Model Comparison

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

Google

Gemma 3n E2B

Google

Gemma 3n E2B is a multimodal language model developed by Google. The model shows competitive results across 11 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. Released in 2025, it represents Google's latest advancement in AI technology.

OpenAI

GPT-4.1 nano

OpenAI

GPT-4.1 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 25 benchmarks. It excels particularly in MMLU (80.1%), IFEval (74.5%), CharXiv-D (73.9%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. 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 OpenAI's latest advancement in AI technology.

OpenAI

GPT-4.1 nano

OpenAI

2025-04-14

Google

Gemma 3n E2B

Google

2025-06-26

2 months newer

Performance Metrics

Context window and performance specifications

Google

Gemma 3n E2B

Max Context:-
Parameters:8.0B
OpenAI

GPT-4.1 nano

Larger context
Max Context:1.1M

Performance comparison across key benchmark categories

Google

Gemma 3n E2B

general
+21.7%
54.1%
OpenAI

GPT-4.1 nano

general
32.4%
Knowledge Cutoff
Training data recency comparison

GPT-4.1 nano

2024-05-31

Gemma 3n E2B

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

0 providers
OpenAI

GPT-4.1 nano

1 providers

OpenAI

Throughput: 200 tok/s
Latency: 2ms
Google

Gemma 3n E2B

Avg Score:0.0%
Providers:0
OpenAI

GPT-4.1 nano

Avg Score:0.0%
Providers:1