Model Comparison

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

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Google

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.

Meta

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.

Meta

Llama 4 Scout

Meta

2025-04-05

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Google

2025-05-20

1 month newer

Performance Metrics

Context window and performance specifications

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Max Context:-
Parameters:1.9B
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 6 common benchmarks

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Average Score:41.4%
Meta

Llama 4 Scout

+25.7%
Average Score:67.1%

Performance comparison across key benchmark categories

Google

Gemma 3n E2B Instructed LiteRT (Preview)

math
40.4%
general
42.2%
code
33.2%
Meta

Llama 4 Scout

math
+30.1%
70.5%
general
+24.1%
66.3%
code
+17.1%
50.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemma 3n E2B Instructed LiteRT (Preview)

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 E2B Instructed LiteRT (Preview)

0 providers
Meta

Llama 4 Scout

6 providers

Together

Throughput: 106.9 tok/s
Latency: 0.54ms

DeepInfra

Throughput: 76.1 tok/s
Latency: 0.31ms

Fireworks

Throughput: 116.1 tok/s
Latency: 0.53ms

Groq

Throughput: 776.1 tok/s
Latency: 1.08ms

Novita

Throughput: 69.82 tok/s
Latency: 0.85ms

Lambda

Throughput: 139.7 tok/s
Latency: 0.43ms
Google

Gemma 3n E2B Instructed LiteRT (Preview)

Avg Score:41.4%
Providers:0
Meta

Llama 4 Scout

+25.7%
Avg Score:67.1%
Providers:6