Comprehensive side-by-side LLM comparison
. Both models have their strengths depending on your specific coding needs.
Moonshot AI
Kimi K2.5, released by Moonshot AI in January 2026, is an updated Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters. It builds on Kimi K2 with improved coding performance across multiple languages and an expanded context window. Kimi K2.5 targets agentic development workflows, polyglot code generation, and open-source deployments requiring large-scale MoE reasoning.
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.
1 year newer

Phi-3.5-MoE Instruct
Microsoft
2024-08-22
Kimi K2.5
Moonshot AI
2026-01
Context window and performance specifications
Available providers and their performance metrics
Kimi K2.5
Moonshot AI
Phi-3.5-MoE Instruct
Kimi K2.5
Phi-3.5-MoE Instruct
Kimi K2.5
Phi-3.5-MoE Instruct