// head_to_head
Gemini 3.7 Flash vs Muse Spark Open
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.
Gemini 3.7 Flash is the cheaper of the two; neither can be ranked on quality here.
Muse Spark Open 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.
Gemini 3.7 Flash
- Blended / 1M
- $1.50
- Context
- 1.0M
- Released
- Aug 13, 2026
- Overall score
- 67.2
meta
Muse Spark Open
- Blended / 1M
- $2.00
- Context
- 1.0M
- Released
- Oct 15, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Gemini 3.7 Flash | Muse Spark Open |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 67.2 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.5935 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.50win | $2.00 |
| Input price / 1M | $0.750win | $1.25 |
| Output price / 1M | $3.75win | $4.25 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.075win | $0.150 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 66K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.7 Flash on top, Muse Spark Open 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 | Gemini 3.7 Flash | Muse Spark Open |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $431.40/mo | $560.80/mo |
| RAG assistant 8K in / 600 out × 100K requests | $555.00/mo | $815.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $522.00/mo | $724.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $997.50/mo | $1,514/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,912/mo | $3,000/mo |
Which should you pick?
You are cost-constrained
Gemini 3.7 Flash
Cheaper on blended list price at $1.50 per million tokens.
Gemini 3.7 Flash vs Muse Spark Open FAQ
Which is better, Gemini 3.7 Flash or Muse Spark Open?
Gemini 3.7 Flash is the cheaper of the two; neither can be ranked on quality here. Muse Spark Open 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 Gemini 3.7 Flash cheaper than Muse Spark Open?
Gemini 3.7 Flash is cheaper. On a 3:1 input:output blend, Gemini 3.7 Flash lists at $1.50 per million tokens and Muse Spark Open at $2.00 — Gemini 3.7 Flash is 33% cheaper. Input and output are priced separately — Gemini 3.7 Flash charges $0.750 in and $3.75 out, Muse Spark Open charges $1.25 and $4.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Gemini 3.7 Flash or Muse Spark Open have a bigger context window?
They are effectively the same — 1.0M for Gemini 3.7 Flash and 1.0M for Muse Spark Open.
Do Gemini 3.7 Flash and Muse Spark Open support prompt caching?
Both publish a cached-input rate: $0.075 per million for Gemini 3.7 Flash and $0.150 for Muse Spark Open, against full input rates of $0.750 and $1.25. 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 2026-09-02, 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.