// head_to_head
Muse Spark 1.2 vs GLM 5.3
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.
Muse Spark 1.2 is the better model, at roughly the same price.
Muse Spark 1.2 leads by 1.8 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call.
meta
Muse Spark 1.2
- Blended / 1M
- $2.00
- Context
- 1.0M
- Released
- Aug 5, 2026
- Overall score
- 78.0
z-ai
GLM 5.3
- Blended / 1M
- $2.15
- Context
- 1.0M
- Released
- Aug 18, 2026
- Overall score
- 76.1
Specs and pricing
| Metric | Muse Spark 1.2 | GLM 5.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 78.0win | 76.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.2199 | $0.2460 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.00 | $2.15 |
| Input price / 1M | $1.25win | $1.40 |
| Output price / 1M | $4.25 | $4.40 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.150win | $0.260 |
| Context window | 1.0M | 1.0M |
| Max output tokens | — | 131K |
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, GLM 5.3 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 | Muse Spark 1.2 | GLM 5.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $560.80/mo | $605.92/mo |
| RAG assistant 8K in / 600 out × 100K requests | $815.00/mo | $928.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $724.00/mo | $833.60/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1513.75/mo | $1673.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3000.00/mo | $3370.00/mo |
Which should you pick?
Quality matters more than the bill
Muse Spark 1.2
Highest overall LiveBench score of the two at 78.0.
The workload is coding or agentic work
GLM 5.3
Leads on agentic coding — 60.9 against 57.6.
Muse Spark 1.2 vs GLM 5.3 FAQ
Which is better, Muse Spark 1.2 or GLM 5.3?
Muse Spark 1.2 is the better model, at roughly the same price. Muse Spark 1.2 leads by 1.8 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call.
Is Muse Spark 1.2 cheaper than GLM 5.3?
They cost about the same. Both land near $2.00 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Muse Spark 1.2 vs GLM 5.3: which scores higher on benchmarks?
Muse Spark 1.2 scores 78.0 and GLM 5.3 scores 76.1 overall on LiveBench, the mean of its seven categories. That is a 1.8-point lead for Muse Spark 1.2. 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 GLM 5.3?
They are close. Muse Spark 1.2 costs $0.2199 per point of overall capability and GLM 5.3 costs $0.2460, a difference small enough that workload shape will matter more than the rate.
Does Muse Spark 1.2 or GLM 5.3 have a bigger context window?
They are effectively the same — 1.0M for Muse Spark 1.2 and 1.0M for GLM 5.3.
Do Muse Spark 1.2 and GLM 5.3 support prompt caching?
Both publish a cached-input rate: $0.150 per million for Muse Spark 1.2 and $0.260 for GLM 5.3, against full input rates of $1.25 and $1.40. 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.