// head_to_head
Muse Wonder 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.
Muse Wonder scores higher, SpaceXAI: Grok 4.7 costs less — it depends on your workload.
Muse Wonder is ahead by 4.0 points overall, and SpaceXAI: Grok 4.7 lists 2.7× cheaper per blended million tokens. Whether 4.0 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: SpaceXAI: Grok 4.7 has the lower sticker price, but Muse Wonder earns each point of capability for less — $0.0201 against $0.0291 — because per-token rates do not predict how many tokens a model actually spends on a task.
meta
Muse Wonder
- Blended / 1M
- $8.00
- Context
- 1.0M
- Released
- Oct 12, 2026
- Overall score
- 74.2
x-ai
SpaceXAI: Grok 4.7
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Sep 21, 2026
- Overall score
- 70.1
Specs and pricing
| Metric | Muse Wonder | SpaceXAI: Grok 4.7 |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 74.2win | 70.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0201win | $0.0291 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $8.00 | $3.00win |
| Input price / 1M | $4.00 | $2.00win |
| Output price / 1M | $20.00 | $6.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200win | $0.500 |
| Context window | 1.0Mwin | 500K |
| Max output tokens | 944Kwin | 450K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Muse Wonder 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 | Muse Wonder | SpaceXAI: Grok 4.7 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $2,286/mo | $852.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $2,880/mo | $1,360/mo |
| Coding agent 40K in / 4K out × 20K requests | $2,672/mo | $1,240/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $5,310/mo | $2,375/mo |
| Bulk classification 500 in / 20 out × 5M requests | $10,100/mo | $4,850/mo |
Which should you pick?
You are running this at volume
Muse Wonder
Lowest measured cost per point of capability at $0.0201 per point — the gap compounds with every request.
Quality matters more than the bill
Muse Wonder
Highest overall LiveBench score of the two at 74.2.
The workload is coding or agentic work
Muse Wonder
Leads on long-horizon agentic — 52.6 against 46.1.
You need to fit large documents in one call
Muse Wonder
Wider context window — 1.0M against 500K.
You are cost-constrained
SpaceXAI: Grok 4.7
Cheaper on blended list price at $3.00 per million tokens.
Muse Wonder vs SpaceXAI: Grok 4.7 FAQ
Which is better, Muse Wonder or SpaceXAI: Grok 4.7?
Muse Wonder scores higher, SpaceXAI: Grok 4.7 costs less — it depends on your workload. Muse Wonder is ahead by 4.0 points overall, and SpaceXAI: Grok 4.7 lists 2.7× cheaper per blended million tokens. Whether 4.0 points is worth that depends on how much a wrong answer costs you. The two cost measures disagree here, which is worth knowing: SpaceXAI: Grok 4.7 has the lower sticker price, but Muse Wonder earns each point of capability for less — $0.0201 against $0.0291 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Muse Wonder cheaper than SpaceXAI: Grok 4.7?
SpaceXAI: Grok 4.7 is cheaper. On a 3:1 input:output blend, Muse Wonder lists at $8.00 per million tokens and SpaceXAI: Grok 4.7 at $3.00 — SpaceXAI: Grok 4.7 is 2.7× cheaper. Input and output are priced separately — Muse Wonder charges $4.00 in and $20.00 out, SpaceXAI: Grok 4.7 charges $2.00 and $6.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Muse Wonder vs SpaceXAI: Grok 4.7: which scores higher on benchmarks?
Muse Wonder scores 74.2 and SpaceXAI: Grok 4.7 scores 70.1 overall on LiveBench, the weighted mean of its seven categories. That is a 4.0-point lead for Muse Wonder. 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, Muse Wonder or SpaceXAI: Grok 4.7?
Muse Wonder. 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. Muse Wonder works out at $0.0201 per point and SpaceXAI: Grok 4.7 at $0.0291.
Does Muse Wonder or SpaceXAI: Grok 4.7 have a bigger context window?
Muse Wonder has the larger context window: 1.0M for Muse Wonder against 500K for SpaceXAI: Grok 4.7. 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 Muse Wonder and SpaceXAI: Grok 4.7 support prompt caching?
Both publish a cached-input rate: $0.200 per million for Muse Wonder and $0.500 for SpaceXAI: Grok 4.7, against full input rates of $4.00 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 2026-10-25, 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.