GPT-5: A new level in artificial intelligence or a shift to a new paradigm?

CONVERSATIONAL AI

The statement made by OpenAI CEO Sam Altman resonated significantly within the tech world:

“I’m scared of what GPT-5 can do. After talking to it, I felt useless.”

Even this single remark has redrawn the boundaries of curiosity surrounding GPT-5. Developed with high expectations, this new model has already become somewhat of a legend in tech circles before its launch. For those closely following generative AI applications, GPT-5 may not just be a version update—it raises the question: could it signal a paradigm shift?

As CBOT, we lead the industry in generative AI, implementing large-scale generative AI-based solutions across various sectors. For us, it’s not just the written technical specifications that matter, but also the performance we witness in real-world use cases.

In this article, we will explore whether GPT-5 truly represents a new level—or marks the transition to a new paradigm in AI.

Enjoy the read.

GPT-5: The Retina Display of Artificial Intelligence?

With OpenAI’s introduction of GPT-5, we began speaking not just of a model update in generative AI, but of a fundamental shift in user expectations. As significant as the model’s capacity increase is, perhaps more important is its ability to deliver a radically new experience that could reshape usage habits.

GPT-5 promises meaningful advances in areas like multi-step reasoning, following long contexts, and generalizing across different tasks. Yet beyond the technical improvements, what’s striking is this: GPT-5 is redefining what users expect from AI. They no longer just seek correct answers—they want systems that understand more with fewer instructions, can establish broader context with minimal explanation. There is rising demand for a system capable of generating strategic insights from a short query, switching tasks fluidly, and building its own context.

Sam Altman’s analogy comparing GPT-5 to Apple’s Retina displays was an attempt to convey this experiential leap in popular terms. But what matters more from our perspective is how this experiential difference will impact internal processes. Because this new level of expectation doesn’t just set a new standard for individual users—it does the same for organizations integrating generative AI into operational workflows.

The Anatomy of GPT-5: Expectations, Realities, and User Experience

Innovations in Training and Model Architecture

GPT-5’s training was completed at the end of 2024. OpenAI announced that the model was trained on both publicly available and licensed datasets, once again using Reinforcement Learning with Human Feedback (RLHF) as a core methodology. Throughout the training process, user interactions and expert feedback were systematically collected and used to fine-tune the model’s behavior. However, no specific technical details about the architecture—such as the number of parameters or layer structure—have been disclosed.

OpenAI claims GPT-5 provides more consistent answers and delivers more accurate results in complex tasks with less prompting compared to previous versions. However, these advancements are not always perceived to the same extent by users. MIT Technology Review describes the model not as a “technological leap,” but as a more refined version of GPT-4. Altman’s “Retina display” metaphor encapsulates this view: not a radical change, but a noticeably smoother user experience.

With GPT-5, several new parameters and API-level enhancements were introduced, showcasing not just improved output, but also greater user control, resource optimization, and prompt flexibility:

  • Overrefusals: If a request contains an unsafe element, the model can now omit only that part and explain why, instead of rejecting the entire input.

  • Reasoning Effort: A new parameter allows users to specify how much “thinking time” the model should spend before generating a response.

  • Verbosity: Users can now directly control the length of responses.

  • Prompt Caching & Token Optimization: Optimization techniques reduce cost and latency for frequently repeated inputs.

  • Model Routing: Depending on the task type, requests can be automatically routed among different GPT-5 variants (gpt-5, gpt-5-mini, gpt-5-nano).

All of these advancements highlight that GPT-5 is not only more powerful—but also more flexible and controllable. This technical evolution is already being reflected in academia. At Stanford University, Kian Katanferi, an AI Adjunct Lecturer, has added GPT-5-specific modules to his Fall 2025 CS230: Deep Learning course. Students are exploring aspects such as steerability, optimization techniques, and task management through hands-on exercises.

Long-Context Capability

One of GPT-5’s most notable improvements is its expanded context window. The model can now process longer texts, dialogue histories, and documents—an important advantage for use cases ranging from document analysis to customer service.

However, according to user feedback, this contextual advantage is not always felt in practice. Comments on Reddit and X suggest the model occasionally produces off-topic answers and sometimes fails in multimodal tasks like visual analysis. While GPT-5 is better at “retaining” long contexts, its ability to actively and accurately use this information still varies depending on the task.

