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
Qwen3-235B-A22B, released by Alibaba's Qwen team on April 28, 2025, is a Mixture-of-Experts large language model with 235 billion total parameters and 22 billion active parameters per inference. It features a 256K token context window, hybrid thinking capabilities (both reasoning and direct generation modes), and was trained on 36 trillion tokens across 119 languages. Qwen3-235B targets complex reasoning, multilingual tasks, and open-source deployments under the Apache 2.0 license.
8 months newer

Phi-3.5-MoE Instruct
Microsoft
2024-08-22
Qwen3-235B-A22B
Alibaba / Qwen
2025-04-28
Context window and performance specifications
Average performance across 1 common benchmarks
Phi-3.5-MoE Instruct
Qwen3-235B-A22B
Performance comparison across key benchmark categories
Phi-3.5-MoE Instruct
Qwen3-235B-A22B
Available providers and their performance metrics
Phi-3.5-MoE Instruct
Qwen3-235B-A22B
OpenRouter
Phi-3.5-MoE Instruct
Qwen3-235B-A22B
Phi-3.5-MoE Instruct
Qwen3-235B-A22B