// head_to_head
GPT-5.6 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.
Effectively the same quality — GPT-5.6 Luna is the cheaper way to get it.
The two are within 0.4 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GPT-5.6 Luna lists 4.9× cheaper per blended million tokens. When quality ties, cost is the whole decision.
openai
GPT-5.6 Luna
- Blended / 1M
- $0.450
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 73.6
qwen
Qwen3.7 Max
- Blended / 1M
- $2.21
- Context
- 1M
- Released
- May 21, 2026
- Overall score
- 73.1
Specs and pricing
| Metric | GPT-5.6 Luna | Qwen3.7 Max |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 73.6 | 73.1 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0911 | $0.0971 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.450win | $2.21 |
| Input price / 1M | $0.200win | $1.48 |
| Output price / 1M | $1.20win | $4.42 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $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-5.6 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-5.6 Luna | Qwen3.7 Max |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $131.04/mo | $623.04/mo |
| RAG assistant 8K in / 600 out × 100K requests | $160.00/mo | $973.50/mo |
| Coding agent 40K in / 4K out × 20K requests | $155.20/mo | $873.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $281.00/mo | $1747.88/mo |
| Bulk classification 500 in / 20 out × 5M requests | $530.00/mo | $3540.00/mo |
Which should you pick?
The workload is coding or agentic work
GPT-5.6 Luna
Leads on agentic coding — 48.4 against 43.6.
GPT-5.6 Luna vs Qwen3.7 Max FAQ
Which is better, GPT-5.6 Luna or Qwen3.7 Max?
Effectively the same quality — GPT-5.6 Luna is the cheaper way to get it. The two are within 0.4 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. GPT-5.6 Luna lists 4.9× cheaper per blended million tokens. When quality ties, cost is the whole decision.
Is GPT-5.6 Luna cheaper than Qwen3.7 Max?
GPT-5.6 Luna is cheaper. On a 3:1 input:output blend, GPT-5.6 Luna lists at $0.450 per million tokens and Qwen3.7 Max at $2.21 — GPT-5.6 Luna is 4.9× cheaper. Input and output are priced separately — GPT-5.6 Luna charges $0.200 in and $1.20 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-5.6 Luna vs Qwen3.7 Max: which scores higher on benchmarks?
GPT-5.6 Luna scores 73.6 and Qwen3.7 Max scores 73.1 overall on LiveBench, the mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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-5.6 Luna or Qwen3.7 Max?
They are close. GPT-5.6 Luna costs $0.0911 per point of overall capability and Qwen3.7 Max costs $0.0971, a difference small enough that workload shape will matter more than the rate.
Does GPT-5.6 Luna or Qwen3.7 Max have a bigger context window?
They are effectively the same — 1.1M for GPT-5.6 Luna and 1M for Qwen3.7 Max.
Do GPT-5.6 Luna and Qwen3.7 Max support prompt caching?
Both publish a cached-input rate: $0.020 per million for GPT-5.6 Luna and $0.295 for Qwen3.7 Max, against full input rates of $0.200 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.