PhiBench
Progress Over Time
Interactive timeline showing model performance evolution on PhiBench
PhiBench Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Microsoft | 14B | — | — | ||
| 2 | Microsoft | 14B | — | — | ||
| 3 | Microsoft | 15B | — | — |
What is PhiBench?
PhiBench is an internal benchmark designed to evaluate diverse skills and reasoning abilities of language models, covering a wide range of tasks including coding (debugging, extending incomplete code, explaining code snippets) and mathematics (identifying proof errors, generating related problems). Created by Microsoft's research team to address limitations of standard academic benchmarks and guide the development of the Phi-4 model.
PhiBench is a text benchmark evaluating models on math, reasoning, and general tasks. LLM Stats tracks 3 models on this benchmark, scored on a 0–1 scale. The current average is 0.7, with the leader at 0.7.
Compare leaders on the best AI for math, best AI for reasoning and best AI for general leaderboards.
Current leaders
Phi 4 Reasoning Plus from Microsoft currently leads the PhiBench leaderboard with a score of 0.742 across 3 evaluated AI models.
Source paper
- Title
- Phi-4 Technical Report
- Authors
- Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, and 23 others
- Published
- arXiv
- 2412.08905
Abstract
We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size -- especially on reasoning-focused benchmarks -- due to improved data, training curriculum, and innovations in the post-training scheme.
FAQ
Common questions about the PhiBench benchmark and leaderboard.