Comprehensive side-by-side LLM comparison
Qwen2.5 32B Instruct leads with 3.2% higher average benchmark score. 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
Qwen2.5-32B-Instruct is a 32-billion-parameter open-weight model from Alibaba's Qwen team, released in September 2024 as part of the Qwen2.5 series trained on 18 trillion tokens. The model is positioned as a high-capability option for developers with access to multi-GPU setups or high-VRAM hardware, offering strong performance on coding, structured reasoning, and multilingual tasks while remaining fully open under Apache 2.0. Its 128K context window and support for structured output generation made it a popular choice for document processing and agentic workflows in the open-source community.
28 days newer

Phi-3.5-MoE Instruct
Microsoft
2024-08-22
Qwen2.5 32B Instruct
Alibaba / Qwen
2024-09-19
Average performance across 1 common benchmarks
Phi-3.5-MoE Instruct
Qwen2.5 32B Instruct
Performance comparison across key benchmark categories
Phi-3.5-MoE Instruct
Qwen2.5 32B Instruct
Available providers and their performance metrics
Phi-3.5-MoE Instruct
Qwen2.5 32B Instruct
Phi-3.5-MoE Instruct
Qwen2.5 32B Instruct