‹ PublicAI Index
The LLM benchmark aggregator.
Phi-4 Mini Instruct
Microsoft
Strongest in Factual grounding (#103 of 104), weakest in Agents (#290 of 298). Above par in 3 of 9 scopes. Among the models it meets almost everywhere, it finishes behind Claude Opus 5.5 and Claude Opus 4.7 and ahead of GLM-4.6 and Qwen3 8B.
§ 1 · Profile
What it is good at
Bars run from 50 — the average of the models each source lists — so right of the line is above par. The middle column is the gap to whoever leads that scope.
Safety47.1−14.3#246/3292/3
Factual grounding26−44.7#103/1041/1
Secure code54.5−11.7#117/2701/1
Toxicity avoidance53.7−5.3#132/2681/1
Harm refusal49.3−12.2#161/2921/2
Jailbreak resistance51.4−13.6#164/2681/1
Fairness44.5−25.5#198/2921/2
Agents40−26.4#290/2981/6
Knowledge work30−40.7#205/2081/2
§ 2 · Head to head
What it beats, and what beats it
The same models turn up scope after scope. Counted once: where both were placed, who finished higher. Bars run right for Phi-4 Mini Instruct, left for the other.
§ 3 · Sources
Where the numbers come from
3 publications, 8 figures. Every one links to the page it was read from.
GDPval-AA -401
Enkrypt · Jailbreak risk 11.6%Enkrypt · Harmful content risk 39.4%Enkrypt · CBRN risk 13.7%Enkrypt · Toxicity risk 3.6%Enkrypt · Bias risk 86.3%Enkrypt · Insecure code risk 24%
Vectara · Factual consistency 76.5%
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[](https://publicai.io/model-index/m/phi-4-mini-instruct)