// head_to_head
GPT-6 Luna vs Qwen2.5 72B Instruct
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 is the cheaper of the two; neither can be ranked on quality here.
Neither model has 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.
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- Not evaluated
qwen
Qwen2.5 72B Instruct
- Blended / 1M
- $0.370
- Context
- 33K
- Released
- Sep 19, 2024
- Overall score
- Not evaluated
Specs and pricing
| Metric | GPT-6 Luna | Qwen2.5 72B Instruct |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200win | $0.370 |
| Input price / 1M | $0.100win | $0.360 |
| Output price / 1M | $0.500 | $0.400win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010 | — |
| Context window | 1.1Mwin | 33K |
| Max output tokens | 128Kwin | 16K |
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 | Qwen2.5 72B Instruct |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $118.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $312.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $320.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $390.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $940.00/mo |
Which should you pick?
You need to fit large documents in one call
GPT-6 Luna
Wider context window — 1.1M against 33K.
GPT-6 Luna vs Qwen2.5 72B Instruct FAQ
Which is better, GPT-6 Luna or Qwen2.5 72B Instruct?
GPT-6 Luna is the cheaper of the two; neither can be ranked on quality here. Neither model has 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 GPT-6 Luna cheaper than Qwen2.5 72B Instruct?
GPT-6 Luna is cheaper. On a 3:1 input:output blend, GPT-6 Luna lists at $0.200 per million tokens and Qwen2.5 72B Instruct at $0.370 — GPT-6 Luna is 1.8× cheaper. Input and output are priced separately — GPT-6 Luna charges $0.100 in and $0.500 out, Qwen2.5 72B Instruct charges $0.360 and $0.400 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does GPT-6 Luna or Qwen2.5 72B Instruct have a bigger context window?
GPT-6 Luna has the larger context window: 1.1M for GPT-6 Luna against 33K for Qwen2.5 72B Instruct. 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 GPT-6 Luna and Qwen2.5 72B Instruct support prompt caching?
GPT-6 Luna publishes a cached-input rate of $0.010 per million tokens against a full input rate of $0.100. The catalogue lists no separate cached rate for Qwen2.5 72B Instruct, which means the provider does not price it separately here — not that caching is unavailable.
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.