// head_to_head
GPT-5 Mini vs Qwen3.8 27B
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 is the cheaper of the two; neither can be ranked on quality here.
GPT-5 Mini 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.
openai
GPT-5 Mini
- Blended / 1M
- $0.688
- Context
- 400K
- Released
- Aug 7, 2025
- Overall score
- Not evaluated
qwen
Qwen3.8 27B
- Blended / 1M
- $0.581
- Context
- 1.0M
- Released
- Aug 14, 2026
- Overall score
- 58.4
Specs and pricing
| Metric | GPT-5 Mini | Qwen3.8 27B |
|---|---|---|
| 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. | $0.688 | $0.581win |
| Input price / 1M | $0.250 | $0.150win |
| Output price / 1M | $2.00 | $1.88 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.025win | $0.037 |
| Context window | 400K | 1.0Mwin |
| Max output tokens | 128K | 236Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-5 Mini on top, Qwen3.8 27B 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 | GPT-5 Mini | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $203.80/mo | $177.90/mo |
| RAG assistant 8K in / 600 out × 100K requests | $230.00/mo | $187.50/mo |
| Coding agent 40K in / 4K out × 20K requests | $234.00/mo | $207.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $388.75/mo | $285.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $712.50/mo | $506.25/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1.0M against 400K.
GPT-5 Mini vs Qwen3.8 27B FAQ
Which is better, GPT-5 Mini or Qwen3.8 27B?
Qwen3.8 27B is the cheaper of the two; neither can be ranked on quality here. GPT-5 Mini 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 GPT-5 Mini cheaper than Qwen3.8 27B?
Qwen3.8 27B is cheaper. On a 3:1 input:output blend, GPT-5 Mini lists at $0.688 per million tokens and Qwen3.8 27B at $0.581 — Qwen3.8 27B is 18% cheaper. Input and output are priced separately — GPT-5 Mini charges $0.250 in and $2.00 out, Qwen3.8 27B charges $0.150 and $1.88 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does GPT-5 Mini or Qwen3.8 27B have a bigger context window?
Qwen3.8 27B has the larger context window: 400K for GPT-5 Mini against 1.0M for Qwen3.8 27B. 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 GPT-5 Mini and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.025 per million for GPT-5 Mini and $0.037 for Qwen3.8 27B, against full input rates of $0.250 and $0.150. 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.