// head_to_head
MiniMax M3.1 Flash 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.
MiniMax M3.1 Flash scores higher, GPT-6 Luna costs less — it depends on your workload.
MiniMax M3.1 Flash is ahead by 2.3 points overall, and GPT-6 Luna lists 31% cheaper per blended million tokens. Whether 2.3 points is worth that depends on how much a wrong answer costs you. GPT-6 Luna also leads on measured cost per point of capability, at $0.0013 per point.
minimax
MiniMax M3.1 Flash
- Blended / 1M
- $0.262
- Context
- 1.0M
- Released
- Oct 19, 2026
- Overall score
- 66.9
openai
GPT-6 Luna
- Blended / 1M
- $0.200
- Context
- 1.1M
- Released
- Sep 22, 2026
- Overall score
- 64.6
Specs and pricing
| Metric | MiniMax M3.1 Flash | GPT-6 Luna |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 66.9win | 64.6 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0015 | $0.0013win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.262 | $0.200win |
| Input price / 1M | $0.150 | $0.100win |
| Output price / 1M | $0.600 | $0.500win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.015 | $0.010win |
| Context window | 1.0M | 1.1M |
| Max output tokens | 524Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiniMax M3.1 Flash 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 | MiniMax M3.1 Flash | GPT-6 Luna |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $74.28/mo | $57.52/mo |
| RAG assistant 8K in / 600 out × 100K requests | $102.00/mo | $74.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $92.40/mo | $69.60/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $188.25/mo | $133.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $367.50/mo | $255.00/mo |
Which should you pick?
You are running this at volume
GPT-6 Luna
Lowest measured cost per point of capability at $0.0013 per point — the gap compounds with every request.
Quality matters more than the bill
MiniMax M3.1 Flash
Highest overall LiveBench score of the two at 66.9.
The workload is coding or agentic work
GPT-6 Luna
Leads on long-horizon agentic — 30.9 against 28.8.
MiniMax M3.1 Flash vs GPT-6 Luna FAQ
Which is better, MiniMax M3.1 Flash or GPT-6 Luna?
MiniMax M3.1 Flash scores higher, GPT-6 Luna costs less — it depends on your workload. MiniMax M3.1 Flash is ahead by 2.3 points overall, and GPT-6 Luna lists 31% cheaper per blended million tokens. Whether 2.3 points is worth that depends on how much a wrong answer costs you. GPT-6 Luna also leads on measured cost per point of capability, at $0.0013 per point.
Is MiniMax M3.1 Flash cheaper than GPT-6 Luna?
GPT-6 Luna is cheaper. On a 3:1 input:output blend, MiniMax M3.1 Flash lists at $0.262 per million tokens and GPT-6 Luna at $0.200 — GPT-6 Luna is 31% cheaper. Input and output are priced separately — MiniMax M3.1 Flash charges $0.150 in and $0.600 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.
MiniMax M3.1 Flash vs GPT-6 Luna: which scores higher on benchmarks?
MiniMax M3.1 Flash scores 66.9 and GPT-6 Luna scores 64.6 overall on LiveBench, the weighted mean of its seven categories. That is a 2.3-point lead for MiniMax M3.1 Flash. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, MiniMax M3.1 Flash or GPT-6 Luna?
GPT-6 Luna. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. MiniMax M3.1 Flash works out at $0.0015 per point and GPT-6 Luna at $0.0013.
Does MiniMax M3.1 Flash or GPT-6 Luna have a bigger context window?
They are effectively the same — 1.0M for MiniMax M3.1 Flash and 1.1M for GPT-6 Luna.
Do MiniMax M3.1 Flash and GPT-6 Luna support prompt caching?
Both publish a cached-input rate: $0.015 per million for MiniMax M3.1 Flash and $0.010 for GPT-6 Luna, against full input rates of $0.150 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 2026-10-25, 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.