We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone.
Recent articles
- Useful patterns for building HTML tools - 10th December 2025
- Under the hood of Canada Spends with Brendan Samek - 9th December 2025
- Highlights from my appearance on the Data Renegades podcast with CL Kao and Dori Wilson - 26th November 2025