Azure · model passport
Phi-4 Multimodal Instruct
phi-4-multimodal-instruct- Best percentile
- 16.1MMLU-Pro (Artificial Analysis)
- Composite standing
- 36.4shrunk · 2/27 axes
Thin coverage — read with caution
This model appears on only 2 of 27 benchmark axes considered. The overall standing has been shrunk toward the neutral 50 to reflect the small sample, and no weaknesses have been claimed — a low percentile on 2 axes is a coverage artifact, not a measured weakness.
What the data says
Benchmarked on 2 of 27 tracked axes. Every figure below is the model's own published result on that board.
On only 2 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.
Uneven: 0.5 percentile points between its best and worst axis — 16.1 pct on MMLU-Pro (Artificial Analysis) versus 15.6 pct on LiveCodeBench (Artificial Analysis).
- MMLU-Pro (Artificial Analysis)16.1 pct48.5%Verify →
- LiveCodeBench (Artificial Analysis)15.6 pct13.1%Verify →
- OverallNot benchmarked
- 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
- LiveBenchNot benchmarked
- BFCL v4 (function calling)Not benchmarked
- SWE-bench Verified (live board)Not benchmarked
- Terminal-Bench 4.0Not benchmarked
- IFBench (Artificial Analysis)Not benchmarked
- HHEM Hallucination (Vectara)Not benchmarked
- SWE-bench VerifiedNot benchmarked
- Terminal-BenchNot benchmarked
- CodingNot benchmarked
- Instruction followingNot benchmarked
- Hard promptsNot 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.
Phi-4 Multimodal Instruct stands at 36.4 (raw mean 15.9) — measured on only 2 of 27 axes, so the standing is shrunk toward the neutral 50.
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 →