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

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.

Qwen2.5 VL 32B Instruct
Alibaba
Qwen2.5 VL 32B Instruct is a multimodal language model developed by Alibaba. It achieves strong performance with an average score of 63.6% across 28 benchmarks. It excels particularly in DocVQA (94.8%), Android Control Low_EM (93.3%), HumanEval (91.5%). The model shows particular specialization in code tasks with an average performance of 87.8%. 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 Alibaba's latest advancement in AI technology.

Qwen2.5 VL 32B Instruct
Alibaba
2025-02-28

GPT-4.1 nano
OpenAI
2025-04-14
1 month newer
Performance Metrics
Context window and performance specifications

GPT-4.1 nano

Qwen2.5 VL 32B Instruct
Average performance across 3 common benchmarks

GPT-4.1 nano

Qwen2.5 VL 32B Instruct
Performance comparison across key benchmark categories

GPT-4.1 nano

Qwen2.5 VL 32B Instruct
GPT-4.1 nano
2024-05-31
Provider Availability & Performance
Available providers and their performance metrics

GPT-4.1 nano
OpenAI

Qwen2.5 VL 32B Instruct

GPT-4.1 nano

Qwen2.5 VL 32B Instruct