Mistral Small 3.1 released
Mistral · Model release
Source confirmed
Data as of October 14, 2026 · How the score is built
EstablishedOpen WeightNon-Reasoning
Released Mar 17, 2025128K contextMistral Small 3.1 24B Instruct model card
Mistral Small 3.1
Decision readingMistral Small 3.1 is tracked, but not publicly ranked yet. The profile exposes 7 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Released Mar 17, 2025 — see all recent releases
Mistral Small 3.1 will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
Unranked
field median 49.8Not eligible for a public rank
Price
Not listed
input median $0.80No comparable first-party hosted token rate
Speed
Not measured
field median 90 tok/sTime to first token not measured
Context
128Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Published rows are visible, but no category has enough eligible evidence for a comparative rank.
Validate before choosing
7 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 7 displayable benchmark rows
Follow model changesCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| CodingWeight 20%1 benchmarkVerified | Score pending | 1 benchmark | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalRank Not rankedWeight 12%2 benchmarksVerified | 18.8 | 2 benchmarks | Verified | |||
| KnowledgeRank Not rankedWeight 12%4 benchmarksVerified | 51.4 | 4 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
7 of 670 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- AgenticNot measured
- Coding1/1 verified
- ReasoningNot measured
- Multimodal2/2 verified
- Knowledge4/4 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
Verified sourceProvisionalNot measured
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HumanEvalEvaluating Large Language Models Trained on Code | Score88.4% | Versus best verified row Best verified: o4-mini · 97.3% | Gap8.9 behind | WeightDisplay only | Provider exact |
Multimodal2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMU-ProMassive Multi-discipline Multimodal Understanding Pro | Score49.3% | Versus best verified row Best verified: Gemini 3.5 Flash · 83.6% | Gap34.3 behind | WeightWeighted 40% | Provider exact |
| MMMUMassive Multi-discipline Multimodal Understanding | Score64.0% | Versus best verified row Best verified: Qwen3.5 397B · 85.0% | Gap21 behind | WeightDisplay only | Provider exact |
Knowledge4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-ProMassive Multitask Language Understanding Professional | Score66.8% | Versus best verified row Best verified: Qwen3.7 Max · 89.6% | Gap22.8 behind | Weight6% ref. weight | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score46.0% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap50 behind | Weight3% ref. weight | Provider exact |
| MMLUMassive Multitask Language Understanding | Score80.6% | Versus best verified row Best verified: o1 · 91.8% | Gap11.2 behind | WeightDisplay only | Provider exact |
| SimpleQAMeasuring Short-Form Factuality in Large Language Models | Score10.4% | Versus best verified row Best verified: GPT-4.5 · 62.5% | Gap52.1 behind | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 7 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Base entry
Radar
Full release historyMistral Small 3.1 release history
Radar confirmed these at the source. Use Mistral Small 3.1 in your work? Explore Radar to follow supported changes and choose your alerts.
Radar
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 128K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Established
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track Mistral Small 3.1, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
Mistral Small 3.1 is a open weight model with a 128K context window. No explicit reasoning mode is documented in this profile.
Official exact values from Mistral AI's Mistral Small 3.1 24B Instruct model card, instruction evaluation tables. BenchLM stores mmlu, mmluPro, gpqa, simpleQa, humaneval, mmmu, mmmuPro. Each value's source and evaluation setting is recorded in row-level source verification. Kept out of rankings because the published results cover few of the benchmarks BenchLM ranks on.
7 of 670 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Last updated October 14, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Mistral Small 3.1 perform overall in AI benchmarks?
Mistral Small 3.1 has 7 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is Mistral Small 3.1 good for knowledge and understanding?
Mistral Small 3.1 has source-displayable benchmark coverage for knowledge and understanding, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Mistral Small 3.1 good for coding and programming?
Mistral Small 3.1 has source-displayable benchmark coverage for coding and programming, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Mistral Small 3.1 good for multimodal and grounded tasks?
Mistral Small 3.1 has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Mistral Small 3.1 open source?
Mistral Small 3.1 is an open-weight model from Mistral. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Mistral Small 3.1 have full benchmark coverage on BenchLM?
No. Mistral Small 3.1 currently has 7 source-displayable rows across 670 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Mistral Small 3.1?
Mistral Small 3.1 has a reported context window of 128K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.