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-3.5-mini-instruct

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.

Google

Gemini 1.5 Pro

Google

2024-05-01

Microsoft

Phi-3.5-mini-instruct

Microsoft

2024-08-23

3 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 1.5 Pro

Input:$2.50
Output:$10.00
Microsoft

Phi-3.5-mini-instruct

$12.30 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

Google

Gemini 1.5 Pro

Larger context
Max Context:2.1M
Microsoft

Phi-3.5-mini-instruct

Max Context:256.0K
Parameters:3.8B

Average performance across 9 common benchmarks

Google

Gemini 1.5 Pro

+24.6%
Average Score:83.6%
Microsoft

Phi-3.5-mini-instruct

Average Score:59.0%

Performance comparison across key benchmark categories

Google

Gemini 1.5 Pro

reasoning
+19.1%
93.3%
math
+14.1%
74.9%
code
+8.3%
74.5%
general
+13.6%
68.9%
Microsoft

Phi-3.5-mini-instruct

reasoning
74.2%
math
60.9%
code
66.2%
general
55.4%
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

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-3.5-mini-instruct

1 providers

Azure

Throughput: 23 tok/s
Latency: 0.52ms
Google

Gemini 1.5 Pro

+24.6%
Avg Score:83.6%
Providers:1
Microsoft

Phi-3.5-mini-instruct

Avg Score:59.0%
Providers:1