Model Comparison

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

Google

Gemma 3 12B

Google

Gemma 3 12B is a multimodal language model developed by Google. It achieves strong performance with an average score of 63.8% across 26 benchmarks. It excels particularly in GSM8k (94.4%), IFEval (88.9%), DocVQA (87.1%). 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.

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 3 12B

Google

2025-03-12

OpenAI

GPT-5 nano

OpenAI

2025-08-07

4 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemma 3 12B

$0.30 cheaper
Input:$0.05
Output:$0.10
OpenAI

GPT-5 nano

Input:$0.05
Output:$0.40

Performance Metrics

Context window and performance specifications

Google

Gemma 3 12B

Max Context:262.1K
Parameters:12.0B
OpenAI

GPT-5 nano

Larger context
Max Context:528.0K

Average performance across 1 common benchmarks

Google

Gemma 3 12B

Average Score:40.9%
OpenAI

GPT-5 nano

+30.3%
Average Score:71.2%

Performance comparison across key benchmark categories

Google

Gemma 3 12B

math
+64.3%
73.9%
general
53.2%
OpenAI

GPT-5 nano

math
9.6%
general
+7.0%
60.2%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
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 3 12B

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
OpenAI

GPT-5 nano

2 providers

ZeroEval

Throughput: 500 tok/s
Latency: 0.3ms

OpenAI

Throughput: 500 tok/s
Latency: 0.3ms
Google

Gemma 3 12B

Avg Score:40.9%
Providers:1
OpenAI

GPT-5 nano

+30.3%
Avg Score:71.2%
Providers:2