// head_to_head
GPT-6 Astra 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.
GPT-6 Astra scores higher, Qwen3.8 27B costs less — it depends on your workload.
GPT-6 Astra is ahead by 6.9 points overall, and Qwen3.8 27B lists 19× cheaper per blended million tokens. Whether 6.9 points is worth that depends on how much a wrong answer costs you. Qwen3.8 27B also leads on measured cost per point of capability, at $0.0556 per point.
openai
GPT-6 Astra
- Blended / 1M
- $20.00
- Context
- 1.1M
- Released
- Sep 4, 2026
- Overall score
- 82.2
qwen
Qwen3.8 27B
- Blended / 1M
- $1.06
- Context
- 1M
- Released
- Aug 14, 2026
- Overall score
- 75.3
Specs and pricing
| Metric | GPT-6 Astra | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 82.2win | 75.3 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.3942 | $0.0556win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $20.00 | $1.06win |
| Input price / 1M | $10.00 | $0.420win |
| Output price / 1M | $50.00 | $3.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $1.00 | $0.085win |
| Context window | 1.1M | 1M |
| Max output tokens | 128K | 131K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-6 Astra 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 | GPT-6 Astra | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $5752.00/mo | $316.68/mo |
| RAG assistant 8K in / 600 out × 100K requests | $7400.00/mo | $382.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $6960.00/mo | $388.40/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $13,300/mo | $628.25/mo |
| Bulk classification 500 in / 20 out × 5M requests | $25,500/mo | $1182.50/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
GPT-6 Astra
Highest overall LiveBench score of the two at 82.2.
The workload is coding or agentic work
Qwen3.8 27B
Leads on agentic coding — 61.4 against 57.3.
GPT-6 Astra vs Qwen3.8 27B FAQ
Which is better, GPT-6 Astra or Qwen3.8 27B?
GPT-6 Astra scores higher, Qwen3.8 27B costs less — it depends on your workload. GPT-6 Astra is ahead by 6.9 points overall, and Qwen3.8 27B lists 19× cheaper per blended million tokens. Whether 6.9 points is worth that depends on how much a wrong answer costs you. Qwen3.8 27B also leads on measured cost per point of capability, at $0.0556 per point.
Is GPT-6 Astra cheaper than Qwen3.8 27B?
Qwen3.8 27B is cheaper. On a 3:1 input:output blend, GPT-6 Astra lists at $20.00 per million tokens and Qwen3.8 27B at $1.06 — Qwen3.8 27B is 19× cheaper. Input and output are priced separately — GPT-6 Astra charges $10.00 in and $50.00 out, Qwen3.8 27B charges $0.420 and $3.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-6 Astra vs Qwen3.8 27B: which scores higher on benchmarks?
GPT-6 Astra scores 82.2 and Qwen3.8 27B scores 75.3 overall on LiveBench, the mean of its seven categories. That is a 6.9-point lead for GPT-6 Astra. 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, GPT-6 Astra 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. GPT-6 Astra works out at $0.3942 per point and Qwen3.8 27B at $0.0556.
Does GPT-6 Astra or Qwen3.8 27B have a bigger context window?
They are effectively the same — 1.1M for GPT-6 Astra and 1M for Qwen3.8 27B.
Do GPT-6 Astra and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $1.00 per million for GPT-6 Astra and $0.085 for Qwen3.8 27B, against full input rates of $10.00 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.