Comprehensive side-by-side LLM comparison
. Both models have their strengths depending on your specific coding needs.
Moonshot AI
Kimi K2, released by Moonshot AI on July 11, 2025, is an open-weight Mixture-of-Experts large language model with 1 trillion total parameters and 32 billion active parameters per inference. It features a 256K token context window (expanded from 128K in an September 2025 update) and demonstrated strong performance on agentic coding benchmarks. Kimi K2 targets software engineering agents, tool-use workflows, and open-source deployments under a modified MIT license.
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.
10 months newer

Phi-3.5-MoE Instruct
Microsoft
2024-08-22
Kimi K2
Moonshot AI
2025-07-11
Context window and performance specifications
Available providers and their performance metrics
Kimi K2
Moonshot AI
Phi-3.5-MoE Instruct
Kimi K2
Phi-3.5-MoE Instruct
Kimi K2
Phi-3.5-MoE Instruct