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

Gemma 3n E4B Instructed LiteRT Preview
Gemma 3n E4B Instructed LiteRT Preview is a multimodal language model developed by Google. The model shows competitive results across 28 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. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Google's latest advancement in AI technology.

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.

Gemma 3n E4B Instructed LiteRT Preview
2025-05-20

GPT-5 nano
OpenAI
2025-08-07
2 months newer
Performance Metrics
Context window and performance specifications

Gemma 3n E4B Instructed LiteRT Preview

GPT-5 nano
Average performance across 2 common benchmarks

Gemma 3n E4B Instructed LiteRT Preview

GPT-5 nano
Performance comparison across key benchmark categories

Gemma 3n E4B Instructed LiteRT Preview

GPT-5 nano
GPT-5 nano
2024-05-30
Gemma 3n E4B Instructed LiteRT Preview
2024-06-01
Provider Availability & Performance
Available providers and their performance metrics

Gemma 3n E4B Instructed LiteRT Preview

GPT-5 nano
ZeroEval
OpenAI

Gemma 3n E4B Instructed LiteRT Preview

GPT-5 nano