Multi-Step Reasoning

According to OpenAI, GPT-5 outperforms its predecessors in step-by-step problem-solving. This claim is backed by the new reasoning_effort API parameter, which allows users to adjust how much the model should “think” before responding. Higher reasoning_effort settings are designed to yield more consistent outputs, especially for complex problems.

However, an example in MIT Technology Review shows GPT-5 and GPT-4o producing similar results for an app design task. GPT-5 offered a more aesthetic output, while GPT-4o provided similar functionality with a simpler design. This suggests that the difference in multi-step reasoning lies more in polish and consistency than in raw problem-solving capacity.

Cross-Task Generalization

Another standout feature of GPT-5 is its ability to generalize to previously unseen tasks. This means the model has improved pattern recognition and task adaptation, even in scenarios not present in its training set. OpenAI describes this as a structure that enables “high accuracy with minimal prompting.”

Improvements in this area make it easier for GPT-5 to select an appropriate approach automatically for different types of tasks. For instance, in Stanford’s CS230 course taught by Kian Katanferi, one of the topics is model routing, which is directly related to this feature. The model can route tasks among GPT-5 variants based on task complexity, enabling a balance between performance, cost, and latency.

However, this generalization ability is not consistently successful in all contexts. Some users report that the model becomes indecisive or produces formally correct but factually wrong outputs in unfamiliar tasks. This shows that despite strong pattern recognition, a context-sensitive validation mechanism is still essential.

Steerability and Answer Accuracy

GPT-5 has significantly improved in terms of steerability compared to earlier versions. Users can now define answer length directly via the verbosity parameter without needing to specify it in the prompt.

The model’s ability to adapt tone, style, and behavior—its “steerability”—has also been enhanced. This makes it easier for businesses to shape output according to brand language or user segments.

However, this flexibility doesn’t always equate to consistent answers. Some users report that responses are overly interpretive, less direct, and occasionally “reluctant.” This indicates that although steerability has improved, GPT-5 still does not produce fully deterministic results.

Hallucination Rate: Reduced, But Not Eliminated

OpenAI states that GPT-5 has achieved a meaningful reduction in hallucination rates—i.e., the tendency to produce false or fabricated information. This improvement is attributed to the diversity of training data and behavioral fine-tuning using RLHF. Particularly in fact-based queries, higher accuracy rates are reported.

But this does not mean hallucinations have been eradicated. In scenarios involving recent events, niche topics, or technical content, the model still produces incorrect outputs. User feedback shows that while this isn’t unique to GPT-5, it remains a risk. AI educator Kian Katanferi highlights technical strategies to reduce hallucination—such as reasoning effort and prompt optimization—in his training content. This underscores that reliable output depends not just on the model itself, but also on conscious user input and control.

GPT-5 is presented as a model with improved long-context handling, better steerability, and enhanced cross-task generalization. Its development was shaped by user feedback and expert contributions. While OpenAI’s internal benchmarks and early user experiences show meaningful improvements in some areas, in others the model still falls short of expectations—particularly in software development, visual analysis, and multimodal applications.

User feedback indicates that GPT-5’s technical promises are context-dependent in practice. While reduced hallucinations and better performance in complex tasks are seen positively, response speed, output depth, and task-specific accuracy remain points of contention.

This leads us beyond a classic question into a deeper reflection. As Paul Hlivko puts it, the real question today is:

“Can we think intelligently about machines?”

We must focus not only on the model itself, but how it is integrated; not just on the parameter count, but on what problem it solves and how. As AI becomes more accessible, the significance of technical differences fades; the real value lies in how the technology is used—what architecture, in what process, to produce what outcomes.

OpenAI shares performance claims in its official GPT-5 documentation. Users, however, also highlight inconsistencies in real-world use. This gap compels us to think more deeply about strategy, process, and user experience design—before choosing the technology itself.

Thus, GPT-5 is a model whose true value emerges only in the right context. It won’t deliver the same results for every organization or solve every problem equally. But one thing is clear: the models are evolving, yet creating value still depends on how people and organizations think.