// head_to_head
Qwen3.8 27B vs Qwen3 VL 8B Instruct
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 Qwen3 VL 8B Instruct are priced within ~10% of each other.
Qwen3 VL 8B Instruct 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
qwen
Qwen3 VL 8B Instruct
- Blended / 1M
- $0.375
- Context
- 262K
- Released
- Oct 14, 2025
- Overall score
- Not evaluated
Specs and pricing
| Metric | Qwen3.8 27B | Qwen3 VL 8B Instruct |
|---|---|---|
| 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 | $0.375 |
| Input price / 1M | $0.039win | $0.250 |
| Output price / 1M | $1.25 | $0.750win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.0039win | $0.120 |
| Context window | 1Mwin | 262K |
| Max output tokens | 66K | 236Kwin |
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, Qwen3 VL 8B Instruct 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 | Qwen3 VL 8B Instruct |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $106.83/mo | $110.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $92.16/mo | $193.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $111.54/mo | $187.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $131.00/mo | $299.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $204.95/mo | $635.00/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1M against 262K.
Qwen3.8 27B vs Qwen3 VL 8B Instruct FAQ
Which is better, Qwen3.8 27B or Qwen3 VL 8B Instruct?
Qwen3.8 27B and Qwen3 VL 8B Instruct are priced within ~10% of each other. Qwen3 VL 8B Instruct 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 Qwen3 VL 8B Instruct?
They cost about the same. Both land near $0.342 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 Qwen3 VL 8B Instruct have a bigger context window?
Qwen3.8 27B has the larger context window: 1M for Qwen3.8 27B against 262K for Qwen3 VL 8B Instruct. 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 Qwen3 VL 8B Instruct support prompt caching?
Both publish a cached-input rate: $0.0039 per million for Qwen3.8 27B and $0.120 for Qwen3 VL 8B Instruct, against full input rates of $0.039 and $0.250. 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.