Microsoft
Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4...
Running this yourself: can likely run on your own machine.
34.2
Quality Score
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Arena ELO
Unknown
Parameters
131K
Context
Use this section to answer one simple question first: how much outside evidence do we have that this model performs well? Structured benchmark scores appear first, then official provider evidence, then live arena signal.
This model has normalized benchmark rows, so scores here are directly comparable across benchmark sources.
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Oct 2025
Released
These are recent benchmark or leaderboard claims from official provider sources. They are useful for freshness and context, but they are not treated the same as normalized independent benchmark rows.
Phi-4 Mini Instruct Benchmark Update
Quality: 8.4/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.465% | HumanEval: 0.126%
View sourcePhi-4 Mini Instruct Benchmark Update
Quality: 8.4/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.465% | HumanEval: 0.126%
View sourcePhi-4 Mini Instruct Benchmark Update
Quality: 8.4/100 | Price: $0/M tokens | Output: 43.438 tok/s | MMLU: 0.465% | HumanEval: 0.126%
View sourcePhi-4 Mini Instruct Benchmark Update
Quality: 8.4/100 | Price: $0/M tokens | Output: 44.149 tok/s | MMLU: 0.465% | HumanEval: 0.126%
View sourcePhi-4 Mini Instruct Benchmark Update
Quality: 8.4/100 | Price: $0/M tokens | Output: 44.194 tok/s | MMLU: 0.465% | HumanEval: 0.126%
View sourcePhi-4 Mini Instruct Benchmark Update
Quality: 8.4/100 | Price: $0/M tokens | Output: 44.226 tok/s | MMLU: 0.465% | HumanEval: 0.126%
View source