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-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 E2B 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 E2B Instructed LiteRT (Preview)

GPT-5 nano
Average performance across 2 common benchmarks

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-5 nano
Performance comparison across key benchmark categories

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-5 nano
GPT-5 nano
2024-05-30
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-5 nano
ZeroEval
OpenAI

Gemma 3n E2B Instructed LiteRT (Preview)

GPT-5 nano