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

Gemma 3n E2B Instructed LiteRT (Preview)
Gemma 3n E2B Instructed LiteRT (Preview) is a multimodal language model developed by Google. The model shows competitive results across 28 benchmarks. Notable strengths include PIQA (78.9%), BoolQ (76.4%), ARC-E (75.8%). 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-4.1 nano
OpenAI
GPT-4.1 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 25 benchmarks. It excels particularly in MMLU (80.1%), IFEval (74.5%), CharXiv-D (73.9%). With a 1.1M 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 2025, it represents OpenAI's latest advancement in AI technology.

GPT-4.1 nano
OpenAI
2025-04-14

Gemma 3n E2B Instructed LiteRT (Preview)
2025-05-20
1 month newer
Performance Metrics
Context window and performance specifications

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-4.1 nano
Average performance across 2 common benchmarks

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-4.1 nano
Performance comparison across key benchmark categories

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-4.1 nano
GPT-4.1 nano
2024-05-31
Gemma 3n E2B Instructed LiteRT (Preview)
2024-06-01
Provider Availability & Performance
Available providers and their performance metrics

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-4.1 nano
OpenAI

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-4.1 nano