Model Comparison

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

Google

Gemma 3n E4B Instructed

Google

Gemma 3n E4B Instructed is a multimodal language model developed by Google. The model shows competitive results across 18 benchmarks. Notable strengths include HumanEval (75.0%), MGSM (67.0%), MMLU (64.9%). The model is available through 1 API provider. 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.

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 E4B Instructed

Google

2025-06-26

2 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemma 3n E4B Instructed

Input:$20.00
Output:$40.00
Meta

Llama 4 Scout

$59.62 cheaper
Input:$0.08
Output:$0.30

Performance Metrics

Context window and performance specifications

Google

Gemma 3n E4B Instructed

Max Context:64.0K
Parameters:8.0B
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 6 common benchmarks

Google

Gemma 3n E4B Instructed

Average Score:47.2%
Meta

Llama 4 Scout

+19.9%
Average Score:67.1%

Performance comparison across key benchmark categories

Google

Gemma 3n E4B Instructed

math
52.4%
general
41.6%
code
38.9%
Meta

Llama 4 Scout

math
+18.2%
70.5%
general
+24.7%
66.3%
code
+11.4%
50.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemma 3n E4B Instructed

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 E4B Instructed

1 providers

Together

Throughput: 42.09 tok/s
Latency: 0.43ms
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 E4B Instructed

Avg Score:47.2%
Providers:1
Meta

Llama 4 Scout

+19.9%
Avg Score:67.1%
Providers:6