GPT-5 vs GPT-4o: Is OpenAI's Latest Model an Upgrade?

OpenAI’s recent unveiling of GPT-5 has sparked considerable debate across the technology landscape. While some laud its advanced capabilities, others point to perceived shortcomings, leading many to question if this new flagship model truly surpasses its highly acclaimed predecessor, GPT-4o. For many users, GPT-4o had become the indispensable large language model (LLM) for a wide range of tasks, from summarizing text and generating images to complex data analysis. With GPT-5 now positioned as its successor, a critical evaluation is warranted to determine if this upgrade represents a genuine evolutionary leap or a potentially premature release that could diminish ChatGPT’s broad appeal.

To understand the nuances of this transition, it’s essential to recap what each model brings to the table. GPT-4o, released in May 2024, was a groundbreaking multimodal LLM, signifying a major shift in how users interacted with ChatGPT. Nicknamed “omni” for its ability to seamlessly process text, images, and audio, it offered enhanced coding and visual analysis capabilities, alongside robust speech recognition and analysis. Its notable features included increased processing speed, reduced response latency, and the generation of remarkably natural and coherent responses, coupled with the ability to access external tools and provide real-time information.

A year later, in August 2025, OpenAI introduced GPT-5 as its most advanced model to date. This latest iteration expands on GPT-4o’s multimodal foundation by adding video processing capabilities. GPT-5 introduces novel “agentic capabilities,” allowing it to autonomously plan and execute complex tasks, and features a “unified system” that intelligently determines whether a query requires deep reasoning or more basic processing. Embracing a “learn-by-doing” approach, GPT-5 is designed to be more empathetic while exhibiting less agreeableness than previous models. It also boasts significantly enhanced coding and writing prowess.

A direct comparison of their technical specifications reveals GPT-5’s ambition. While GPT-4o offered a substantial context window of approximately 128,000 tokens for both ChatGPT and API usage, GPT-5 nearly doubles this, providing 256,000 tokens for ChatGPT and an impressive 400,000 tokens for its API, allowing it to process much larger volumes of information. GPT-5 also introduces a dual-mode reasoning system—switching between fast and deep reasoning—in contrast to GPT-4o’s single reasoning mode. Furthermore, OpenAI claims GPT-5 has the lowest hallucination rate yet, a significant improvement over GPT-4o’s already low rate. GPT-5 also introduces personalization features like personality presets and tone control, and integrates with a broader array of tools, including Gmail and Calendar, extending beyond GPT-4o’s more limited tool access. For enterprise applications, GPT-5 offers “safe completions,” providing bounded, useful answers, a feature absent in GPT-4o. Benchmark tests underscore GPT-5’s leaps in complex problem-solving: its SWE-bench verified accuracy stands at 74.9% compared to GPT-4o’s 30.8%; in the AIME 2025 mathematics test, GPT-5 achieved 94.6% (without tools) against GPT-4o’s 71%; and it significantly improved on VideoMMMU (81.1% vs. 58.8%) and HealthBench (46.2% vs. 31.6%). These metrics suggest GPT-5 is engineered for complex reasoning and enterprise workflows, while GPT-4o remains optimized for real-time interaction and creative tasks.

Putting both models to the test across various tasks reveals a nuanced picture of their performance. In content creation, GPT-5 proved superior for generating concise, expert-level summaries, merging points effectively to provide just enough context for a knowledgeable reader. GPT-4o, by contrast, provided a more detailed, step-by-step summary of all points discussed in the source material. For image generation, both models performed well. GPT-5 produced more vibrant images with popping colors, text, and icons, though it exhibited a minor error with an arrow connection. GPT-4o generated images with solid colors, making them less vibrant, but notably included well-integrated audio input and output sources.

When it came to coding, GPT-5 demonstrated a clear advantage. While it took some time to process the query for a word-counting website, its final output was impressive, delivering a fully functional webpage with a refined user interface and experience (UI/UX) and additional features. GPT-4o’s output, in comparison, felt basic and outdated, offering only the core word-counting functionality without stylistic refinements. In image analysis, GPT-5 efficiently analyzed a circuit diagram, correctly identifying its components, extracting values, and applying the proper logic to calculate output current and voltage. GPT-4o struggled significantly with this task, recognizing only the output waveform but failing to extract critical values needed for calculations.

Finally, in a reasoning challenge involving a Sudoku puzzle, GPT-5 initially struggled with image interpretation, requiring over three minutes and manual confirmation of multiple values. However, once assisted, it successfully processed and solved the puzzle correctly. GPT-4o, conversely, failed entirely, populating all missing values with zeros.

The battle between GPT-5 and GPT-4o does not yield a clear-cut winner, as performance varies significantly by task. GPT-5 demonstrably dominates in complex tasks like coding and advanced reasoning, where its enhanced capabilities shine. However, GPT-4o continues to hold its own in areas such as content creation and image generation/analysis. A notable difference also lies in their operational pace: GPT-4o generally delivers faster responses, whereas GPT-5 sometimes exhibits hesitation, presumably engaging in more thorough analysis before generating an output. While GPT-5 benefits from more recent training data and agentic optimizations, the question remains whether its improvements are truly groundbreaking enough to overshadow its beloved predecessor.

Ultimately, despite GPT-5’s incremental improvements since its launch, a strong sentiment persists among users for the return of GPT-4o. Many feel that GPT-5’s launch was perhaps rushed, leaving users to grapple with adapting to a model that, for many common tasks, only marginally surpasses its predecessor. The perceived difference, often described as “a tad better,” makes it difficult for users to fully abandon GPT-4o. This suggests that more rigorous testing and refinement might have been beneficial before GPT-5’s public release, leaving a lingering desire for the consistency and user-friendliness that GPT-4o represented.