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Mistral Small 3.2 24B

mistralai/Mistral-Small-3.2-24B-Instruct-2506

A 24B multilingual instruction model with vision and function-calling examples for capable self-hosted systems.

Direct answer

Where Mistral Small 3.2 24B fits.

A 24B instruction model intended for capable local or self-hosted systems.

The pinned Mistral model card includes vision and function-calling examples and lists multilingual coverage.

Identity before opinion

The facts this release can support.

Exact model ID
mistralai/Mistral-Small-3.2-24B-Instruct-2506
Release identity
Mistral Small / 95a6d26c4bfb
Parameters
24.01B 24,01,13,61,280 recorded
Documented context
1,31,072 tokens Publisher maximum, not a local-fit promise
Input → output
text + image → text
Weights access
Publicly discoverable files
Listed licence
Apache-2.0 Read the complete publisher terms
API status
Not reviewed No API identity, price or data policy asserted
Use-case fit

Consider it when…

  • Evaluate multilingual assistants on workstation-class hardware
  • Prototype vision and function-calling workflows
  • Test self-hosted instruction quality before selecting an API route
Limits and cautions

Check carefully when…

  • Workstation-class memory does not guarantee acceptable speed
  • Function calling requires a compatible serving stack and exact template
  • Vision and language coverage need domain-specific human evaluation
Transparent local planning

What 4-bit weights may ask of the machine.

These are Maviter calculations at an 8,192-token context—not publisher hardware requirements or proof that a runtime supports this release.

01 / Weight floor11.18 GiB

Parameters × 4 bits, converted to GiB.

02 / Runtime + KV3+ GiB

Runtime reserve and formula-backed KV cache.

03 / Planning floor15 GiB

Calculated working set rounded upward.

04 / Comfortable band24 GiB

A planning band with headroom—not a guarantee.

Before adoption

A three-check evaluation is more useful than a leaderboard.

Run the exact publisher release, on the intended runtime, with inputs from the work it must actually perform.

  1. 01Verify the documented serving and tokenizer versions
  2. 02Test function-call arguments against a strict schema
  3. 03Measure representative multilingual and visual inputs
Evidence ledger

Publisher sources, not copied binaries.

Pinned repository treemistralai/Mistral-Small-3.2-24B-Instruct-250695a6d26c4bfb886c58daf9d3f7332c857cb27b43Open source ↗Pinned model cardCapabilities, licence and usage notesREADME.md at the recorded revisionRead card ↗

The evidence ledger pins an exact publisher revision and monitors it for drift. A source check does not independently reproduce benchmarks or certify production suitability.

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Common questions

Know the boundary before downloading.

Can Mistral Small 3.2 24B run locally?

The publisher provides discoverable weights, but runtime support and performance are machine-specific. Use Maviter's fit calculator, then run one controlled test.

Is this an API model?

This record documents downloadable weights. It does not assert a provider-hosted API ID, current price, rate limit or data policy.

Does Maviter host or redistribute the files?

No. The pinned links return to the publisher. Maviter does not mirror binaries or endorse unaudited third-party quantisations.