// head_to_head
Qwen3.8 27B vs Relace Apply 3
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.
Relace Apply 3 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
Specs and pricing
| Metric | Qwen3.8 27B | Relace Apply 3 |
|---|---|---|
| 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. | $0.581win | $0.950 |
| Input price / 1M | $0.150win | $0.850 |
| 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. | $0.037 | — |
| Context window | 1.0Mwin | 256K |
| Max output tokens | 236Kwin | 128K |
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, Relace Apply 3 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 | Relace Apply 3 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $177.90/mo | $304.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $187.50/mo | $755.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $207.00/mo | $780.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $285.00/mo | $943.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $506.25/mo | $2,250/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1.0M against 256K.
Qwen3.8 27B vs Relace Apply 3 FAQ
Which is better, Qwen3.8 27B or Relace Apply 3?
Qwen3.8 27B is the cheaper of the two; neither can be ranked on quality here. Relace Apply 3 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 Relace Apply 3?
Qwen3.8 27B is cheaper. On a 3:1 input:output blend, Qwen3.8 27B lists at $0.581 per million tokens and Relace Apply 3 at $0.950 — Qwen3.8 27B is 1.6× cheaper. Input and output are priced separately — Qwen3.8 27B charges $0.150 in and $1.88 out, Relace Apply 3 charges $0.850 and $1.25 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Qwen3.8 27B or Relace Apply 3 have a bigger context window?
Qwen3.8 27B has the larger context window: 1.0M for Qwen3.8 27B against 256K for Relace Apply 3. 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 Relace Apply 3 support prompt caching?
Qwen3.8 27B publishes a cached-input rate of $0.037 per million tokens against a full input rate of $0.150. The catalogue lists no separate cached rate for Relace Apply 3, 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.