Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 3.4% higher average benchmark score. Gemini 1.5 Pro offers 1.8M more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $12.30 cheaper per million tokens. Gemini 1.5 Pro supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Gemini 1.5 Pro is a multimodal language model developed by Google. It achieves strong performance with an average score of 72.6% across 23 benchmarks. It excels particularly in XSTest (98.8%), HellaSwag (93.3%), GSM8k (90.8%). With a 2.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, 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.
3 months newer
Gemini 1.5 Pro
2024-05-01
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Cost per million tokens (USD)
Gemini 1.5 Pro
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 45 common benchmarks
Gemini 1.5 Pro
Phi-3.5-mini-instruct
Gemini 1.5 Pro
2023-11-01
Available providers and their performance metrics
Gemini 1.5 Pro
Phi-3.5-mini-instruct
Gemini 1.5 Pro
Phi-3.5-mini-instruct
Gemini 1.5 Pro
Phi-3.5-mini-instruct
Azure