// head_to_head
GPT-6 Luna vs Qwen3.7 Max
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.
Qwen3.7 Max scores higher, GPT-6 Luna costs less — it depends on your workload.
Qwen3.7 Max is ahead by 1.1 points overall, and GPT-6 Luna lists 11× cheaper per blended million tokens. Whether 1.1 points is worth that depends on how much a wrong answer costs you. GPT-6 Luna also leads on measured cost per point of capability, at $0.0134 per point.
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- 72.0
qwen
Qwen3.7 Max
- Blended / 1M
- $2.21
- Context
- 1M
- Released
- May 21, 2026
- Overall score
- 73.1
Specs and pricing
| Metric | GPT-6 Luna | Qwen3.7 Max |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 72.0 | 73.1win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0134win | $0.0971 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200win | $2.21 |
| Input price / 1M | $0.100win | $1.48 |
| Output price / 1M | $0.500win | $4.42 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010win | $0.295 |
| 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 Luna on top, Qwen3.7 Max 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 | Qwen3.7 Max |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $623.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $973.50/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $873.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $1,748/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $3,540/mo |
Which should you pick?
You are running this at volume
GPT-6 Luna
Lowest measured cost per point of capability at $0.0134 per point — the gap compounds with every request.
Quality matters more than the bill
Qwen3.7 Max
Highest overall LiveBench score of the two at 73.1.
The workload is coding or agentic work
GPT-6 Luna
Leads on agentic coding — 51.2 against 43.6.
GPT-6 Luna vs Qwen3.7 Max FAQ
Which is better, GPT-6 Luna or Qwen3.7 Max?
Qwen3.7 Max scores higher, GPT-6 Luna costs less — it depends on your workload. Qwen3.7 Max is ahead by 1.1 points overall, and GPT-6 Luna lists 11× cheaper per blended million tokens. Whether 1.1 points is worth that depends on how much a wrong answer costs you. GPT-6 Luna also leads on measured cost per point of capability, at $0.0134 per point.
Is GPT-6 Luna cheaper than Qwen3.7 Max?
GPT-6 Luna is cheaper. On a 3:1 input:output blend, GPT-6 Luna lists at $0.200 per million tokens and Qwen3.7 Max at $2.21 — GPT-6 Luna is 11× cheaper. Input and output are priced separately — GPT-6 Luna charges $0.100 in and $0.500 out, Qwen3.7 Max charges $1.48 and $4.42 — so the model that looks cheaper flips depending on how output-heavy your workload is.
GPT-6 Luna vs Qwen3.7 Max: which scores higher on benchmarks?
GPT-6 Luna scores 72.0 and Qwen3.7 Max scores 73.1 overall on LiveBench, the mean of its seven categories. That is a 1.1-point lead for Qwen3.7 Max. 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 Qwen3.7 Max?
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 $0.0134 per point and Qwen3.7 Max at $0.0971.
Does GPT-6 Luna or Qwen3.7 Max have a bigger context window?
They are effectively the same — 1.1M for GPT-6 Luna and 1M for Qwen3.7 Max.
Do GPT-6 Luna and Qwen3.7 Max support prompt caching?
Both publish a cached-input rate: $0.010 per million for GPT-6 Luna and $0.295 for Qwen3.7 Max, against full input rates of $0.100 and $1.48. 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.