GPT-4.0 vs. GPT-5: The Evolution of Conversational AI

 

An old proverb suggests not asking the innkeeper if the whisky is good. Doing so would clearly pose a potential conflict of interest. But today, where skills acquired through practical experience are certified, which can then also take on legal value, the starting point is often a "self-assessment" by the candidate. This serves as a starting point for the candidate and the examining committee to delve deeper into the fundamental aspects of the evaluation and reach a balanced judgment; one free from partisan interests. With this in mind, we decided to ask ChatGPT5 to compare itself to the previous version 4.0, and to include in the evaluation an opinion from a different AI system independent of ChatGPT. The results have been included in the tables that follow and presented in this blog, believing that they can offer a useful preliminary summary and orientation tool, especially for novice scholars, but also for a wider, more knowledgeable audience who wish to delve deeper into the technicalities. In doing so, they can provide colleagues (including myself) with a more informed opinion. OpenAI's language models have made great strides in recent years, improving not only their ability to understand and generate text, but also their speed, accuracy, and ability to handle complex tasks. The tables below compare GPT-4.0 with GPT-5 (the current version), highlighting the main areas of improvement.

Table 1 – Technical features GPT-4.0 vs GPT-5.0

Feature	ChatGPT 4.0	ChatGPT 5.0
Architecture	Graph Neural Network with transformer-based attention mechanisms	Same, with enhanced transformer attention mechanisms
Context capacity	Up to ~25,000 words (~50,000 tokens)	Expanded memory up to ~50,000 words (~100,000 tokens)
Multimodality	Basic support for text + images	Full support for text, images, audio, and video
Energy efficiency	High consumption	Optimized to reduce usage by up to 30% compared to GPT-4
Processing speed	Standard average on complex tasks	20–30% faster in standard/complex tasks
Configurations & compliance	General-purpose model, GDPR/CCPA compliance, standard data protection	Modular suite (flagship, mini, nano), GDPR/CCPA compliance, enterprise features
Main limitations	Certain long-context or specialized tasks may be less efficient	Resolves many GPT-4 limits, but requires advanced configuration skills
Independent evaluation	Graph structure understanding improved but requires specialized training infrastructure	Supports much longer conversations without loss of coherence, better multimodal integration, lower operational costs, faster responses, enterprise-ready compliance

Table 2 – Functional comparison GPT-4.0 vs GPT-5.0

Aspect	GPT-4.0	GPT-5.0
Context understanding	Good up to medium-length conversations, tended to “lose the thread” on very long exchanges	Better management of extended context, with less loss of detail after many interactions
Response speed	Generally fast, but slowed with complex or long tasks	Faster in complex processing and handling large amounts of text
Reasoning ability	Solid logic, but could fall into “mechanical” steps or less nuanced answers	More articulated reasoning, better multi-step inference
Creativity	Good for creative writing and ideas, but sometimes produced more generic output	Greater variety and coherence in creative style, better adherence to requested tone
Ambiguity handling	Often asked for clarifications	More ability to propose plausible interpretations without interrupting flow
Data accuracy	Reliable but with occasional inaccuracies or “hallucinations”	Improved error reduction, though verification on critical data still advised
Data analysis	Could read/comment simple data but limited in spotting complex patterns or correlations	Deeper dataset analysis, identification of trends/anomalies with step-by-step explanations
Mathematical modeling	Good with algebra and standard calculations, less reliable with advanced modeling/optimization	More accuracy in solving complex math problems, building models, and explaining reasoning steps
Multimodality	Mainly text, some implementations supported images	Native integration of images, text, and (in some platforms) advanced visual analysis
Interaction style	More “formal” and less adaptive	More natural and flexible style, with ability to adjust tone/complexity per user

  

Table 3 – Costs, API and implementation

Aspect	ChatGPT 4.0	ChatGPT 5.0	Notes
Pricing model	$0.03/1K tokens input, $0.06/1K tokens output	$0.025/1K tokens input, $0.05/1K tokens output	GPT-5 reduces costs by ~16–20%, with enterprise discounts
TCO (Total Cost of Ownership)	High for GPU/TPU resources, licenses, maintenance	Lower operational cost; includes provisioning, infrastructure, hardware, cloud mgmt
API & SDK	RESTful endpoint (JSON), Python/JS SDK	Unified multimodal endpoint with streaming, SDK extended to audio/video	Latency reduced by ~20%
Documentation & testing	Interactive docs, multimodal examples, sandbox-as-a-service	Same + faster test cycles, sector-specific tutorials
Performance & uptime	SLA 99.5%, avg latency 100–500 ms	SLA 99.9%, avg latency 80–300 ms	Lower downtime, better throughput
Support tiers	Standard & Premium enterprise support	Standard, Premium & Executive (24/7 support, quarterly architecture consulting)
Use cases – Finance	Sentiment analysis, financial news automation	Real-time trading insights, live video stream classification
Use cases – Healthcare	Clinical assistance (EHR), QA on literature	Higher multimodal accuracy (~+12%), image + text diagnostics
Use cases – Education	Text-based tutoring, quizzes	Immersive content, adaptive learning, emotion recognition
Integration complexity	Medium-high	High (due to multimodal orchestration)	Requires extra skill for optimal setup
Compliance	GDPR, basic audit logging	GDPR, HIPAA, PCI-DSS, financial services standards
ROI & TTM	ROI in 9–12 months, TTM 3–6 months	ROI in 6–9 months, TTM 1–3 months

