Mistral Small 3.2 24B
Mistral AI · released Jun 19, 2025 · mistralai/Mistral-Small-3.2-24B-Instruct-2506
- Type
- Open weightsApache License 2.0
- Params
- 24B
- Context
- 256K
about 192K words of context
Our take
Written Aug 2, 2026Mistral Small is a 24-billion-parameter text-and-image model released in 2025 with a permissive Apache licence. Its input rate is among the lowest of capable mid-size models, making it a good fit for budget production workloads that do not need the latest release.
Pick this for budget production workloads where low input cost matters. Use it for self-hosting on 24GB-class GPUs with a permissive licence, or vision-input tasks at minimal cost. Skip it if you want measured quality scores or the newest 2026 models.
The case for it
- Lowest-cost hosted inference in the 24–27-billion-parameter class.
- Apache 2.0 licence allows unrestricted commercial use and fine-tuning.
The case against it
- Released in 2025, a generation behind current mid-size releases.
- No benchmark scores yet, so there is no measured quality data.
- Only five tracked offers, a thinner hosting market than peers.
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 Mistral Small 3.2 24B — 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. 5.7 GB spare means a 10% error in the size would not change the answer.
GeForce RTX 5090 · 32 GB
Room to spare. 13.7 GB spare means a 10% error in the size would not change the answer.
Apple M2 (10-core GPU) · 24 GB
Borderline fit on an estimated size. It leaves 0.9 GB spare on a size we calculated rather than measured, and a 10% error either way would 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 6 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
- $0.075 in / $0.20 out
- Context served
- 256K
- Throughput
- Not measured
| Provider | In / out per 1M tokens | Context | Throughput | Trains on prompts | Logs prompts | Zero retention |
|---|---|---|---|---|---|---|
| OpenRouter | $0.075 / $0.20 | 256K | not measured | Unknown | Unknown | Unknown |
| DeepInfrafp8 | $0.075 / $0.20 | 128K | not measured | Unknown | Unknown | Unknown |
| DeepInfrafp8 | $0.075 / $0.20 | 128K | 31 tok/s | No | No | Confirmed |
| Venice AIfp8 | $0.094 / $0.25 | 256K | 25 tok/s | No | No | Confirmed |
| Mistral AI | $0.10 / $0.30 | 131K | 102 tok/s | No | Yes30 days | Unknown |
| Parasailbf16 | $0.090 / $0.30 | 131K | 14 tok/s | No | No | Confirmed |
DeepInfra
Mistral AIAcross the 6 listings we hold: 4 say they do not train on prompts, 0 say they do and 2 do not say. 3 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 | ✓ | ✓ | ✓ |
| DeepInfrafp8 | |||
| DeepInfrafp8 | ✓ | ✓ | ✓ |
| Venice AIfp8 | ✓ | ✓ | ✓ |
| Mistral AI | ✓ | ✓ | ✓ |
| Parasailbf16 | ✗ | ✓ | ✓ |
Tool calling: 4 of 6 listings say yes, 1 says no, 1 publishes no parameter list. JSON output: 5 of 6 listings say yes, 1 publishes no parameter list. Strict schema: 5 of 6 listings say yes, 1 publishes no parameter list.
Models people weigh against Mistral Small 3.2 24B
When we formed this view
Dates behind this page
Prices last checked 4d 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 6 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 6 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/Mistral-Small-3.2-24B-Instruct-2506
- Architecture
- Dense
- Modality record
- text+image->text
- Catalogue slug
- mistralai-mistral-small-3-2-24b