// head_to_head
Qwen3.8 27B vs GLM 4.5 Air
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 4.5 Air is the cheaper of the two; neither can be ranked on quality here.
GLM 4.5 Air 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
Qwen3.8 27B
- Blended / 1M
- $0.342
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 58.4
z-ai
GLM 4.5 Air
- Blended / 1M
- $0.310
- Context
- 131K
- Released
- Jul 25, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Qwen3.8 27B | GLM 4.5 Air |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 58.4 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0084 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.342 | $0.310win |
| Input price / 1M | $0.039win | $0.130 |
| Output price / 1M | $1.25 | $0.850win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0039win | $0.025 |
| Context window | 1Mwin | 131K |
| Max output tokens | 66K | 98Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen3.8 27B on top, GLM 4.5 Air 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 | Qwen3.8 27B | GLM 4.5 Air |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $106.83/mo | $91.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $92.16/mo | $113.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $111.54/mo | $113.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $131.00/mo | $188.50/mo |
| Bulk classification 500 in / 20 out × 5M requests | $204.95/mo | $357.50/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1M against 131K.
You are cost-constrained
GLM 4.5 Air
Cheaper on blended list price at $0.310 per million tokens.
Qwen3.8 27B vs GLM 4.5 Air FAQ
Which is better, Qwen3.8 27B or GLM 4.5 Air?
GLM 4.5 Air is the cheaper of the two; neither can be ranked on quality here. GLM 4.5 Air 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 Qwen3.8 27B cheaper than GLM 4.5 Air?
GLM 4.5 Air is cheaper. On a 3:1 input:output blend, Qwen3.8 27B lists at $0.342 per million tokens and GLM 4.5 Air at $0.310 — GLM 4.5 Air is 10% cheaper. Input and output are priced separately — Qwen3.8 27B charges $0.039 in and $1.25 out, GLM 4.5 Air charges $0.130 and $0.850 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Qwen3.8 27B or GLM 4.5 Air have a bigger context window?
Qwen3.8 27B has the larger context window: 1M for Qwen3.8 27B against 131K for GLM 4.5 Air. 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 Qwen3.8 27B and GLM 4.5 Air support prompt caching?
Both publish a cached-input rate: $0.0039 per million for Qwen3.8 27B and $0.025 for GLM 4.5 Air, against full input rates of $0.039 and $0.130. 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.