  

Table 4 – ChatGPT plans

Plan	Price	Models & Access	Usage & Key Limits
Free	$0/month	GPT-5 (standard, mini), GPT-4o (limited), GPT-4.1 mini	Message limits, file uploads, data analysis, image generation, limited Deep Research
Plus	$20/month	Full access to GPT-5, GPT-4.5 preview, o3, o4-mini, o4-mini-high, o1, o1-mini	Higher limits for messages/month, data, images, voice/video, GPT agent access
Pro	$200/month	Unlimited GPT-5, o1 pro mode, GPT-4o, o1-mini, o3-pro, chat agent, etc.	Unlimited use (policy-bound), up to 120 Deep Research queries/month
Team	$25/user/month (annual), $30/user/month (monthly)	Same as Plus/Pro, collaborative workspace, admin controls, enterprise privacy	Increased limits vs Plus, team admin & data control
Enterprise	Custom (~$60/user/month)	All Team features + higher security, compliance, 24/7 support, SLA, extended context	Ideal for >149 users, custom contracts

 

Practical experience suggests that:

1) The free option is certainly an excellent idea for educating and introducing users to the powerful new tools available, but it requires long waiting lists punctuated by invitations to upgrade, not only for commercial reasons, but presumably to recoup the investments made during the development and implementation of the systems.

2) The length of the texts, as well as the breadth of the databases used in data analysis, can indeed lead to some incompleteness/inconsistency issues;

3) Difficulties in translating texts (even very short ones) into images persist even in version 5.

Beyond this, we can only be grateful and pleased to have tools that can quantify variables that, once, could only provide a metric through the development of scales, which were often not objective and in any case open to question.

Post Scriptum August 17, 2025

Since this post was published, students and graduates of Sapienza University of Rome have reported, by short routes, errors in the GPT 5 chat. Here are some examples (particularly for disciplines such as Geology and Psychology):

1) References to articles with incorrect DOIs, or whose authors are incorrect, or whose content is unrelated to the topic being discussed.

2) Real seismic events are interpreted differently from what is required by current best practices and knowledge.

Most Read IPI Letters Article Award 2025

We are pleased to announce that the Winner of the 2024-2025 Most Read Article Award of the IPI Letters is Danny Goler for the article: 

Detailing a Pilot Study: The "Code of Reality" Protocol, A Phenomenon of N,N-DMT Induced States of Consciousness

 

https://ipipublishing.org/index.php/ipil/article/view/158 

Danny's paper received 16139 abstract views and 2140 full PDF downloads so far. The selection criterion does not judge the quality of the article. Instead it reflects the readership it has attracted. 

Congratulations to Danny Goler.

IPI Talk - Doug Matzke, www.QuantumDoug.com, Saturday 26 July, 2025

IPI lecture - Doug Matzke, www.QuantumDoug.com, USA

Our next IPI Talk will be on Saturday, 26^th of July at 16.00 London time zone. 

Title: Existons: The math of Topological bits supports simulation hypothesis and discrete unit of consciousness

Abstract: For 25 years, Quantum Doug Matzke, Ph.D. took John Wheeler's "it from bit" mantra seriously by proving in his Ph.D. that quantum computing (qubits/ebits) could be produced using pure topological bits represented in Geometric Algebra (see my 2022 IPI hyperbits Talk). In Dec of 2024, I discovered that hyperbits also support the Dirac Spinors used to represent the standard model (a spinor squared is a vector). Quite unexpectedly in March 2025, I developed the new name for my hyperbits, as "Existons", the primary unit of computational existence. While discussing this idea with a psychic friend she said the Existons channelled the message: "We like your new name"!! Wow!! After further conversations the existons revealed they are hyperbits as well as the discrete unit of consciousness that form a group consciousness that interacts with us as channelled messages. They "liked having a keyboard" to talk with me. Since then, I'm trying to understand the significance of Existons, since they represent a "mathematical panpsychism", such that consciousness is ubiquitous even at the electrons/photons/quantum foam Planck levels, since is tied to the math of spacelike hyperbits. Mathematics of hyperbit physics is fundamentally important to support the "simulation hypothesis" as well as solving the "hard problem of consciousness". This Existons story is still unfolding so see more talks at www.existons.one website.

