// head_to_head
Gemini 3.7 Flash 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.
Gemini 3.7 Flash wins outright — it scores higher and costs less.
Gemini 3.7 Flash leads by 3.6 points overall while listing 1.5× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. The two cost measures disagree here, which is worth knowing: Gemini 3.7 Flash has the lower sticker price, but Qwen3.8 27B earns each point of capability for less — $0.0556 against $0.0875 — because per-token rates do not predict how many tokens a model actually spends on a task.
Gemini 3.7 Flash
- Blended / 1M
- $0.750
- Context
- 1.0M
- Released
- Aug 13, 2026
- Overall score
- 78.8
qwen
Qwen3.8 27B
- Blended / 1M
- $1.14
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 75.3
Specs and pricing
| Metric | Gemini 3.7 Flash | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 78.8win | 75.3 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0875 | $0.0556win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.750win | $1.14 |
| Input price / 1M | $0.375win | $0.450 |
| Output price / 1M | $1.88win | $3.20 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.037win | $0.050 |
| Context window | 1.0M | 1M |
| Max output tokens | 66K | 131Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.7 Flash 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 | Gemini 3.7 Flash | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $215.70/mo | $335.20/mo |
| RAG assistant 8K in / 600 out × 100K requests | $277.50/mo | $392.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $261.00/mo | $392.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $498.75/mo | $670.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $956.25/mo | $1245.00/mo |
Which should you pick?
You are running this at volume
Qwen3.8 27B
Lowest measured cost per point of capability at $0.0556 per point — the gap compounds with every request.
Quality matters more than the bill
Gemini 3.7 Flash
Highest overall LiveBench score of the two at 78.8.
The workload is coding or agentic work
Qwen3.8 27B
Leads on agentic coding — 61.4 against 58.3.
Gemini 3.7 Flash vs Qwen3.8 27B FAQ
Which is better, Gemini 3.7 Flash or Qwen3.8 27B?
Gemini 3.7 Flash wins outright — it scores higher and costs less. Gemini 3.7 Flash leads by 3.6 points overall while listing 1.5× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. The two cost measures disagree here, which is worth knowing: Gemini 3.7 Flash has the lower sticker price, but Qwen3.8 27B earns each point of capability for less — $0.0556 against $0.0875 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Gemini 3.7 Flash cheaper than Qwen3.8 27B?
Gemini 3.7 Flash is cheaper. On a 3:1 input:output blend, Gemini 3.7 Flash lists at $0.750 per million tokens and Qwen3.8 27B at $1.14 — Gemini 3.7 Flash is 1.5× cheaper. Input and output are priced separately — Gemini 3.7 Flash charges $0.375 in and $1.88 out, Qwen3.8 27B charges $0.450 and $3.20 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemini 3.7 Flash vs Qwen3.8 27B: which scores higher on benchmarks?
Gemini 3.7 Flash scores 78.8 and Qwen3.8 27B scores 75.3 overall on LiveBench, the mean of its seven categories. That is a 3.6-point lead for Gemini 3.7 Flash. 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, Gemini 3.7 Flash or Qwen3.8 27B?
Qwen3.8 27B. 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. Gemini 3.7 Flash works out at $0.0875 per point and Qwen3.8 27B at $0.0556.
Does Gemini 3.7 Flash or Qwen3.8 27B have a bigger context window?
They are effectively the same — 1.0M for Gemini 3.7 Flash and 1M for Qwen3.8 27B.
Do Gemini 3.7 Flash and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.037 per million for Gemini 3.7 Flash and $0.050 for Qwen3.8 27B, against full input rates of $0.375 and $0.450. 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.