MiniMax M1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
MiniMaxVerified 05/10/2026Released 17/06/202501 / decision
Decision snapshot
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 370 models tracked here, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$0.55
field median $0.40
Output / 1M tokens
$2.20
field median $1.68
Context
1,000K
field median 262K
02 / overview
What MiniMax M1 is, and when to reach for it
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
This model has no written explanation yet.
The page is created the moment a model appears in a provider's catalogue, and the writing follows. Until then the measured sections below are the whole page.
What counts as evidence03 / evidence
How much of this is verified
Split by category, so a strong number never hides a thin evidence base. Verified means it was read on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
A score is published only where the result can be verified. Until a benchmark result for this model exists in a source the tracker reads, this section stays empty rather than being filled with a provider's marketing figure.
What counts as evidence04 / ledger
Benchmark ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
What counts as evidence05 / capability
Capability shape
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against every tracked model: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
What counts as evidence06 / cost
What it costs
List API rates as last read, each with the date it was confirmed, plus every change recorded since tracking began.
| Charge | Price | Unit | Read on |
|---|---|---|---|
| Output | $2.20 | per 1M tokens | 2026-10-01 |
| Input | $0.55 | per 1M tokens | 2026-10-01 |
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $22.00 |
| A busy support assistant | 200M tokens | 40M tokens | $198.00 |
| A document pipeline | 1000M tokens | 100M tokens | $770.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
Price history (2 earlier readings)
- 2026-09-30: Input $0.40 per 1M tokens
- 2026-09-30: Output $2.20 per 1M tokens
Every reading taken, kept whether it changed or not. This is the part nobody can copy in a week.
07 / specs
Specifications
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 1,000,000 tokens |
|---|---|
| Maximum output | 40,000 tokens |
| Modalities | text |
| Released | 17/06/2025 |
| Status | Current |
08 / lineage
Lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from MiniMax
- MiniMax M331/05/2026
- MiniMax M2.718/03/2026
- MiniMax M2.512/02/2026
- MiniMax M2-her23/01/2026
- MiniMax M2.123/12/2025
- MiniMax M223/10/2025
- MiniMax-0115/01/2025
09 / line
The line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Nothing else in this line yet.
A line is worked out from the naming and the release dates across every model page on the tracker. It fills in as the provider ships successors, or as the models that came before this one are added.
What counts as evidence10 / notes
WriteWorks notes
What this model changes for a brand trying to be cited in AI answers, and every change logged since it launched.
Change log
Every release, price and feature change for MiniMax M1, newest first. They also appear on the MiniMax page.
- PricingMiniMax M1 pricing changed
Model: MiniMax M1 Input was $0.4 per 1M tokens, now $0.55 per 1M tokens Output was $2.2 per 1M tokens, now $2.2 per 1M tokens
11 / questions
Questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
- What does MiniMax M1 cost?
- $0.55 per million input tokens and $2.20 per million output tokens, as last read from the provider. The cost section works that into a monthly figure.
- How current is this page?
- The catalogue behind it is re-read every three hours, and the stamp at the top says when it last confirmed. A re-check that finds nothing changed updates that stamp and deliberately does not touch the page’s modified date.
- Why are some sections empty?
- Because nothing has been published that can be linked to. An empty section is better than a number you cannot check. The methodology sets out what counts.
Is MiniMax M1 recommending you?
Models change what gets cited. WriteWorks tracks whether 10+ AI platforms, from ChatGPT and Claude to Gemini and Perplexity, name your brand or your competitors, and shows you the content gaps to close.