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 Maverick

Meta

Llama 4 Maverick is a multimodal language model developed by Meta. It achieves strong performance with an average score of 71.8% across 13 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (92.3%), ChartQA (90.0%). The model shows particular specialization in vision tasks with an average performance of 75.8%. With a 2.0M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 7 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 Maverick

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 Maverick

$59.23 cheaper
Input:$0.17
Output:$0.60

Performance Metrics

Context window and performance specifications

Google

Gemma 3n E4B Instructed

Max Context:64.0K
Parameters:8.0B
Meta

Llama 4 Maverick

Larger context
Max Context:2.0M
Parameters:400.0B

Average performance across 6 common benchmarks

Google

Gemma 3n E4B Instructed

Average Score:47.2%
Meta

Llama 4 Maverick

+27.7%
Average Score:74.8%

Performance comparison across key benchmark categories

Google

Gemma 3n E4B Instructed

math
52.4%
general
41.6%
code
38.9%
Meta

Llama 4 Maverick

math
+23.4%
75.7%
general
+29.9%
71.5%
code
+21.6%
60.5%
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 Maverick

7 providers

Sambanova

Throughput: 638.7 tok/s
Latency: 2.04ms

Together

Throughput: 97.93 tok/s
Latency: 0.2ms

DeepInfra

Throughput: 83.59 tok/s
Latency: 0.38ms

Fireworks

Throughput: 63.03 tok/s
Latency: 0.62ms

Groq

Throughput: 307.3 tok/s
Latency: 0.27ms

Novita

Throughput: 69.42 tok/s
Latency: 0.62ms

Lambda

Throughput: 93.69 tok/s
Latency: 0.65ms
Google

Gemma 3n E4B Instructed

Avg Score:47.2%
Providers:1
Meta

Llama 4 Maverick

+27.7%
Avg Score:74.8%
Providers:7