// head_to_head
Pareto 26.10 Preview vs GLM 5.2
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.
Pareto 26.10 Preview and GLM 5.2 are priced within ~10% of each other.
Pareto 26.10 Preview 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.
unbiased
Pareto 26.10 Preview
- Blended / 1M
- $1.40
- Context
- 1.0M
- Released
- Oct 1, 2026
- Overall score
- Not evaluated
z-ai
GLM 5.2
- Blended / 1M
- $1.30
- Context
- 1.0M
- Released
- Jun 16, 2026
- Overall score
- 73.2
Specs and pricing
| Metric | Pareto 26.10 Preview | GLM 5.2 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 73.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.1260 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.40 | $1.30 |
| Input price / 1M | $0.800 | $0.410win |
| Output price / 1M | $3.20win | $3.99 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.030win | $0.260 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 131K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Pareto 26.10 Preview on top, GLM 5.2 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 | Pareto 26.10 Preview | GLM 5.2 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $392.56/mo | $406.80/mo |
| RAG assistant 8K in / 600 out × 100K requests | $524.00/mo | $507.40/mo |
| Coding agent 40K in / 4K out × 20K requests | $464.80/mo | $563.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,002/mo | $701.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,935/mo | $1,349/mo |
Pareto 26.10 Preview vs GLM 5.2 FAQ
Which is better, Pareto 26.10 Preview or GLM 5.2?
Pareto 26.10 Preview and GLM 5.2 are priced within ~10% of each other. Pareto 26.10 Preview 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 Pareto 26.10 Preview cheaper than GLM 5.2?
They cost about the same. Both land near $1.40 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Pareto 26.10 Preview or GLM 5.2 have a bigger context window?
They are effectively the same — 1.0M for Pareto 26.10 Preview and 1.0M for GLM 5.2.
Do Pareto 26.10 Preview and GLM 5.2 support prompt caching?
Both publish a cached-input rate: $0.030 per million for Pareto 26.10 Preview and $0.260 for GLM 5.2, against full input rates of $0.800 and $0.410. 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.