llmrun

Mistral Small Instruct 2409 — Benchmarks

Benchmark scores for Mistral Small Instruct 2409 aggregated from public leaderboards (2 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.

No llmrun Composite Score for Mistral Small Instruct 2409: the score is only published for a model measured on at least 4 of the boards in the score panel, spanning at least 2 benchmark categories and including a reasoning or coding board — or on 3 boards that narrow its score to a 90% range of at most 15.6 points. Either way the results must come from at least two different benchmarks. Its individual scores below stand on their own — see how the llmrun Composite Score gets calculated.

Mistral Small Instruct 2409 reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

  • DTBench

    49.5%

    median 68.7%open leader 93.9%

    Overall best: Claude Opus 5.5 Max 98.9%

    10th percentile · #63 of 70 open · #222 of 235 all

    Lab-reported

    Epoch AI ·

Mistral Small Instruct 2409 knowledge benchmarks

Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.

  • MMLU-Pro

    48.4%

    median 52.0%open leader 88.0%

    Overall best: Gemini-3.1-Pro 91.2%

    48th percentile · #77 of 146 open · #168 of 257 all

    Independent

    TIGER-Lab

Scores aggregated from public benchmark sources (Epoch AI: 1 board, TIGER-Lab: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.