Phi-4
BenchLM is tracking Phi-4, but sourced benchmark results are not published on the site yet. This page currently shows the model metadata we can verify now, and score-level benchmark coverage will appear once public evaluations land.
Phi-4 is a open weight model with a 16K token context window. It processes queries without explicit chain-of-thought reasoning, offering faster response times and lower token usage.
This profile currently has 0 sourced benchmarks on BenchLM, so the benchmark sections below are intentionally marked as coming soon.
Its strongest category is Multilingual (#79), while its weakest is Agentic (#93). This performance profile makes it a well-rounded choice across a range of tasks.
Ranking Distribution
Category rank across 6 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
#93Coding
Reasoning
#91Knowledge
Math
Multilingual
#79Multimodal
#87Inst. Following
Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how Phi-4 stacks up against similar models
Frequently Asked Questions
How does Phi-4 perform overall in AI benchmarks?
BenchLM is tracking Phi-4, but sourced benchmark coverage is still coming soon. We currently list its creator, model type, and context window while we wait for public benchmark results.
Is Phi-4 open source?
Yes, Phi-4 is an open weight model created by Microsoft, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Phi-4 have full benchmark coverage on BenchLM?
Not yet. Phi-4 currently has 0 published benchmark scores out of the 178 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of Phi-4?
Phi-4 has a context window of 16K, which determines how much text it can process in a single interaction.
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