What hardware do you need to run MiniMax M2.7?
MiniMax M2.7 at Q4_K_M is 140 GB of weights, with a context ceiling of 200k tokens. Not yet rated: released after our last ratings pass. 10B active out of 229B; fast for its size once it fits.
How good is it, really?
On the Artificial Analysis Intelligence Index v4.3 it scores 23, which puts it in the Haiku-class band. In the same band as Anthropic's cheap, fast tier. Every current OpenAI model scores above this band. Score source. See the whole table.
- Summarising — not rated
- Translation — not rated
- Everyday coding — not rated
- Reasoning & maths — not rated
- Agentic work — not rated
What it costs either way
Renting the same model costs $0.21 per million input tokens and $0.84 per million output (OpenRouter, cheapest active endpoint, checked 2026-09-15). Buying a machine only beats that if you use it hard enough, for long enough, that the hardware price divides down below the rental bill.
Machines that run it
| Machine | Price | Speed at 32k | Pay-back | |
|---|---|---|---|---|
| Mac Studio M5 Ultra, 256GB | $10,799 | 25 tok/s estimated | Pays back in 272 years | Run the numbers |
One machine per family, cheapest first. Speeds are measured where a public benchmark exists and estimated from memory bandwidth otherwise; the calculator says which for any configuration.
The specifics
- Parameters
- 228.7B, of which 10B are active per token
- Quantisation
- Q4_K_M
- Weights on disk
- 140 GB
- KV cache
- 8.3 GB at 32k context — All 62 layers are full attention in this release, so the context is expensive despite the small active-parameter count.
- Maximum context
- 200k tokens (200k)
- Licence
- other (see repo)
- Sources
- source 1