Model Comparison

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

Google

Gemma 2 27B

Google

Gemma 2 27B is a language model developed by Google. It achieves strong performance with an average score of 69.1% across 16 benchmarks. It excels particularly in ARC-E (88.6%), HellaSwag (86.4%), BoolQ (84.8%). The model shows particular specialization in reasoning tasks with an average performance of 82.5%. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Google's latest advancement in AI technology.

OpenAI

GPT-5 nano

OpenAI

GPT-5 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 5 benchmarks. It excels particularly in AIME 2025 (85.2%), HMMT 2025 (75.6%), GPQA (71.2%). It supports a 528K token context window for handling large documents. The model is available through 2 API providers. 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.

Google

Gemma 2 27B

Google

2024-06-27

OpenAI

GPT-5 nano

OpenAI

2025-08-07

1 year newer

Performance Metrics

Context window and performance specifications

Google

Gemma 2 27B

Max Context:-
Parameters:27.2B
OpenAI

GPT-5 nano

Larger context
Max Context:528.0K

Performance comparison across key benchmark categories

Google

Gemma 2 27B

general
+9.8%
70.0%
math
+48.5%
58.1%
OpenAI

GPT-5 nano

general
60.2%
math
9.6%
Knowledge Cutoff
Training data recency comparison

GPT-5 nano

2024-05-30

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemma 2 27B

0 providers
OpenAI

GPT-5 nano

2 providers

ZeroEval

Throughput: 500 tok/s
Latency: 0.3ms

OpenAI

Throughput: 500 tok/s
Latency: 0.3ms
Google

Gemma 2 27B

Avg Score:0.0%
Providers:0
OpenAI

GPT-5 nano

Avg Score:0.0%
Providers:2