Model Comparison

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

Google

Gemini 1.5 Pro

Google

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 4

Microsoft

Phi 4 is a language model developed by Microsoft. It achieves strong performance with an average score of 66.0% across 13 benchmarks. It excels particularly in MMLU (84.8%), HumanEval+ (82.8%), HumanEval (82.6%). The model shows particular specialization in math tasks with an average performance of 80.5%. 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.

Google

Gemini 1.5 Pro

Google

2024-05-01

Microsoft

Phi 4

Microsoft

2024-12-12

7 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 1.5 Pro

Input:$2.50
Output:$10.00
Microsoft

Phi 4

$12.29 cheaper
Input:$0.07
Output:$0.14

Performance Metrics

Context window and performance specifications

Google

Gemini 1.5 Pro

Larger context
Max Context:2.1M
Microsoft

Phi 4

Max Context:32.0K
Parameters:14.7B

Average performance across 7 common benchmarks

Google

Gemini 1.5 Pro

+3.3%
Average Score:79.1%
Microsoft

Phi 4

Average Score:75.8%

Performance comparison across key benchmark categories

Google

Gemini 1.5 Pro

math
74.9%
code
74.5%
general
+8.7%
68.9%
Microsoft

Phi 4

math
+5.6%
80.5%
code
+1.7%
76.1%
general
60.2%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 1.5 Pro

2023-11-01

Phi 4

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 1.5 Pro

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
Microsoft

Phi 4

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
Google

Gemini 1.5 Pro

+3.3%
Avg Score:79.1%
Providers:1
Microsoft

Phi 4

Avg Score:75.8%
Providers:1