Skip to content
ModelCap

Mistral Small 3.2 24B

Official publisher releaseOpen weights6.9d #248 → #251

Mistral AI·#251 of 352 on the current board·cheaper than #250 gpt-oss-safeguard-20b

Mistral Small 3.2 24B is a language model from Mistral AI. It ranks #251 of 352 on ModelCap's current board, with an Index score of 27.6, modeled from its measured predecessor. Its score range of 13.9–41.3 spans positions #155–#304. It is listed at $0.075 per million input tokens and $0.20 per million output tokens across 4 providers. It reads up to 256K tokens of context, and its weights are openly downloadable.

Figures as of 6 Nov 2026, 20:32 UTC · download the public dataset · methodology

ModelCap Index · Current board
27.6#251
Modeled from its measured predecessor · range 13.9–41.3
Market Gravity
35.1Decreased by 0.1
Secondary market signal
Input
$0.075
per 1M tokens
Output
$0.20
per 1M tokens
Context
256K
tokens
Providers
4
endpoints

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
DeepInfrafp8Cheapest$0.075$0.20128Kfp8100.0%
Venicefp8$0.094$0.25256Kfp8100.0%
Mistraleu$0.10$0.3033K—100.0%
Parasailbf16$0.09$0.30131Kbf16100.0%

Other versions (1)

ModelCap Index · Current board

Methodology
27.6
Modeled from its measured predecessor
Current board
#251 of 352
Score range
13.9–41.3
Rank range
#155–#304
Technical details
Public rank basis
Modeled · succession (architecture)
Evidence label
Modeled · succession · publisher-corpus-prior over 210 held-out anchors (20%); opens from measured predecessor mistralai/mistral-small-3.1-24b-instruct (80%); shrunk 2.8 up toward the measured corpus
Index support
31.3% · limited
Identity binding
Exact catalogue product · mistralai/mistralai/mistral-small-3.2-24b-instruct · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata mistralai/Mistral-Small-3.2-24B-Instruct-2506@95a6d26c4bfb886c58daf9d3f7332c857cb27b43 (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
27.6
How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate
27.6 ± 10.7
Independent share
0%
Shrink toward corpus
+2.8
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Publisher corpus prior · not independent
publisher-corpus-prior over 210 held-out anchors
38.5 ± 23.820%
Family succession · not independent
opens from measured predecessor mistralai/mistral-small-3.1-24b-instruct
24.8 ± 12.080%
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
—
Evidence status
No admitted measured evidence
General preference
—
Coding
—
Agents & tools
—
Reasoning
—
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
17.0
Liquidity (providers × uptime)
72.1
Open reach (HF downloads)
46.4
Surface (context / tools / modalities)
65.1
Freshness
14.7
  • · OpenRouter popularity #126
  • · 4 live providers
  • · HF 30d downloads 110264
  • · Catalogue: context 256000, tools, modalities Image/Text
  • · First seen 2025-06-20

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
32.1
17.9–46.3 uncertainty interval
Excluded from rank
Confidence
78%
Training support
220 models · 128 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
8.3 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
80.0
strong · 80% metadata coverage
Not capability
Artifact reproducibility
100.0
Access & legal clarity
100.0
Deployability
100.0
Evaluation provenance
0.0

Not yet published: admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
35.1
OpenRouter weekly popularity
#126 of 339

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
17.0

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
72.1

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
46.4

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
14.7

Time since first public availability, on a six-month half-life.

Specification

Model ID
mistralai/mistral-small-3.2-24b-instruct
Context window
256K tokens
Declared output cap
16K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

Inputs
Image, Text
Outputs
Text
Tokenizer
Mistral
Cached input tokens
Not offered
First seen on OpenRouter
20 Jun 2025
Knowledge cutoff
2023-10-31

Capabilities

  • Not supported: Reasoning
  • Supported: Tool use
  • Supported: Structured output
  • Supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
apache-2.0
Pinned revision
95a6d26c4bfb
Parameters
24.0B
Architecture
Mistral3ForConditionalGeneration
Model type
mistral3
Hugging Face downloads (30d)
110K
Downloads (all time)
5.6M
Likes
644
Repository updated
22 Dec 2025, 10:16 UTC

Benchmark leaderboards featuring Mistral Small 3.2 24B

All leaderboards
Public benchmark boards on which Mistral Small 3.2 24B has a published result
LeaderboardScoreSource rankConfigurationPublished
BFCL V4 function-calling leaderboardUC Berkeley (Gorilla)37.2#38 of 83Function calling12 Apr 2026

Alternatives to Mistral Small 3.2 24B, by the numbers

Ranked neighbours

Models within 3 places of #251 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked Mistral AI models

The same publisher's other current models with a public Index position.

Mistral Small 3.2 24B: common questions

How does Mistral Small 3.2 24B rank among AI models?

Mistral Small 3.2 24B holds ModelCap Index position #251 of 352 ranked language models as of 6 November 2026, with an Index score of 27.6 (interval 13.9–41.3); evidence: modeled · succession. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Mistral Small 3.2 24B cost per 1M tokens?

Mistral Small 3.2 24B is listed at $0.075 per 1M input tokens and $0.20 per 1M output tokens as of 6 November 2026 across 4 providers. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.25 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Mistral Small 3.2 24B?

Mistral Small 3.2 24B has a published context window of 256K tokens (256,000). The snapshot lists a 16,384 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is Mistral Small 3.2 24B open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is mistralai/Mistral-Small-3.2-24B-Instruct-2506 on Hugging Face.

Which API providers serve Mistral Small 3.2 24B?

4 providers serve Mistral Small 3.2 24B through OpenRouter as of 6 November 2026: DeepInfra, Parasail, Venice and Mistral. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Mistral Small 3.2 24B been evaluated on?

Mistral Small 3.2 24B has published results on 1 public board tracked by ModelCap as of 6 November 2026: BFCL V4 37.2 (#38 of 83). Each board page ranks every tracked model on that benchmark. The ModelCap Index scores the results its methodology admits, with published uncertainty, and shows reference boards without scoring them.

When was Mistral Small 3.2 24B released?

Mistral Small 3.2 24B first appeared in the catalogue on 20 Jun 2025, with a published knowledge cutoff of 2023-10-31. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.