// head_to_head
GPT-5.4 Mini vs SpaceXAI: Grok 4.5
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.
SpaceXAI: Grok 4.5 scores higher, GPT-5.4 Mini costs less — it depends on your workload.
SpaceXAI: Grok 4.5 is ahead by 9.4 points overall, and GPT-5.4 Mini lists 1.8× cheaper per blended million tokens. Whether 9.4 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GPT-5.4 Mini has the lower sticker price, but SpaceXAI: Grok 4.5 earns each point of capability for less — $0.0720 against $0.1871 — because per-token rates do not predict how many tokens a model actually spends on a task.
openai
GPT-5.4 Mini
- Blended / 1M
- $1.69
- Context
- 400K
- Released
- Mar 17, 2026
- Overall score
- 66.4
x-ai
SpaceXAI: Grok 4.5
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Jul 8, 2026
- Overall score
- 75.8
Specs and pricing
| Metric | GPT-5.4 Mini | SpaceXAI: Grok 4.5 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 66.4 | 75.8win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.1871 | $0.0720win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.69win | $3.00 |
| Input price / 1M | $0.750win | $2.00 |
| Output price / 1M | $4.50win | $6.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.075win | $0.300 |
| Context window | 400K | 500K |
| Max output tokens | 128K | — |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5.4 Mini on top, SpaceXAI: Grok 4.5 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-5.4 Mini | SpaceXAI: Grok 4.5 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $491.40/mo | $837.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $600.00/mo | $1280.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $582.00/mo | $1128.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1053.75/mo | $2365.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1987.50/mo | $4750.00/mo |
Which should you pick?
You are running this at volume
SpaceXAI: Grok 4.5
Lowest measured cost per point of capability at $0.0720 per point — the gap compounds with every request.
Quality matters more than the bill
SpaceXAI: Grok 4.5
Highest overall LiveBench score of the two at 75.8.
The workload is coding or agentic work
SpaceXAI: Grok 4.5
Leads on agentic coding — 56.5 against 41.7.
You are cost-constrained
GPT-5.4 Mini
Cheaper on blended list price at $1.69 per million tokens.
GPT-5.4 Mini vs SpaceXAI: Grok 4.5 FAQ
Which is better, GPT-5.4 Mini or SpaceXAI: Grok 4.5?
SpaceXAI: Grok 4.5 scores higher, GPT-5.4 Mini costs less — it depends on your workload. SpaceXAI: Grok 4.5 is ahead by 9.4 points overall, and GPT-5.4 Mini lists 1.8× cheaper per blended million tokens. Whether 9.4 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: GPT-5.4 Mini has the lower sticker price, but SpaceXAI: Grok 4.5 earns each point of capability for less — $0.0720 against $0.1871 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is GPT-5.4 Mini cheaper than SpaceXAI: Grok 4.5?
GPT-5.4 Mini is cheaper. On a 3:1 input:output blend, GPT-5.4 Mini lists at $1.69 per million tokens and SpaceXAI: Grok 4.5 at $3.00 — GPT-5.4 Mini is 1.8× cheaper. Input and output are priced separately — GPT-5.4 Mini charges $0.750 in and $4.50 out, SpaceXAI: Grok 4.5 charges $2.00 and $6.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-5.4 Mini vs SpaceXAI: Grok 4.5: which scores higher on benchmarks?
GPT-5.4 Mini scores 66.4 and SpaceXAI: Grok 4.5 scores 75.8 overall on LiveBench, the mean of its seven categories. That is a 9.4-point lead for SpaceXAI: Grok 4.5. 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 Mini or SpaceXAI: Grok 4.5?
SpaceXAI: Grok 4.5. 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 Mini works out at $0.1871 per point and SpaceXAI: Grok 4.5 at $0.0720.
Does GPT-5.4 Mini or SpaceXAI: Grok 4.5 have a bigger context window?
They are effectively the same — 400K for GPT-5.4 Mini and 500K for SpaceXAI: Grok 4.5.
Do GPT-5.4 Mini and SpaceXAI: Grok 4.5 support prompt caching?
Both publish a cached-input rate: $0.075 per million for GPT-5.4 Mini and $0.300 for SpaceXAI: Grok 4.5, against full input rates of $0.750 and $2.00. 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.
- 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.