// head_to_head
Qwen3.8 27B vs GLM 5.3 FlashX Legal
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.
Qwen3.8 27B and GLM 5.3 FlashX Legal are priced within ~10% of each other.
GLM 5.3 FlashX Legal 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
- $0.581
- Context
- 1.0M
- Released
- Aug 14, 2026
- Overall score
- 58.4
z-ai
GLM 5.3 FlashX Legal
- Blended / 1M
- $0.590
- Context
- 1.0M
- Released
- Nov 2, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Qwen3.8 27B | GLM 5.3 FlashX Legal |
|---|---|---|
| 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. | $0.581 | $0.590 |
| Input price / 1M | $0.150win | $0.370 |
| Output price / 1M | $1.88 | $1.25win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.037win | $0.075 |
| Context window | 1.0M | 1.0M |
| Max output tokens | 236Kwin | 131K |
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 5.3 FlashX Legal 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 5.3 FlashX Legal |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $177.90/mo | $167.56/mo |
| RAG assistant 8K in / 600 out × 100K requests | $187.50/mo | $253.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $207.00/mo | $230.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $285.00/mo | $449.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $506.25/mo | $902.50/mo |
Qwen3.8 27B vs GLM 5.3 FlashX Legal FAQ
Which is better, Qwen3.8 27B or GLM 5.3 FlashX Legal?
Qwen3.8 27B and GLM 5.3 FlashX Legal are priced within ~10% of each other. GLM 5.3 FlashX Legal 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 5.3 FlashX Legal?
They cost about the same. Both land near $0.581 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Qwen3.8 27B or GLM 5.3 FlashX Legal have a bigger context window?
They are effectively the same — 1.0M for Qwen3.8 27B and 1.0M for GLM 5.3 FlashX Legal.
Do Qwen3.8 27B and GLM 5.3 FlashX Legal support prompt caching?
Both publish a cached-input rate: $0.037 per million for Qwen3.8 27B and $0.075 for GLM 5.3 FlashX Legal, against full input rates of $0.150 and $0.370. 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.