Model Comparison

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

Gemma 3n E4B

Google

Gemma 3n E4B is a multimodal language model developed by Google. It achieves strong performance with an average score of 64.6% across 11 benchmarks. It excels particularly in ARC-E (81.6%), BoolQ (81.6%), PIQA (81.0%). As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.

Llama 3.2 3B Instruct

Meta

Llama 3.2 3B Instruct is a language model developed by Meta. The model shows competitive results across 15 benchmarks. It excels particularly in NIH/Multi-needle (84.7%), ARC-C (78.6%), GSM8k (77.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. Released in 2024, it represents Meta's latest advancement in AI technology.

Llama 3.2 3B Instruct

Performance Metrics

Context window and performance specifications

Gemma 3n E4B

Max Context:-

Parameters:8.0B

Llama 3.2 3B Instruct

Larger context

Max Context:256.0K

Parameters:3.2B

Average performance across 2 common benchmarks

Gemma 3n E4B

Average Score:70.1%

Llama 3.2 3B Instruct

+4.1%

Average Score:74.2%

Performance comparison across key benchmark categories

Gemma 3n E4B

reasoning

73.4%

general

+17.2%

59.6%

Llama 3.2 3B Instruct

reasoning

+0.8%

74.2%

general

42.5%

Benchmark Scores - Detailed View

Side-by-side comparison of all benchmark scores

Knowledge Cutoff

Training data recency comparison

Gemma 3n E4B

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Gemma 3n E4B

0 providers

Llama 3.2 3B Instruct

1 providers

DeepInfra

Throughput: 171.5 tok/s

Latency: 0.24ms

Gemma 3n E4B

Avg Score:70.1%

Providers:0

Llama 3.2 3B Instruct

+4.1%

Avg Score:74.2%

Providers:1