// head_to_head
MiniMax M3 vs Qwen2.5 72B Instruct
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.
Qwen2.5 72B Instruct is the cheaper of the two; neither can be ranked on quality here.
Qwen2.5 72B Instruct 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.
minimax
MiniMax M3
- Blended / 1M
- $0.525
- Context
- 1.0M
- Released
- May 31, 2026
- Overall score
- 67.3
qwen
Qwen2.5 72B Instruct
- Blended / 1M
- $0.370
- Context
- 33K
- Released
- Sep 19, 2024
- Overall score
- Not evaluated
Specs and pricing
| Metric | MiniMax M3 | Qwen2.5 72B Instruct |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 67.3 | — |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0339 | — |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.525 | $0.370win |
| Input price / 1M | $0.300win | $0.360 |
| Output price / 1M | $1.20 | $0.400win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.060 | — |
| Context window | 1.0Mwin | 33K |
| Max output tokens | 512Kwin | 16K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiniMax M3 on top, Qwen2.5 72B Instruct 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 | Qwen2.5 72B Instruct |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $150.72/mo | $118.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $216.00/mo | $312.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $201.60/mo | $320.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $378.00/mo | $390.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $750.00/mo | $940.00/mo |
Which should you pick?
You need to fit large documents in one call
MiniMax M3
Wider context window — 1.0M against 33K.
You are cost-constrained
Qwen2.5 72B Instruct
Cheaper on blended list price at $0.370 per million tokens.
MiniMax M3 vs Qwen2.5 72B Instruct FAQ
Which is better, MiniMax M3 or Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is the cheaper of the two; neither can be ranked on quality here. Qwen2.5 72B Instruct 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 MiniMax M3 cheaper than Qwen2.5 72B Instruct?
Qwen2.5 72B Instruct is cheaper. On a 3:1 input:output blend, MiniMax M3 lists at $0.525 per million tokens and Qwen2.5 72B Instruct at $0.370 — Qwen2.5 72B Instruct is 42% cheaper. Input and output are priced separately — MiniMax M3 charges $0.300 in and $1.20 out, Qwen2.5 72B Instruct charges $0.360 and $0.400 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does MiniMax M3 or Qwen2.5 72B Instruct have a bigger context window?
MiniMax M3 has the larger context window: 1.0M for MiniMax M3 against 33K for Qwen2.5 72B Instruct. 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 MiniMax M3 and Qwen2.5 72B Instruct support prompt caching?
MiniMax M3 publishes a cached-input rate of $0.060 per million tokens against a full input rate of $0.300. The catalogue lists no separate cached rate for Qwen2.5 72B Instruct, 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.