// head_to_head
GPT-5 Image Mini vs SpaceXAI: Grok 4.7
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 Image Mini and SpaceXAI: Grok 4.7 are priced within ~10% of each other.
GPT-5 Image Mini 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 Image Mini
- Blended / 1M
- $2.38
- Context
- 400K
- Released
- Oct 16, 2025
- Overall score
- Not evaluated
x-ai
SpaceXAI: Grok 4.7
- Blended / 1M
- $2.40
- Context
- 500K
- Released
- Sep 21, 2026
- Overall score
- 77.4
Specs and pricing
| Metric | GPT-5 Image Mini | SpaceXAI: Grok 4.7 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 77.4 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.4062 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.38 | $2.40 |
| Input price / 1M | $2.50 | $1.60win |
| Output price / 1M | $2.00win | $4.80 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.250win | $0.400 |
| Context window | 400K | 500K |
| Max output tokens | 128K | 450Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5 Image Mini on top, SpaceXAI: Grok 4.7 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 Image Mini | SpaceXAI: Grok 4.7 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $598.00/mo | $681.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,220/mo | $1,088/mo |
| Coding agent 40K in / 4K out × 20K requests | $900.00/mo | $992.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,538/mo | $1,900/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,325/mo | $3,880/mo |
GPT-5 Image Mini vs SpaceXAI: Grok 4.7 FAQ
Which is better, GPT-5 Image Mini or SpaceXAI: Grok 4.7?
GPT-5 Image Mini and SpaceXAI: Grok 4.7 are priced within ~10% of each other. GPT-5 Image Mini 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 Image Mini cheaper than SpaceXAI: Grok 4.7?
They cost about the same. Both land near $2.38 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does GPT-5 Image Mini or SpaceXAI: Grok 4.7 have a bigger context window?
They are effectively the same — 400K for GPT-5 Image Mini and 500K for SpaceXAI: Grok 4.7.
Do GPT-5 Image Mini and SpaceXAI: Grok 4.7 support prompt caching?
Both publish a cached-input rate: $0.250 per million for GPT-5 Image Mini and $0.400 for SpaceXAI: Grok 4.7, against full input rates of $2.50 and $1.60. 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.