Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 25.7% higher average benchmark score. Gemini 2.0 Flash-Lite offers 800.8K more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. Gemini 2.0 Flash-Lite supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
Gemini 2.0 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in MATH (86.8%), FACTS Grounding (83.6%), Global-MMLU-Lite (78.2%). With a 1.1M 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 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.
5 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Gemini 2.0 Flash-Lite
2025-02-05
Cost per million tokens (USD)
Gemini 2.0 Flash-Lite
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 41 common benchmarks
Gemini 2.0 Flash-Lite
Phi-3.5-mini-instruct
Gemini 2.0 Flash-Lite
2024-06-01
Available providers and their performance metrics
Gemini 2.0 Flash-Lite
Phi-3.5-mini-instruct
Gemini 2.0 Flash-Lite
Phi-3.5-mini-instruct
Gemini 2.0 Flash-Lite
Phi-3.5-mini-instruct
Azure