// head_to_head
GPT-5.6 Luna vs Beam 1.0 501B A23B
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-5.6 Luna and Beam 1.0 501B A23B are priced within ~10% of each other.
Beam 1.0 501B A23B 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-5.6 Luna
- Blended / 1M
- $0.450
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 66.1
reflection
Beam 1.0 501B A23B
- Blended / 1M
- $0.438
- Context
- 1.0M
- Released
- Oct 16, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | GPT-5.6 Luna | Beam 1.0 501B A23B |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 66.1 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.4693 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.450 | $0.438 |
| Input price / 1M | $0.200win | $0.250 |
| Output price / 1M | $1.20 | $1.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020 | — |
| Context window | 1.1M | 1.0M |
| 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, Beam 1.0 501B A23B 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 | Beam 1.0 501B A23B |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $131.04/mo | $140.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $160.00/mo | $260.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $155.20/mo | $280.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $281.00/mo | $325.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $530.00/mo | $725.00/mo |
GPT-5.6 Luna vs Beam 1.0 501B A23B FAQ
Which is better, GPT-5.6 Luna or Beam 1.0 501B A23B?
GPT-5.6 Luna and Beam 1.0 501B A23B are priced within ~10% of each other. Beam 1.0 501B A23B 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-5.6 Luna cheaper than Beam 1.0 501B A23B?
They cost about the same. Both land near $0.450 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does GPT-5.6 Luna or Beam 1.0 501B A23B have a bigger context window?
They are effectively the same — 1.1M for GPT-5.6 Luna and 1.0M for Beam 1.0 501B A23B.
Do GPT-5.6 Luna and Beam 1.0 501B A23B support prompt caching?
GPT-5.6 Luna publishes a cached-input rate of $0.020 per million tokens against a full input rate of $0.200. The catalogue lists no separate cached rate for Beam 1.0 501B A23B, 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 2026-09-02, 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.