// head_to_head
Gemma 4 31B 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 scores higher, Gemma 4 31B costs less — it depends on your workload.
Qwen3.8 27B is ahead by 19.7 points overall, and Gemma 4 31B lists 37% cheaper per blended million tokens. Whether 19.7 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.
Gemma 4 31B
- Blended / 1M
- $0.250
- Context
- 262K
- Released
- Apr 2, 2026
- Overall score
- 38.7
qwen
Qwen3.8 27B
- Blended / 1M
- $0.342
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 58.4
Specs and pricing
| Metric | Gemma 4 31B | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 38.7 | 58.4win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0017win | $0.0084 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.250win | $0.342 |
| Input price / 1M | $0.200 | $0.039win |
| Output price / 1M | $0.400win | $1.25 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.0039 |
| Context window | 262K | 1Mwin |
| Max output tokens | 16K | 66Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemma 4 31B 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 | Gemma 4 31B | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $80.00/mo | $106.83/mo |
| RAG assistant 8K in / 600 out × 100K requests | $184.00/mo | $92.16/mo |
| Coding agent 40K in / 4K out × 20K requests | $192.00/mo | $111.54/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $230.00/mo | $131.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $540.00/mo | $204.95/mo |
Which should you pick?
You are running this at volume
Gemma 4 31B
Lowest measured cost per point of capability at $0.0017 per point — the gap compounds with every request.
Quality matters more than the bill
Qwen3.8 27B
Highest overall LiveBench score of the two at 58.4.
The workload is coding or agentic work
Qwen3.8 27B
Leads on long-horizon agentic — 20.6 against 10.7.
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1M against 262K.
Gemma 4 31B vs Qwen3.8 27B FAQ
Which is better, Gemma 4 31B or Qwen3.8 27B?
Qwen3.8 27B scores higher, Gemma 4 31B costs less — it depends on your workload. Qwen3.8 27B is ahead by 19.7 points overall, and Gemma 4 31B lists 37% cheaper per blended million tokens. Whether 19.7 points is worth that depends on how much a wrong answer costs you. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.
Is Gemma 4 31B cheaper than Qwen3.8 27B?
Gemma 4 31B is cheaper. On a 3:1 input:output blend, Gemma 4 31B lists at $0.250 per million tokens and Qwen3.8 27B at $0.342 — Gemma 4 31B is 37% cheaper. Input and output are priced separately — Gemma 4 31B charges $0.200 in and $0.400 out, Qwen3.8 27B charges $0.039 and $1.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemma 4 31B vs Qwen3.8 27B: which scores higher on benchmarks?
Gemma 4 31B scores 38.7 and Qwen3.8 27B scores 58.4 overall on LiveBench, the weighted mean of its seven categories. That is a 19.7-point lead for Qwen3.8 27B. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Gemma 4 31B or Qwen3.8 27B?
Gemma 4 31B. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Gemma 4 31B works out at $0.0017 per point and Qwen3.8 27B at $0.0084.
Does Gemma 4 31B or Qwen3.8 27B have a bigger context window?
Qwen3.8 27B has the larger context window: 262K for Gemma 4 31B against 1M 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 Gemma 4 31B and Qwen3.8 27B support prompt caching?
Qwen3.8 27B publishes a cached-input rate of $0.0039 per million tokens against a full input rate of $0.039. The catalogue lists no separate cached rate for Gemma 4 31B, which means the provider does not price it separately here — not that caching is unavailable.
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.