Comprehensive side-by-side LLM comparison
Qwen2.5-Coder 7B Instruct leads with 2.7% 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-Coder-7B-Instruct is a 7-billion-parameter code-specialized model from Alibaba, released in November 2024 as part of the Qwen2.5-Coder family, trained on a curated corpus spanning 92 programming languages with emphasis on code generation, debugging, and fill-in-the-middle completion. Built on the Qwen2.5 architecture, it extends the base series' improvements in instruction-following and long-context handling to coding-specific tasks within a compact deployable footprint. It became popular for integration into IDE extensions, CI pipelines, and self-hosted code assistant tools.
2 months newer

Phi-3.5-MoE Instruct
Microsoft
2024-08-22
Qwen2.5-Coder 7B Instruct
Alibaba / Qwen
2024-11-12
Average performance across 1 common benchmarks
Phi-3.5-MoE Instruct
Qwen2.5-Coder 7B Instruct
Performance comparison across key benchmark categories
Phi-3.5-MoE Instruct
Qwen2.5-Coder 7B Instruct
Available providers and their performance metrics
Phi-3.5-MoE Instruct
Qwen2.5-Coder 7B Instruct
Phi-3.5-MoE Instruct
Qwen2.5-Coder 7B Instruct