Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Google

Gemini 1.0 Pro

Google

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

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%). The model shows particular specialization in general tasks with an average performance of 75.8%. 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.

Google

Gemini 1.0 Pro

Google

2024-02-15

Microsoft

Phi-4-multimodal-instruct

Microsoft

2025-02-01

11 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 1.0 Pro

Input:$0.50
Output:$1.50
Microsoft

Phi-4-multimodal-instruct

$1.85 cheaper
Input:$0.05
Output:$0.10

Performance Metrics

Context window and performance specifications

Google

Gemini 1.0 Pro

Max Context:41.0K
Microsoft

Phi-4-multimodal-instruct

Larger context
Max Context:256.0K
Parameters:5.6B

Average performance across 2 common benchmarks

Google

Gemini 1.0 Pro

Average Score:47.3%
Microsoft

Phi-4-multimodal-instruct

+11.5%
Average Score:58.8%

Performance comparison across key benchmark categories

Google

Gemini 1.0 Pro

general
51.4%
vision
47.9%
math
39.6%
Microsoft

Phi-4-multimodal-instruct

general
+24.3%
75.8%
vision
+21.8%
69.7%
math
+22.8%
62.4%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 1.0 Pro

2024-02-01

Phi-4-multimodal-instruct

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 1.0 Pro

1 providers

Google

Throughput: 120 tok/s
Latency: 0.4ms
Microsoft

Phi-4-multimodal-instruct

1 providers

DeepInfra

Throughput: 25 tok/s
Latency: 0.5ms
Google

Gemini 1.0 Pro

Avg Score:47.3%
Providers:1
Microsoft

Phi-4-multimodal-instruct

+11.5%
Avg Score:58.8%
Providers:1