// head_to_head
Muse Spark 1.2 vs Step 5 Preview
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.
Effectively the same quality — Step 5 Preview is the cheaper way to get it.
The two are within 0.2 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Step 5 Preview lists 40% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: Step 5 Preview has the lower sticker price, but Muse Spark 1.2 earns each point of capability for less — $0.5350 against $4.08 — because per-token rates do not predict how many tokens a model actually spends on a task.
meta
Muse Spark 1.2
- Blended / 1M
- $2.00
- Context
- 1.0M
- Released
- Aug 5, 2026
- Overall score
- 61.8
stepfun
Step 5 Preview
- Blended / 1M
- $1.43
- Context
- 1.0M
- Released
- Oct 8, 2026
- Overall score
- 62.0
Specs and pricing
| Metric | Muse Spark 1.2 | Step 5 Preview |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 61.8 | 62.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.5350win | $4.08 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.00 | $1.43win |
| Input price / 1M | $1.25 | $1.00win |
| Output price / 1M | $4.25 | $2.70win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.150 | $0.050win |
| Context window | 1.0M | 1.0M |
| Max output tokens | 944Kwin | 64K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Muse Spark 1.2 on top, Step 5 Preview 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 Spark 1.2 | Step 5 Preview |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $560.80/mo | $387.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $815.00/mo | $582.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $724.00/mo | $484.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,514/mo | $1,155/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3,000/mo | $2,295/mo |
Which should you pick?
You are running this at volume
Muse Spark 1.2
Lowest measured cost per point of capability at $0.5350 per point — the gap compounds with every request.
The workload is coding or agentic work
Step 5 Preview
Leads on coding — 85.6 against 80.6.
Muse Spark 1.2 vs Step 5 Preview FAQ
Which is better, Muse Spark 1.2 or Step 5 Preview?
Effectively the same quality — Step 5 Preview is the cheaper way to get it. The two are within 0.2 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Step 5 Preview lists 40% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: Step 5 Preview has the lower sticker price, but Muse Spark 1.2 earns each point of capability for less — $0.5350 against $4.08 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Muse Spark 1.2 cheaper than Step 5 Preview?
Step 5 Preview is cheaper. On a 3:1 input:output blend, Muse Spark 1.2 lists at $2.00 per million tokens and Step 5 Preview at $1.43 — Step 5 Preview is 40% cheaper. Input and output are priced separately — Muse Spark 1.2 charges $1.25 in and $4.25 out, Step 5 Preview charges $1.00 and $2.70 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Muse Spark 1.2 vs Step 5 Preview: which scores higher on benchmarks?
Muse Spark 1.2 scores 61.8 and Step 5 Preview scores 62.0 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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 Spark 1.2 or Step 5 Preview?
Muse Spark 1.2. 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 Spark 1.2 works out at $0.5350 per point and Step 5 Preview at $4.08.
Does Muse Spark 1.2 or Step 5 Preview have a bigger context window?
They are effectively the same — 1.0M for Muse Spark 1.2 and 1.0M for Step 5 Preview.
Do Muse Spark 1.2 and Step 5 Preview support prompt caching?
Both publish a cached-input rate: $0.150 per million for Muse Spark 1.2 and $0.050 for Step 5 Preview, against full input rates of $1.25 and $1.00. 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 2026-09-02, 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.