// head_to_head
Llama 4 Scout vs Muse Spark 1.3
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.
Llama 4 Scout is the cheaper of the two; neither can be ranked on quality here.
Llama 4 Scout 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.
meta-llama
Llama 4 Scout
- Blended / 1M
- $0.150
- Context
- 1.3M
- Released
- Apr 5, 2025
- Overall score
- Not evaluated
meta
Muse Spark 1.3
- Blended / 1M
- $2.00
- Context
- 1.0M
- Released
- Sep 2, 2026
- Overall score
- 81.6
Specs and pricing
| Metric | Llama 4 Scout | Muse Spark 1.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 81.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1210 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.150win | $2.00 |
| Input price / 1M | $0.100win | $1.25 |
| Output price / 1M | $0.300win | $4.25 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.150 |
| Context window | 1.3M | 1.0M |
| Max output tokens | 16K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Llama 4 Scout on top, Muse Spark 1.3 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 | Llama 4 Scout | Muse Spark 1.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $48.00/mo | $560.80/mo |
| RAG assistant 8K in / 600 out × 100K requests | $98.00/mo | $815.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $104.00/mo | $724.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $122.50/mo | $1,514/mo |
| Bulk classification 500 in / 20 out × 5M requests | $280.00/mo | $3,000/mo |
Which should you pick?
You are cost-constrained
Llama 4 Scout
Cheaper on blended list price at $0.150 per million tokens.
Llama 4 Scout vs Muse Spark 1.3 FAQ
Which is better, Llama 4 Scout or Muse Spark 1.3?
Llama 4 Scout is the cheaper of the two; neither can be ranked on quality here. Llama 4 Scout 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 Llama 4 Scout cheaper than Muse Spark 1.3?
Llama 4 Scout is cheaper. On a 3:1 input:output blend, Llama 4 Scout lists at $0.150 per million tokens and Muse Spark 1.3 at $2.00 — Llama 4 Scout is 13× cheaper. Input and output are priced separately — Llama 4 Scout charges $0.100 in and $0.300 out, Muse Spark 1.3 charges $1.25 and $4.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Llama 4 Scout or Muse Spark 1.3 have a bigger context window?
They are effectively the same — 1.3M for Llama 4 Scout and 1.0M for Muse Spark 1.3.
Do Llama 4 Scout and Muse Spark 1.3 support prompt caching?
Muse Spark 1.3 publishes a cached-input rate of $0.150 per million tokens against a full input rate of $1.25. The catalogue lists no separate cached rate for Llama 4 Scout, 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.