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Mistral Small 3.1 24B

Mistral Small 3.1 24B is a 24 billion parameter open-weight instruct model from Mistral that handles both text and images, positioned as the upgraded version of Mistral Small 3.

MistralVerified 24/09/2026Released 17/03/2025

01 / 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 179 models we track, not against an absolute standard.

Capability

Not measured

No published benchmark scores yet

Input / 1M tokens

$0.35

field median $0.46

Output / 1M tokens

$0.56

field median $1.82

Context

128K

field median 524K

02 / overview

What Mistral Small 3.1 24B 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.

What it is

The job it was built for, and the job it is not for.

Mistral Small 3.1 24B is a small, instruction-tuned multimodal model built for text reasoning and image understanding at a size that runs cheaply and can be self-hosted. The job it is designed for is high-volume work where a frontier model is overkill: classification, extraction, summarisation, chat over documents and screenshots. It is not the model to reach for when you need the deepest reasoning on a hard problem, that is what the larger tiers exist for.

When it arrived, and when to use it

Where it sits in its line, and when a sibling is the better pick.

Mistral Small 3.1 24B first appeared in March 2025 as the successor to Mistral Small 3, adding image input to the same small-model slot in Mistral's range. Pick it when the task is well defined, the volume is high and you want vision without moving up a price tier. Pick something larger when the work involves long multi-step reasoning or where a wrong answer is expensive.

How you reach it

The API, the apps it powers, and what its limits let you do.

Mistral Small 3.1 24B is reached through Mistral's API and through the third-party hosts that serve open-weight models, and the weights can be run on your own hardware. Its context window is large enough to hold a long document set, a full support thread or a batch of pages in a single call, and the generous output ceiling means it can return long structured extractions rather than being cut short. Because it takes images as well as text, it will read a screenshot, a scanned invoice or a chart alongside the prompt.

Why it matters

What changes because this exists, or why it does not.

What Mistral Small 3.1 24B changes is the floor for multimodal work. Pipelines that previously sent every image to a large hosted model can now route the routine cases to a small model that costs a fraction of that and can sit inside your own infrastructure, keeping the expensive model for the cases that actually need it.

03 / evidence

How much of this is verified

Split by category, so a strong number never hides a thin evidence base. Verified means we read it 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.

We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.

How we decide what counts as evidence

04 / 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.

How we decide what counts as evidence

05 / capability

Capability shape

Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: 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.

How we decide what counts as evidence

06 / cost

What it costs

List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.

Mistral Small 3.1 24B API pricingSurge45°
ChargePriceUnitRead onSource
Input$0.35per 1M tokens2026-09-24Check
Output$0.56per 1M tokens2026-09-24Check

Mistral Small 3.1 24B sits at the cheap end of the market, well below the flagship tiers from the major labs and competitive with the other small open-weight models it shares a bracket with. Output costs modestly more than input, so the economics stay comfortable even on tasks that generate long responses, and self-hosting removes the per-token cost entirely if you have the hardware.

What a month costsSurge45°
WorkloadInput / monthOutput / monthCost
A small product team20M tokens5M tokens$9.80
A busy support assistant200M tokens40M tokens$92.40
A document pipeline1000M tokens100M tokens$406.50

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.

07 / specs

Specifications

As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.

SpecificationSurge45°
Context window128,000 tokens
Maximum output102,400 tokens
Modalitiestext, image
Released17/03/2025
StatusCurrent
Catalogue identifiermistralai/mistral-small-3.1-24b-instruct

08 / lineage

Lineage

What this model replaced, what replaced it, and what else its provider has in the field.

Also from Mistral

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 we hold. It fills in as the provider ships successors, or as we pick up the models that came before this one.

How we decide what counts as evidence

10 / notes

Our notes

What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.

What it changes for you

A small open-weight model like Mistral Small 3.1 24B widens the set of engines that might summarise your category, because more products can afford to run retrieval and summarisation on every query. Smaller models lean harder on what they retrieve rather than what they recall, so clear, well-structured pages that state plainly what you do and who you serve carry more weight than brand familiarity.

Where buyers meet this model

Buyers meet Mistral Small 3.1 24B mostly through the API and through Le Chat, Mistral's consumer assistant, rather than as a named model in a product they use. More often it is invisible: it is the model behind a vendor's document reader, support triage or in-app assistant, chosen because it is cheap enough to run on every request.

Change log

Nothing published here yet. Changes appear within hours of a provider announcing them.

11 / questions

Questions

The things people ask about this model, answered from what is on this page rather than from anywhere else.

Can Mistral Small 3.1 24B read images?
Yes. It accepts both text and image input, so it can work over screenshots, scanned documents and charts alongside a written prompt.
How does it differ from Mistral Small 3?
Mistral Small 3.1 24B is the upgraded variant of Mistral Small 3, keeping the 24 billion parameter size while adding multimodal input and improving text reasoning.
Is Mistral Small 3.1 24B worth self-hosting?
At 24 billion parameters it is small enough to run on your own hardware, which is the main reason teams choose it over an API-only model of similar cost, particularly where data cannot leave the estate.
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Is Mistral Small 3.1 24B recommending you?

Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.

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