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Microsoft · model passport

Phi 3 Mini 4k Instruct

phi-3-mini-4k-instruct
  • Open weights
  • MIT licence
  • 3.8B params
  • Released 2024-04-23(curated)
5/27axes covered
Overall · LMArena
1,128#370 of 396
Best percentile
10.7Coding
Cohort standing
24.3median 33.1

What the data says

Benchmarked on 5 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 6.8 pct — Overall (1,128)
  • 8.3 pct — Instruction following (1,113)
  • 8.9 pct — LiveBench (24.5%)

Uneven: 3.9 percentile points between its best and worst axis — 10.7 pct on Coding versus 6.8 pct on Overall.

  • Coding10.7 pct1,188Verify →
  • Hard prompts9.6 pct1,154Verify →
  • LiveBench8.9 pct24.5%Verify →
  • Instruction following8.3 pct1,113Verify →
  • Overall6.8 pct1,128Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorymicrosoft/Phi-3-mini-4k-instruct
Parameters3.8B
ArchitecturePhi3ForCausalLM
LicenceMIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "microsoft/Phi-3-mini-4k-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • phi-3-mini-4k-instruct stands at 24.3 (raw mean 8.9) — measured on 5 of 27 axes.

    standingtemplateVerify →

  • phi-3-mini-4k-instruct stands at 24.3 versus a cohort median of 33.1 across 118 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →