// head_to_head
Qwen2.5 VL 72B Instruct 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.
Qwen2.5 VL 72B Instruct is the cheaper of the two; neither can be ranked on quality here.
Qwen2.5 VL 72B Instruct 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.
qwen
Qwen2.5 VL 72B Instruct
- Blended / 1M
- $0.850
- Context
- 128K
- Released
- Feb 1, 2025
- Overall score
- Not evaluated
z-ai
GLM 5.3
- Blended / 1M
- $1.29
- Context
- 1.3M
- Released
- Aug 18, 2026
- Overall score
- 76.1
Specs and pricing
| Metric | Qwen2.5 VL 72B Instruct | GLM 5.3 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 76.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.2460 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.850win | $1.29 |
| Input price / 1M | $0.800 | $0.840 |
| Output price / 1M | $1.00win | $2.64 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.400 | $0.156win |
| Context window | 128K | 1.3Mwin |
| Max output tokens | 115K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen2.5 VL 72B Instruct 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 | Qwen2.5 VL 72B Instruct | GLM 5.3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $243.20/mo | $363.55/mo |
| RAG assistant 8K in / 600 out × 100K requests | $540.00/mo | $556.80/mo |
| Coding agent 40K in / 4K out × 20K requests | $496.00/mo | $500.16/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $855.00/mo | $1,004/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,900/mo | $2,022/mo |
Which should you pick?
You need to fit large documents in one call
GLM 5.3
Wider context window — 1.3M against 128K.
You are cost-constrained
Qwen2.5 VL 72B Instruct
Cheaper on blended list price at $0.850 per million tokens.
Qwen2.5 VL 72B Instruct vs GLM 5.3 FAQ
Which is better, Qwen2.5 VL 72B Instruct or GLM 5.3?
Qwen2.5 VL 72B Instruct is the cheaper of the two; neither can be ranked on quality here. Qwen2.5 VL 72B Instruct 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 Qwen2.5 VL 72B Instruct cheaper than GLM 5.3?
Qwen2.5 VL 72B Instruct is cheaper. On a 3:1 input:output blend, Qwen2.5 VL 72B Instruct lists at $0.850 per million tokens and GLM 5.3 at $1.29 — Qwen2.5 VL 72B Instruct is 1.5× cheaper. Input and output are priced separately — Qwen2.5 VL 72B Instruct charges $0.800 in and $1.00 out, GLM 5.3 charges $0.840 and $2.64 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Qwen2.5 VL 72B Instruct or GLM 5.3 have a bigger context window?
GLM 5.3 has the larger context window: 128K for Qwen2.5 VL 72B Instruct against 1.3M for GLM 5.3. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Qwen2.5 VL 72B Instruct and GLM 5.3 support prompt caching?
Both publish a cached-input rate: $0.400 per million for Qwen2.5 VL 72B Instruct and $0.156 for GLM 5.3, against full input rates of $0.800 and $0.840. 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.