Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 29.3% higher average benchmark score. Phi-4-multimodal-instruct offers 215.0K more tokens in context window than Gemini 1.0 Pro. Phi-4-multimodal-instruct is $1.85 cheaper per million tokens. Phi-4-multimodal-instruct supports multimodal inputs. Overall, Phi-4-multimodal-instruct is the stronger choice for coding tasks.
Gemini 1.0 Pro is a language model developed by Google. The model shows competitive results across 9 benchmarks. Notable strengths include BIG-Bench (75.0%), MMLU (71.8%), WMT23 (71.7%). The model is available through 1 API provider. Released in 2024, it represents Google's latest advancement in AI technology.
Microsoft
Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. 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 Microsoft's latest advancement in AI technology.
11 months newer
Gemini 1.0 Pro
2024-02-15
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Cost per million tokens (USD)
Gemini 1.0 Pro
Phi-4-multimodal-instruct
Context window and performance specifications
Average performance across 22 common benchmarks
Gemini 1.0 Pro
Phi-4-multimodal-instruct
Gemini 1.0 Pro
2024-02-01
Phi-4-multimodal-instruct
2024-06-01
Available providers and their performance metrics
Gemini 1.0 Pro
Phi-4-multimodal-instruct
Gemini 1.0 Pro
Phi-4-multimodal-instruct
Gemini 1.0 Pro
Phi-4-multimodal-instruct
DeepInfra