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 3.2 90B Instruct

Meta

Llama 3.2 90B Instruct is a multimodal language model developed by Meta. It achieves strong performance with an average score of 71.3% across 13 benchmarks. It excels particularly in AI2D (92.3%), DocVQA (90.1%), MGSM (86.9%). It supports a 256K token context window for handling large documents. The model is available through 5 API providers. 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 2024, it represents Meta's latest advancement in AI technology.

Meta

Llama 3.2 90B Instruct

Meta

2024-09-25

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Google

2025-05-20

7 months newer

Performance Metrics

Context window and performance specifications

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Max Context:-
Parameters:1.9B
Meta

Llama 3.2 90B Instruct

Larger context
Max Context:256.0K
Parameters:90.0B

Average performance across 3 common benchmarks

Google

Gemma 3n E2B Instructed LiteRT (Preview)

Average Score:46.0%
Meta

Llama 3.2 90B Instruct

+27.2%
Average Score:73.2%

Performance comparison across key benchmark categories

Google

Gemma 3n E2B Instructed LiteRT (Preview)

general
42.2%
math
40.4%
Meta

Llama 3.2 90B Instruct

general
+31.3%
73.5%
math
+30.3%
70.7%
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 3.2 90B Instruct

5 providers

Together

Throughput: 57 tok/s
Latency: 0.5ms

Hyperbolic

Throughput: 42 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 24 tok/s
Latency: 0.5ms

Fireworks

Throughput: 50 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms
Google

Gemma 3n E2B Instructed LiteRT (Preview)

Avg Score:46.0%
Providers:0
Meta

Llama 3.2 90B Instruct

+27.2%
Avg Score:73.2%
Providers:5