// head_to_head
Granite 4.0 Micro 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.
Granite 4.0 Micro 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.
ibm-granite
Granite 4.0 Micro
- Blended / 1M
- $0.041
- Context
- 131K
- Released
- Oct 20, 2025
- Overall score
- Not evaluated
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- Not evaluated
Specs and pricing
| Metric | Granite 4.0 Micro | GPT-6 Luna |
|---|---|---|
| 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. | $0.041win | $0.200 |
| Input price / 1M | $0.017win | $0.100 |
| Output price / 1M | $0.112win | $0.500 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.010 |
| Context window | 131K | 1.1Mwin |
| Max output tokens | 118K | 128K |
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 | Granite 4.0 Micro | GPT-6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $13.04/mo | $57.52/mo |
| RAG assistant 8K in / 600 out × 100K requests | $20.32/mo | $74.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $22.56/mo | $69.60/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $25.40/mo | $133.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $53.70/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 131K.
You are cost-constrained
Granite 4.0 Micro
Cheaper on blended list price at $0.041 per million tokens.
Granite 4.0 Micro vs GPT-6 Luna FAQ
Which is better, Granite 4.0 Micro or GPT-6 Luna?
Granite 4.0 Micro 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 Granite 4.0 Micro cheaper than GPT-6 Luna?
Granite 4.0 Micro is cheaper. On a 3:1 input:output blend, Granite 4.0 Micro lists at $0.041 per million tokens and GPT-6 Luna at $0.200 — Granite 4.0 Micro is 4.9× cheaper. Input and output are priced separately — Granite 4.0 Micro charges $0.017 in and $0.112 out, GPT-6 Luna charges $0.100 and $0.500 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Granite 4.0 Micro or GPT-6 Luna have a bigger context window?
GPT-6 Luna has the larger context window: 131K for Granite 4.0 Micro 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 Granite 4.0 Micro and GPT-6 Luna 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 Granite 4.0 Micro, 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.