Model Comparison

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

Google

Gemini 2.5 Pro

Google

Gemini 2.5 Pro is a multimodal language model developed by Google. It achieves strong performance with an average score of 67.1% across 16 benchmarks. It excels particularly in MRCR (93.0%), AIME 2024 (92.0%), Global-MMLU-Lite (88.6%). The model shows particular specialization in vision tasks with an average performance of 82.2%. 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 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

Gemini 2.5 Pro

Google

2025-05-20

OpenAI

GPT-5 nano

OpenAI

2025-08-07

2 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Pro

Input:$1.25
Output:$10.00
OpenAI

GPT-5 nano

$10.80 cheaper
Input:$0.05
Output:$0.40

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Pro

Larger context
Max Context:1.1M
OpenAI

GPT-5 nano

Max Context:528.0K

Average performance across 3 common benchmarks

Google

Gemini 2.5 Pro

+6.2%
Average Score:61.3%
OpenAI

GPT-5 nano

Average Score:55.0%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Pro

general
+9.2%
69.4%
OpenAI

GPT-5 nano

general
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

Gemini 2.5 Pro

2025-01-31

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.5 Pro

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
OpenAI

GPT-5 nano

2 providers

ZeroEval

Throughput: 500 tok/s
Latency: 0.3ms

OpenAI

Throughput: 500 tok/s
Latency: 0.3ms
Google

Gemini 2.5 Pro

+6.2%
Avg Score:61.3%
Providers:1
OpenAI

GPT-5 nano

Avg Score:55.0%
Providers:2