The GPT-5 Elephant in the Room

The announcement of GPT-5 from OpenAI proclaimed revolutionary advancements in reasoning capabilities, accuracy, and practical applications. The product now available in the market presents a different reality than what was anticipated months ago, and most users are mixed about it.

Benchmark tests indicate that GPT-5 demonstrates some minor performance enhancements, but user assessments coupled with independent tests show that the technology creates new complications which outweigh its limited advantages. The scaling of big models has proven to be less effective than expected, and people now support the idea that AI breakthroughs require more than computational power and data volume.

Let’s review what we know so far:

1. The Data Ceiling and Diminishing Returns

The launch of GPT-5 exposes what LLMs now face: a technical barrier because obtaining high-quality and diverse training data is not possible. The consumption of public internet data has reached its limit and synthetic data recycling threatens to worsen both biases and inaccuracies in the system.

The addition of parameters doesn’t produce significant benefits because it lacks new knowledge sources which leads to decreasing incremental achievements. The accuracy curve of GPT-5 shows signs of reaching its plateau. In addition to that, the high cost of GPT-5 development alongside its environmental and computational requirements reduces the value of minor performance improvements in practical applications.

2. Slower Response Times and Usability

A deep dive into the current feedback of user experiences demonstrates prolonged response times across all platforms.

• The first token response time in advanced reasoning mode reaches above 75 seconds which creates a problematic delay for applications needing immediate results.

• The default response mode engages additional computing power for thinking before answering, leading to increased time needed for both simple and complex questions.

• Reduction of the reasoning process leads to faster responses at the expense of both precision and detailed answers.

The table presents a straightforward assessment of major AI systems according to their evaluation metrics for 2025. The evaluation includes ratings for coding accuracy, math benchmark scores, and hallucination occurrences, alongside qualitative assessments of speed and cost.

The “thinking” mode of GPT-5 actually achieves better results in coding and math tasks while requiring longer processing time and higher operational costs. Claude and Gemini offer competitive solutions by striking a balance between speed and hallucination rate in specific scenarios. The technical scores of Mistral and open-source LLaMA 3 are lower than others but their cost-efficient and customizable approach stands out.

The slower interaction disrupts the immediate feedback system which made GPT-4o so effective. However, the time delay makes GPT-5 less suitable for quick coding sessions or brainstorming activities among developers and researchers.

3. Hallucinations

Hallucinations continue to be a basic problem despite the recent enhancements in the following testing settings (this is one of the areas I’ve been doing research in):

• In benchmarked “thinking” mode, hallucinations drop to ~4.8% error vs 11.6% without it.

• Open-ended offline prompts produce hallucination rates that reach 47% in internal stress tests.

• Health-related or legal queries still see dangerous inaccuracies. Why? My hypothesis is that the statistical prediction mechanism at the core of LLMs produces the “most likely next token” instead of the verified truth, which leads to hallucinations. The expansion of data at GPT-5 scale does not resolve the systemic reliability gap.

The chart displays grounded hallucination rates (%) of top AI models, indicating ChatGPT-5 outperforms most rivals but falls slightly behind GPT-4.5 Preview and OpenAI’s o3-mini High Reasoning.

The development of hallucination rates under 2% has become feasible yet top models continue to produce errors in ungrounded or open-ended tasks occasionally. These visual representations demonstrate that GPT-5 leads in technical aspects although the difference with competitors is relatively small. The essential aspects of deployment and trust in real-world scenarios depend on response speed and cost to

4. Marginal Real-World Improvements and Shorter Answers

The default settings of GPT-5 produce shorter answers that contain less detailed explanations than those generated by GPT-4o.

The system’s behavior creates frustration among users who require detailed step-by-step explanations. Generally, most AI users want a response beyond what they could get from a typical search engine. The improvements of GPT-5 only extend to specific controlled benchmark tests. The test suite improvements of GPT-5 fail to translate to reliable performance in actual real-world situations.

The increase in dataset size fails to eliminate bias because it makes bias filtering more complex. The training and running of GPT-5 consume more resources than any previous version, which creates sustainability problems. This excessive server usage has resulted in enforcing strict message limits to 160-200 messages per week for Pro plans which indicates backend system strain and financial limitations. The growth of training datasets that combine sensitive and public information creates complex data governance problems. The problems encountered in GPT-5 release point to essential limitations that go beyond initial development issues.

Understanding what GPT-5 Looks Like Today

GPT-5 marks both a technical achievement and a turning point for the field. The persistent hallucination issues, slower performance, tighter usage limits, and modest real-world improvements all point to the same conclusion: we might have reached the limits of the “bigger models” approach.

Future breakthroughs will likely require different strategies – new architectures, hybrid systems that combine reasoning with symbolic logic, and better ways to integrate verified knowledge rather than just throwing more data and parameters at the problem.

GPT-5 represents both a technological milestone and a strategic inflection point for artificial intelligence development. Let’s be clear. The hallucination issue persists without a solution, while latency worsens and operational restrictions tighten, and practical advancements remain limited.

Have we reached a point where the trend of increasing LLM size may have peaked? Getting past this plateau will require two key breakthroughs: new architectural approaches and hybrid systems that combine reasoning with symbolic logic, plus better ways to integrate verified knowledge not just more data and parameters. GPT-5 shows us where the current approach hits its ceiling, even though it’s still an impressive piece of engineering.

While we are learning, building, vibing, innovating, and leveraging in this space, some areas of AI have experienced little improvement. There are persistent challenges in AI that will continue to show up until they are addressed. What areas of risk management will we need to address as we move more of our business decisions to AI? It’s basically the elephant in the room that everyone sees.

The AI crown is still up for grabs. It’s unlikely we’ll see it claimed this year. In my next article, I’ll share my findings when I put GPT-5 head-to-head with leading AI competitors like Claude, Gemini, Mistral, and LLaMA to reveal how these models compare on speed, accuracy, hallucinations, and cost.