Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 2.4% higher average benchmark score. Gemma 3 27B offers 6.1K more tokens in context window than Phi-3.5-mini-instruct. Both models have similar pricing. Gemma 3 27B supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
Gemma 3 27B is a multimodal language model developed by Google. It achieves strong performance with an average score of 65.4% across 26 benchmarks. It excels particularly in GSM8k (95.9%), IFEval (90.4%), MATH (89.0%). It supports a 262K token context window for handling large documents. The model is available through 2 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. 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.
6 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
Gemma 3 27B
2025-03-12
Cost per million tokens (USD)
Gemma 3 27B
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 50 common benchmarks
Gemma 3 27B
Phi-3.5-mini-instruct
Available providers and their performance metrics
Gemma 3 27B
DeepInfra
Novita
Gemma 3 27B
Phi-3.5-mini-instruct
Gemma 3 27B
Phi-3.5-mini-instruct
Phi-3.5-mini-instruct
Azure