// head_to_head
MiniMax M1 vs Mistral Large 4
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 M1 and Mistral Large 4 are priced within ~10% of each other.
MiniMax M1 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 M1
- Blended / 1M
- $0.963
- Context
- 1M
- Released
- Jun 17, 2025
- Overall score
- Not evaluated
mistralai
Mistral Large 4
- Blended / 1M
- $1.05
- Context
- 1.0M
- Released
- Oct 6, 2026
- Overall score
- 43.9
Specs and pricing
| Metric | MiniMax M1 | Mistral Large 4 |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 43.9 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0162 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.963 | $1.05 |
| Input price / 1M | $0.550win | $0.700 |
| Output price / 1M | $2.20 | $2.10 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.070 |
| Context window | 1M | 1.0M |
| Max output tokens | 40K | 262Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — MiniMax M1 on top, Mistral Large 4 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 M1 | Mistral Large 4 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $308.00/mo | $290.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $572.00/mo | $434.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $616.00/mo | $375.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $715.00/mo | $826.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,595/mo | $1,645/mo |
MiniMax M1 vs Mistral Large 4 FAQ
Which is better, MiniMax M1 or Mistral Large 4?
MiniMax M1 and Mistral Large 4 are priced within ~10% of each other. MiniMax M1 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 M1 cheaper than Mistral Large 4?
They cost about the same. Both land near $0.963 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Does MiniMax M1 or Mistral Large 4 have a bigger context window?
They are effectively the same — 1M for MiniMax M1 and 1.0M for Mistral Large 4.
Do MiniMax M1 and Mistral Large 4 support prompt caching?
Mistral Large 4 publishes a cached-input rate of $0.070 per million tokens against a full input rate of $0.700. The catalogue lists no separate cached rate for MiniMax M1, 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 2026-10-25, 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.