46.8.
You need to fit large documents in one call
Ox Alpha
Wider context window — 1.0M against 400K.
GPT-5.4 Nano vs Ox Alpha FAQ
Which is better, GPT-5.4 Nano or Ox Alpha?
Effectively the same quality — Ox Alpha is the cheaper way to get it. The two are within 0.3 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. When quality ties, cost is the whole decision. Ox Alpha also leads on measured cost per point of capability, at $0.0000 per point.
Is GPT-5.4 Nano cheaper than Ox Alpha?
Ox Alpha is cheaper. On a 3:1 input:output blend, GPT-5.4 Nano lists at $0.463 per million tokens and Ox Alpha at Free. Input and output are priced separately — GPT-5.4 Nano charges $0.200 in and $1.25 out, Ox Alpha charges Free and Free — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-5.4 Nano vs Ox Alpha: which scores higher on benchmarks?
GPT-5.4 Nano scores 69.6 and Ox Alpha scores 69.2 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, GPT-5.4 Nano or Ox Alpha?
Ox Alpha. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. GPT-5.4 Nano works out at $0.0500 per point and Ox Alpha at $0.0000.
Does GPT-5.4 Nano or Ox Alpha have a bigger context window?
Ox Alpha has the larger context window: 400K for GPT-5.4 Nano against 1.0M for Ox Alpha. 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-5.4 Nano and Ox Alpha support prompt caching?
GPT-5.4 Nano publishes a cached-input rate of $0.020 per million tokens against a full input rate of $0.200. The catalogue lists no separate cached rate for Ox Alpha, 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.