// head_to_head
Pareto 26.10 vs GLM 5.3 Flash
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.
GLM 5.3 Flash is the cheaper of the two; neither can be ranked on quality here.
Pareto 26.10 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
- Blended / 1M
- $1.40
- Context
- 1.0M
- Released
- Oct 15, 2026
- Overall score
- Not evaluated
z-ai
GLM 5.3 Flash
- Blended / 1M
- $0.053
- Context
- 1.0M
- Released
- Aug 26, 2026
- Overall score
- 62.1
Specs and pricing
| Metric | Pareto 26.10 | GLM 5.3 Flash |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 62.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0018 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.40 | $0.053win |
| Input price / 1M | $0.800 | $0.046win |
| Output price / 1M | $3.20 | $0.074win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.030 | $0.030 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 131K | 131K |
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 on top, GLM 5.3 Flash 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 | GLM 5.3 Flash |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $392.56/mo | $15.82/mo |
| RAG assistant 8K in / 600 out × 100K requests | $524.00/mo | $34.90/mo |
| Coding agent 40K in / 4K out × 20K requests | $464.80/mo | $33.78/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1,002/mo | $50.92/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,935/mo | $114.77/mo |
Which should you pick?
You are cost-constrained
GLM 5.3 Flash
Cheaper on blended list price at $0.053 per million tokens.
Pareto 26.10 vs GLM 5.3 Flash FAQ
Which is better, Pareto 26.10 or GLM 5.3 Flash?
GLM 5.3 Flash is the cheaper of the two; neither can be ranked on quality here. Pareto 26.10 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 cheaper than GLM 5.3 Flash?
GLM 5.3 Flash is cheaper. On a 3:1 input:output blend, Pareto 26.10 lists at $1.40 per million tokens and GLM 5.3 Flash at $0.053 — GLM 5.3 Flash is 26× cheaper. Input and output are priced separately — Pareto 26.10 charges $0.800 in and $3.20 out, GLM 5.3 Flash charges $0.046 and $0.074 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Pareto 26.10 or GLM 5.3 Flash have a bigger context window?
They are effectively the same — 1.0M for Pareto 26.10 and 1.0M for GLM 5.3 Flash.
Do Pareto 26.10 and GLM 5.3 Flash support prompt caching?
Both publish a cached-input rate: $0.030 per million for Pareto 26.10 and $0.030 for GLM 5.3 Flash, against full input rates of $0.800 and $0.046. 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 2026-10-25, 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.