Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 15.3% higher average benchmark score. Gemini 2.5 Pro offers 858.1K more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $11.05 cheaper per million tokens. Gemini 2.5 Pro supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
Gemini 2.5 Pro is a multimodal language model developed by Google. It achieves strong performance with an average score of 69.6% across 16 benchmarks. It excels particularly in MRCR (93.0%), AIME 2024 (92.0%), Global-MMLU-Lite (88.6%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 2 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.
Microsoft
Phi-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.
9 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Gemini 2.5 Pro
2025-05-20
Cost per million tokens (USD)
Gemini 2.5 Pro
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 46 common benchmarks
Gemini 2.5 Pro
Phi-3.5-mini-instruct
Gemini 2.5 Pro
2025-01-31
Available providers and their performance metrics
Gemini 2.5 Pro
ZeroEval
Gemini 2.5 Pro
Phi-3.5-mini-instruct
Gemini 2.5 Pro
Phi-3.5-mini-instruct
Phi-3.5-mini-instruct
Azure