MiniMax M2.1
MiniMax · released Dec 20, 2025 · MiniMaxAI/MiniMax-M2.1
- Type
- Open weightsCustom licence
- Params
- 229B
- Context
- 205K
about 154K words of context · download allowed, licence restricts use
Our take
Written Aug 3, 2026MiniMax M2.1 is a 228.7-billion-parameter text-only model with a 204,800-token request limit and a restricted custom licence. It is a high-capacity inference option for long-document work, though no independent quality scores have been recorded in our data.
Pick this for long-context text tasks up to 204,800 tokens where you can accept unverified quality, or for budget-conscious inference on the base tier across multiple providers. Use the premium tier if you need higher throughput. Skip it if you need a permissive licence, measured benchmark scores, or multimodal input.
The case for it
- Very large parameter count in our data at 228.7 billion total.
- Substantial request limit of 204,800 tokens for long-document work.
- Competitive base pricing across multiple providers on the cheapest tier.
The case against it
- No verified quality scores in our data — no Elo, MMLU, or other benchmarks recorded.
- Restrictive custom licence, not commercially permissive like Apache or MIT.
- Throughput on the cheapest tier is modest at 29–31 tokens per second, against 45 on the premium tier.
How good is it?
We hold no score for this model.
We look for every model we track on every board we watch, and none of them has turned up MiniMax M2.1 — so there is no intelligence, coding, agentic or writing score to show you, not a low one, none.
Can you run it yourself?
- Fits in memory
- weights load entirely on the card
- Spills to system RAM
- some weights offload; much slower
- Too large
- will not load even with offload
- est
- size is calculated; the verdict could change by 10%
GeForce RTX 4090 · 24 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Apple M1 Pro (16-core GPU) · 32 GB
Too large for this card. The weights do not fit even with part of them offloaded to system memory.
Comfortable fit
Apple M3 Ultra (80-core GPU) · 512 GB
Room to spare. 233.8 GB spare means a 10% error in the size would not change the answer.
Memory use by level
Against a 24 GB card.
All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.
Check against your own machine → · All 71 devices, with every size →
Or rent it from someone else
Cheapest of 5 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.
- per 1M tokens
- $0.30 in / $1.20 out
- Context served
- 205K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $0.30 / $1.20 | 205K | not measured | Unknown | Unknown | Unknown |
| Novita AI | $0.30 / $1.20 | 205K | not measured | Unknown | Unknown | Unknown |
| Novita AIfp8 | $0.30 / $1.20 | 205K | 38 tok/s | No | No | Confirmed |
| Minimaxfp8 | $0.30 / $1.20 | 205K | 64 tok/s | No | Yesunknown period | Confirmed |
| Minimaxhighspeed tierfp8 | $0.30 / $2.40 | 205K | 29 tok/s | No | Yesunknown period | Unknown |
Novita AIAcross the 5 listings we hold: 3 say they do not train on prompts, 0 say they do and 2 do not say. 2 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.
What each host's API supports
From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.
- ✓
- Supported
- ✗
- Not supported
- Not published
- host gave no parameter list
| Provider | Tool calling | JSON output | Strict schema |
|---|---|---|---|
| OpenRouter | ✓ | ✓ | ✗ |
| Novita AI | |||
| Novita AIfp8 | ✓ | ✓ | ✗ |
| Minimaxfp8 | ✓ | ✓ | ✗ |
| Minimaxhighspeed · fp8 | ✓ | ✓ | ✗ |
Tool calling: 4 of 5 listings say yes, 1 publishes no parameter list. JSON output: 4 of 5 listings say yes, 1 publishes no parameter list. Strict schema: 0 of 5 listings say yes, 4 say no, 1 publishes no parameter list.
Models people weigh against MiniMax M2.1
When we formed this view
Dates behind this page
Prices last checked 35h ago
What we do not know about this model yet
- We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
- No board we watch has turned up a score, so we hold no quality figures at all.
- 1 of 5 listings publish no parameter list, so what their API accepts is unknown to us.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 2 of 5 listings do not say whether they train on prompts.
Licence and identifiers
What the licence allowsCustom licence, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.
Licence
Custom licence
This model ships custom license terms that don't map to a known template. We haven't parsed them, so commercial use, redistribution and derivatives are unverified — review the original terms before shipping.
Identifiers
- Hugging Face
- MiniMaxAI/MiniMax-M2.1
- Architecture
- Mixture of experts
- Modality record
- text->text
- Catalogue slug
- minimax-minimax-m2-1