Microsoft · model passport
Phi 3 5 Vision Instruct
phi-3-5-vision-instruct- Open weights
- MIT licence
- 4.1B params
- Released 2024-08-20(curated)
- Best percentile
- 1.3Vision Arena
- Cohort standing
- 37.8median 37.5
Thin coverage — read with caution
This model appears on only 1 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 1 of 27 tracked axes. Every figure below is the model's own published result on that board.
On only 1 board(s), a ranked list of strengths would be false authority — the per-axis figures are shown below instead, without a verdict.
- Vision Arena1.3 pct919Verify →
- OverallNot benchmarked
- WebDev 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
- 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
- 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.
How to run it
Run it yourself
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "microsoft/Phi-3.5-vision-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-3.5-vision-instruct stands at 37.8 (raw mean 1.3) — measured on only 1 of 27 axes, so the standing is shrunk toward the neutral 50.
phi-3.5-vision-instruct stands at 37.8 versus a cohort median of 37.5 across 127 models released within 180 days — above the peer median.
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 →