# Preliminary Explorations with GPT-4o(mni) Native Image Generation

arXiv: https://arxiv.org/abs/2505.05501 · HTML: https://mlsanity.com/paper/2505.05501/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2025.

## Built on by (most cited first, 5 of 5)

- [PosterHarness: Turning Scientific Poster Generation into an Auditable Instruction-Following Benchmark](https://mlsanity.com/paper/2607.03006/) (2026)
- [MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks](https://mlsanity.com/paper/2509.14638/) (2025)
- [Purify Once, Edit Freely: Breaking Image Protections under Model Mismatch](https://mlsanity.com/paper/2603.13028/) (2026)
- [MemCoT: Test-Time Scaling through Memory-Driven Chain-of-Thought](https://mlsanity.com/paper/2604.08216/) (2026)
- [Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education](https://mlsanity.com/paper/2605.31212/) (2026)

## Abstract (arXiv)

Recently, the visual generation ability by GPT-4o(mni) has been unlocked by OpenAI. It demonstrates a very remarkable generation capability with excellent multimodal condition understanding and varied task instructions. In this paper, we aim to explore the capabilities of GPT-4o across various tasks. Inspired by previous study, we constructed a task taxonomy along with a carefully curated set of test samples to conduct a comprehensive qualitative test. Benefiting from GPT-4o's powerful multimodal comprehension, its image-generation process demonstrates abilities surpassing those of traditional image-generation tasks. Thus, regarding the dimensions of model capabilities, we evaluate its performance across six task categories: traditional image generation tasks, discriminative tasks, knowledge-based generation, commonsense-based generation, spatially-aware image generation, and temporally-aware image generation. These tasks not only assess the quality and conditional alignment of the model's outputs but also probe deeper into GPT-4o's understanding of real-world concepts. Our results reveal that GPT-4o performs impressively well in general-purpose synthesis tasks, showing strong capabilities in text-to-image generation, visual stylization, and low-level image processing. However, significant limitations remain in its ability to perform precise spatial reasoning, instruction-grounded generation, and consistent temporal prediction. Furthermore, when faced with knowledge-intensive or domain-specific scenarios, such as scientific illustrations or mathematical plots, the model often exhibits hallucinations, factual errors, or structural inconsistencies. These findings suggest that while GPT-4o marks a substantial advancement in unified multimodal generation, there is still a long way to go before it can be reliably applied to professional or safety-critical domains.