// head_to_head
GPT-6 Luna vs Summarize
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.
Summarize is the cheaper of the two; neither can be ranked on quality here.
Summarize does not have 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
- 72.0
scaledown
Summarize
- Blended / 1M
- $0.030
- Context
- 1M
- Released
- Oct 13, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | GPT-6 Luna | Summarize |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 72.0 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0134 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.200 | $0.030win |
| Input price / 1M | $0.100 | $0.040win |
| Output price / 1M | $0.500 | Freewin |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.010 | — |
| Context window | 1.1M | 1M |
| Max output tokens | 128Kwin | 4K |
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, Summarize 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 | Summarize |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $57.52/mo | $9.60/mo |
| RAG assistant 8K in / 600 out × 100K requests | $74.00/mo | $32.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $69.60/mo | $32.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $133.00/mo | $40.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $255.00/mo | $100.00/mo |
Which should you pick?
You are cost-constrained
Summarize
Cheaper on blended list price at $0.030 per million tokens.
GPT-6 Luna vs Summarize FAQ
Which is better, GPT-6 Luna or Summarize?
Summarize is the cheaper of the two; neither can be ranked on quality here. Summarize does not have 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 Summarize?
Summarize is cheaper. On a 3:1 input:output blend, GPT-6 Luna lists at $0.200 per million tokens and Summarize at $0.030 — Summarize is 6.7× cheaper. Input and output are priced separately — GPT-6 Luna charges $0.100 in and $0.500 out, Summarize charges $0.040 and Free — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does GPT-6 Luna or Summarize have a bigger context window?
They are effectively the same — 1.1M for GPT-6 Luna and 1M for Summarize.
Do GPT-6 Luna and Summarize 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 Summarize, 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.