// head_to_head
GPT-6 Luna 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 Luna wins outright — it scores higher and costs less.
GPT-6 Luna leads by 6.2 points overall while listing 2.9× 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. GPT-6 Luna also leads on measured cost per point of capability, at $0.0012 per point.
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- 64.6
qwen
Qwen3.8 27B
- Blended / 1M
- $0.581
- Context
- 1.0M
- Released
- Aug 14, 2026
- Overall score
- 58.4
Specs and pricing
| Metric | GPT-6 Luna | Qwen3.8 27B |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 64.6win | 58.4 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0012win | $0.0084 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200win | $0.581 |
| Input price / 1M | $0.100win | $0.150 |
| Output price / 1M | $0.500win | $1.88 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010win | $0.037 |
| Context window | 1.1M | 1.0M |
| Max output tokens | 128K | 236Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — GPT-6 Luna 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 Luna | Qwen3.8 27B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $177.90/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $187.50/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $207.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $285.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $506.25/mo |
Which should you pick?
You are running this at volume
GPT-6 Luna
Lowest measured cost per point of capability at $0.0012 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-6 Luna
Highest overall LiveBench score of the two at 64.6.
The workload is coding or agentic work
GPT-6 Luna
Leads on long-horizon agentic — 30.9 against 20.6.
GPT-6 Luna vs Qwen3.8 27B FAQ
Which is better, GPT-6 Luna or Qwen3.8 27B?
GPT-6 Luna wins outright — it scores higher and costs less. GPT-6 Luna leads by 6.2 points overall while listing 2.9× 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. GPT-6 Luna also leads on measured cost per point of capability, at $0.0012 per point.
Is GPT-6 Luna cheaper than Qwen3.8 27B?
GPT-6 Luna is cheaper. On a 3:1 input:output blend, GPT-6 Luna lists at $0.200 per million tokens and Qwen3.8 27B at $0.581 — GPT-6 Luna is 2.9× cheaper. Input and output are priced separately — GPT-6 Luna charges $0.100 in and $0.500 out, Qwen3.8 27B charges $0.150 and $1.88 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-6 Luna vs Qwen3.8 27B: which scores higher on benchmarks?
GPT-6 Luna scores 64.6 and Qwen3.8 27B scores 58.4 overall on LiveBench, the weighted mean of its seven categories. That is a 6.2-point lead for GPT-6 Luna. 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 Luna or Qwen3.8 27B?
GPT-6 Luna. 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 Luna works out at $0.0012 per point and Qwen3.8 27B at $0.0084.
Does GPT-6 Luna or Qwen3.8 27B have a bigger context window?
They are effectively the same — 1.1M for GPT-6 Luna and 1.0M for Qwen3.8 27B.
Do GPT-6 Luna and Qwen3.8 27B support prompt caching?
Both publish a cached-input rate: $0.010 per million for GPT-6 Luna and $0.037 for Qwen3.8 27B, against full input rates of $0.100 and $0.150. 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.