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.

Llama 4 Scout
Meta
Llama 4 Scout is a multimodal language model developed by Meta. It achieves strong performance with an average score of 67.3% across 12 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (90.6%), ChartQA (88.8%). The model shows particular specialization in vision tasks with an average performance of 81.9%. With a 20.0M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 6 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Meta's latest advancement in AI technology.

Llama 4 Scout
Meta
2025-04-05

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)

Llama 4 Scout
Average performance across 6 common benchmarks

Gemma 3n E2B Instructed LiteRT (Preview)

Llama 4 Scout
Performance comparison across key benchmark categories

Gemma 3n E2B Instructed LiteRT (Preview)

Llama 4 Scout
Gemma 3n E2B Instructed LiteRT (Preview)
2024-06-01
Provider Availability & Performance
Available providers and their performance metrics

Gemma 3n E2B Instructed LiteRT (Preview)

Llama 4 Scout
Together
DeepInfra
Fireworks
Groq
Novita
Lambda

Gemma 3n E2B Instructed LiteRT (Preview)

Llama 4 Scout