Model Comparison

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

Google

Gemma 3n E4B

Google

Gemma 3n E4B is a multimodal language model developed by Google. It achieves strong performance with an average score of 64.6% across 11 benchmarks. It excels particularly in ARC-E (81.6%), BoolQ (81.6%), PIQA (81.0%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.

OpenAI

GPT-5 nano

OpenAI

GPT-5 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 5 benchmarks. It excels particularly in AIME 2025 (85.2%), HMMT 2025 (75.6%), GPQA (71.2%). It supports a 528K 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. Released in 2025, it represents OpenAI's latest advancement in AI technology.

Google

Gemma 3n E4B

Google

2025-06-26

OpenAI

GPT-5 nano

OpenAI

2025-08-07

1 month newer

Performance Metrics

Context window and performance specifications

Google

Gemma 3n E4B

Max Context:-
Parameters:8.0B
OpenAI

GPT-5 nano

Larger context
Max Context:528.0K

Performance comparison across key benchmark categories

Google

Gemma 3n E4B

general
59.6%
OpenAI

GPT-5 nano

general
+0.5%
60.2%
Knowledge Cutoff
Training data recency comparison

GPT-5 nano

2024-05-30

Gemma 3n E4B

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemma 3n E4B

0 providers
OpenAI

GPT-5 nano

2 providers

ZeroEval

Throughput: 500 tok/s
Latency: 0.3ms

OpenAI

Throughput: 500 tok/s
Latency: 0.3ms
Google

Gemma 3n E4B

Avg Score:0.0%
Providers:0
OpenAI

GPT-5 nano

Avg Score:0.0%
Providers:2