// head_to_head
Aion-2.0 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.
Aion-2.0 and Qwen3.8 27B are priced within ~10% of each other.
Aion-2.0 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.
aion-labs
Aion-2.0
- Blended / 1M
- $1.00
- Context
- 131K
- Released
- Feb 23, 2026
- Overall score
- Not evaluated
qwen
Qwen3.8 27B
- Blended / 1M
- $1.06
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 75.3
Specs and pricing
| Metric | Aion-2.0 | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 75.3 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0556 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.00 | $1.06 |
| Input price / 1M | $0.800 | $0.420win |
| Output price / 1M | $1.60win | $3.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200 | $0.085win |
| Context window | 131K | 1Mwin |
| Max output tokens | 33K | 131Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Aion-2.0 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 | Aion-2.0 | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $276.80/mo | $316.68/mo |
| RAG assistant 8K in / 600 out × 100K requests | $496.00/mo | $382.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $432.00/mo | $388.40/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $890.00/mo | $628.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,860/mo | $1,182/mo |
Which should you pick?
You need to fit large documents in one call
Qwen3.8 27B
Wider context window — 1M against 131K.
Aion-2.0 vs Qwen3.8 27B FAQ
Which is better, Aion-2.0 or Qwen3.8 27B?
Aion-2.0 and Qwen3.8 27B are priced within ~10% of each other. Aion-2.0 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 Aion-2.0 cheaper than Qwen3.8 27B?
They cost about the same. Both land near $1.00 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does Aion-2.0 or Qwen3.8 27B have a bigger context window?
Qwen3.8 27B has the larger context window: 131K for Aion-2.0 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 Aion-2.0 and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.200 per million for Aion-2.0 and $0.085 for Qwen3.8 27B, against full input rates of $0.800 and $0.420. 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.