Ministral 3 8B 2512
Mistral AI · released Oct 31, 2025 · mistralai/Ministral-3-8B-Instruct-2512
- Type
- Open weightsApache License 2.0
- Params
- 8.9B
- Context
- 262K
about 197K words of context
Our take
Written Aug 2, 2026Ministral is a small downloadable model with a permissive Apache licence and a 262,144-token request limit. It is built for edge and low-memory deployment: on-device and laptop-local use rather than server workloads.
Pick this for edge and on-device deployment where every gigabyte of memory counts, or laptop-local inference in the 8GB unified-memory class. Its symmetric input and output pricing suits generation-heavy workloads. Skip it if you need a broad hosting market or measured quality scores.
The case for it
- 8.9 billion parameters: small enough to fit comfortably in under 8GB of memory.
- Text and image input in a sub-9-billion-parameter model.
The case against it
- Only three current offers, a thin hosted market.
- No benchmark scores yet, so there is no measured quality data.
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 Ministral 3 8B 2512 — 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%
Comfortable fit
GeForce RTX 4090 · 24 GB
Room to spare. 15.5 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 23.5 GB spare means a 10% error in the size would not change the answer.
Apple M1 (8-core GPU) · 16 GB
Room to spare. 4.7 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 3 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
- $0.15 in / $0.15 out
- Context served
- 262K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $0.15 / $0.15 | 262K | not measured | Unknown | Unknown | Unknown |
| Mistral AI | $0.15 / $0.15 | 262K | 87 tok/s | No | Yes30 days | Unknown |
| NextBitfp8 | $0.30 / $0.30 | 262K | 4 tok/s | No | No | Unknown |
Mistral AIAcross the 3 listings we hold: 2 say they do not train on prompts, 0 say they do and 1 do not say. 0 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 | ✓ | ✓ | ✓ |
| Mistral AI | ✓ | ✓ | ✓ |
| NextBitfp8 | ✓ | ✓ | ✓ |
Tool calling: 3 of 3 listings say yes. JSON output: 3 of 3 listings say yes. Strict schema: 3 of 3 listings say yes.
Models people weigh against Ministral 3 8B 2512
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.
- Nothing we hold says whether an endpoint streams, so we do not show it either way.
- 1 of 3 listings do not say whether they train on prompts.
Licence and identifiers
What the licence allowsApache License 2.0, 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
Apache License 2.0
Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.
Identifiers
- Hugging Face
- mistralai/Ministral-3-8B-Instruct-2512
- Architecture
- Dense
- Modality record
- text+image->text
- Catalogue slug
- mistralai-ministral-3-8b-2512