llmrun

Phi 3.5 Mini Instruct — Benchmarks

Benchmark scores for Phi 3.5 Mini Instruct aggregated from public leaderboards (4 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 Phi 3.5 Mini Instruct: 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.

Phi 3.5 Mini Instruct reasoning benchmarks

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

  • PIQA

    81.0%

    median 80.1%open leader 88.6%

    Overall best: GPT 4o Mini (Jul 18, 2024) 88.7%

    59th percentile · #17 of 40 open · #31 of 59 all

    Lab-reported

    Epoch AI ·

Phi 3.5 Mini Instruct math benchmarks

Symbolic and quantitative problem-solving, from competition math to multi-step calculation.

  • GSM8K

    86.2%

    median 52.3%open leader 94.5%

    87th percentile · #9 of 62 open · #13 of 93 all

    Lab-reported

    Epoch AI ·

Phi 3.5 Mini Instruct knowledge benchmarks

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

  • MMLU-Pro

    47.9%

    median 52.1%open leader 88.0%

    Overall best: Gemini-3.1-Pro 91.2%

    46th percentile · #79 of 145 open · #171 of 257 all

    Independent

    TIGER-Lab

Other results (1)leaderboard-only, not part of the llmrun Score
  • BoolQ

    78.0%

    median 76.4%open leader 89.4%

    Overall best: T5 11B 91.2%

    57th percentile · #17 of 38 open · #43 of 76 all

    Lab-reported

    Epoch AI ·

Scores aggregated from public benchmark sources (Epoch AI: 3 boards, 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.