Comprehensive side-by-side LLM comparison
Both models show comparable benchmark performance. Both models have their strengths depending on your specific coding needs.
Microsoft
Phi-3.5-MoE-instruct is a sparse mixture-of-experts model from Microsoft's Phi research team, released in August 2024 with 42 billion total parameters across 16 experts and approximately 6.6 billion active parameters per forward pass. The model applies Microsoft's small-data, high-quality training philosophy — developed across earlier Phi generations — to a MoE architecture, targeting reasoning quality comparable to much larger dense models at a fraction of the inference compute. Released under the MIT license, it was notable in the research community for demonstrating that MoE efficiency gains could be realized at smaller total parameter counts than typical large-scale MoE deployments.
Alibaba / Qwen
Qwen2-72B-Instruct is a 72-billion-parameter language model released by Alibaba's Qwen team in June 2024, serving as the flagship of the Qwen2 generation and representing a major step in open-weight multilingual modeling. Trained on data spanning 30+ languages with strong coverage of code and structured reasoning, the model was among the first openly released 70B-class models to demonstrate competitive performance across diverse benchmarks. It established the foundation architecture and training methodology that the Qwen2.5 series would later extend.
2 months newer
Qwen2 72B Instruct
Alibaba / Qwen
2024-06-07

Phi-3.5-MoE Instruct
Microsoft
2024-08-22
Average performance across 1 common benchmarks
Phi-3.5-MoE Instruct
Qwen2 72B Instruct
Performance comparison across key benchmark categories
Phi-3.5-MoE Instruct
Qwen2 72B Instruct
Available providers and their performance metrics
Phi-3.5-MoE Instruct
Qwen2 72B Instruct
Phi-3.5-MoE Instruct
Qwen2 72B Instruct