Model Comparison

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

Google

Gemma 3 4B

Google

Gemma 3 4B is a multimodal language model developed by Google. The model shows competitive results across 26 benchmarks. It excels particularly in IFEval (90.2%), GSM8k (89.2%), DocVQA (75.8%). It supports a 262K token context window for handling large documents. The model is available through 1 API provider. 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 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.

Google

Gemma 3 4B

Google

2025-03-12

Meta

Llama 4 Maverick

Meta

2025-04-05

24 days newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemma 3 4B

$0.71 cheaper
Input:$0.02
Output:$0.04
Meta

Llama 4 Maverick

Input:$0.17
Output:$0.60

Performance Metrics

Context window and performance specifications

Google

Gemma 3 4B

Max Context:262.1K
Parameters:4.0B
Meta

Llama 4 Maverick

Larger context
Max Context:2.0M
Parameters:400.0B

Average performance across 7 common benchmarks

Google

Gemma 3 4B

Average Score:52.9%
Meta

Llama 4 Maverick

+20.9%
Average Score:73.8%

Performance comparison across key benchmark categories

Google

Gemma 3 4B

vision
59.0%
math
64.5%
general
40.7%
code
+1.0%
61.5%
Meta

Llama 4 Maverick

vision
+16.8%
75.8%
math
+11.3%
75.7%
general
+30.8%
71.5%
code
60.5%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemma 3 4B

2024-08-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemma 3 4B

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
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 3 4B

Avg Score:52.9%
Providers:1
Meta

Llama 4 Maverick

+20.9%
Avg Score:73.8%
Providers:7