// head_to_head
LFM2.5-24B-A2B vs GPT-6 Luna
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.
LFM2.5-24B-A2B and GPT-6 Luna are priced within ~10% of each other.
LFM2.5-24B-A2B 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.
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LFM2.5-24B-A2B
- Blended / 1M
- $0.212
- Context
- 128K
- Released
- Oct 13, 2026
- Overall score
- Not evaluated
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- 69.4
Specs and pricing
| Metric | LFM2.5-24B-A2B | GPT-6 Luna |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 69.4 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0894 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.212 | $0.200 |
| Input price / 1M | $0.050win | $0.100 |
| Output price / 1M | $0.700 | $0.500win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.025 | $0.010win |
| Context window | 128K | 1.1Mwin |
| Max output tokens | 8K | 128Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — LFM2.5-24B-A2B on top, GPT-6 Luna 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 | LFM2.5-24B-A2B | GPT-6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $66.20/mo | $57.52/mo |
| RAG assistant 8K in / 600 out × 100K requests | $72.00/mo | $74.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $82.00/mo | $69.60/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $101.25/mo | $133.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $182.50/mo | $255.00/mo |
Which should you pick?
You need to fit large documents in one call
GPT-6 Luna
Wider context window — 1.1M against 128K.
LFM2.5-24B-A2B vs GPT-6 Luna FAQ
Which is better, LFM2.5-24B-A2B or GPT-6 Luna?
LFM2.5-24B-A2B and GPT-6 Luna are priced within ~10% of each other. LFM2.5-24B-A2B 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 LFM2.5-24B-A2B cheaper than GPT-6 Luna?
They cost about the same. Both land near $0.212 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does LFM2.5-24B-A2B or GPT-6 Luna have a bigger context window?
GPT-6 Luna has the larger context window: 128K for LFM2.5-24B-A2B against 1.1M for GPT-6 Luna. 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 LFM2.5-24B-A2B and GPT-6 Luna support prompt caching?
Both publish a cached-input rate: $0.025 per million for LFM2.5-24B-A2B and $0.010 for GPT-6 Luna, against full input rates of $0.050 and $0.100. 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 2026-09-02, 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.