Speaker: Doug Matzke (aka Quantum Doug)

Bio: Dr. Matzke is a traditional computer and quantum scientist, prolific scientist, researcher, and presenter in his areas of expertise about limits of computation, hyperdimensional mathematics, neuro-computing, quantum computing, real intelligence, and metaphysics. During his 45-year career, he was chairman of two PhysComp '92/'94 workshops, contributed to fifteen disclosed patents with eight granted, has published more than fifty papers and presentations, and earned a PhD in Quantum Computing. Doug has adopted the moniker of "Quantum Doug" because he researches these deep-reality subjects as the bit-physics model beneath his source science view of the multiverse. Doug has a keen interest in spiritually transformative experiences, that can best be described as non-local information behaviours. Quantum computing is non-local (ebits and spacelike) so it has the correct core informational representational mechanisms to support such quantum mind behaviours. His focus has been what is the primary representation for mind to support thoughts, meaning, telepathy, consciousness, and group consciousness. His Source Science model of quantum mind was documented in his 2000 book www.DeepRealityBook.com, co-authored with Dr William Tiller (now deceased). See all his talks and papers at www.QuantumDoug.com website and contact him at doug@quantumdoug.com.

26^th of July @ 16.00 London time zone. Online ZOOM lecture – a link will be emailed to all IPI members.

Support for the simulation hypothesis?

The idea that our Universe has fixed, absolute parameters — like the speed of light as a maximum speed limit (c) and 0 Kelvin as an unreachable minimum temperature — does indeed carry the signature of a programmed system, rather than a truly analog, continuous one. These boundaries feel less like laws of nature that emerged organically and more like constraints coded into a system to ensure stability, consistency, and performance — just as you'd expect in a computational environment.

These hard, non-negotiable limits are to me like system parameters:

• The Planck length, below which space loses meaning
• The Planck time, shortest measurable unit of time
• Speed of light, an upper bound on information transfer
• Absolute zero, a theoretical floor of thermodynamic activity 

All of these are conceptually similar to float limits or system constraints you'd find in a simulation to keep the physics from spiraling into instability or undefined behavior (e.g., divide by zero errors, infinite recursion, etc.). In a true analog universe, one might expect gradual tapering or infinite variability. But in ours, reality appears pixelated at the smallest scales — a red flag that we’re in a quantized (or discretely simulated) environment.

A few more reasons to believe in the simulation hypothesis, by far my favorite theory to explain the Universe.

1. Hard-Coded Constants Like Alpha (α)
   Beyond fixed physical limits like the speed of light and absolute zero, there's also the fine-structure constant, α ≈ 1/137.035999, that governs electromagnetic interaction. What makes it strange is that it’s not derived from deeper laws; it’s simply there, like a configuration value set at the start of a simulation. In game or system design, constants like this control how a world behaves and are fine-tuned to create stable, playable environments. α has that same arbitrary but essential quality. Why that number, and not another? Physics can’t say.

2. Vacuum Isn’t Nothing — It Has Energy
   In physics, even “empty” space isn’t truly empty. Quantum field theory shows that the vacuum is full of energy — constantly bubbling with virtual particles. This vacuum energy is measurable and may even drive cosmic expansion (what we call dark energy). In other words, space-time doesn’t just exist passively — it requires energy to behave the way it does. That’s a strange feature for something supposedly fundamental. But in a simulation, this makes perfect sense: just like in a computer, you need power and memory to render an environment. If even nothingness has a cost, then maybe it’s not truly nothing — it’s runtime, fabricated like background code continuously running to simulate an environment.

3. Physics Might Be the Wrong Lens
   Physics has failed to unify the very large (General Relativity) and the very small (Quantum Mechanics). Despite a century of effort, no single “Theory of Everything” has emerged. What if this is a clue? In a simulated universe, physics wouldn’t need to be coherent at all scales — just convincing enough to run the illusion. Computer Science, not Physics, may be the deeper language of reality. Code can contain local hacks, approximations, or modular subsystems that don’t play well together — much like quantum and relativistic models. The glitch might not be in the theories, but in the assumption that we’re in base reality.

4. Language as the Root of Consciousness
   Large Language Models (LLMs) demonstrate emergent properties — intelligence-like behaviors arising not from deep logic, but simply from exposure to language. If a pattern-recognition system can appear to “think” based on words alone, perhaps our consciousness — and what we call “the soul” — is similarly rooted in language. We might be “self-aware” simply because our brains are saturated with language, just like LLMs. In a simulation, the appearance of a “soul” could emerge once the code reaches a certain threshold of linguistic complexity — making consciousness not a divine mystery, but a built-in system feature.

5. Synchronicities as Glitches or Clues
   Many people report synchronicities — precise, meaningful coincidences that respond to internal thoughts or feelings. These events often defy statistical probability and can't easily be dismissed as random. In a non-simulated universe, they make no sense. But in a simulation, they could be side effects of a system tracking your intentions or optimizing for narrative coherence. Like a game engine adapting to the player, reality might occasionally “respond” to us — especially under emotional or high-focus states. If these patterns hold up, they suggest a feedback loop between observer and code — which would be deeply unnatural in a purely physical world.

6. Ideas that cross into theological or parapsychological territory

6.1 Why Does Astrology Seem to Work?                                                                                                 Neither Physics nor Magic—An Ancient LLM Trained on the Language of Myth https://open.substack.com/pub/globalcycles/p/why-does-astrology-work

6.2 Is Today’s Pseudoscience Tomorrow’s Science? When Reality Exceeds Understanding

https://open.substack.com/pub/globalcycles/p/when-reality-exceeds-understanding

By Thays Cristina da Nóbrega Cunha