GPT-4 vs GPT-3: What’s New in ChatGPT (2025)

ChatGPT has come a long way since its early days running on GPT-3. In 2025, the AI chatbot is smarter, faster, and more versatile than ever.

OpenAI’s latest models – GPT-4 (including newer variants like GPT-4 Turbo and GPT-4 Omni) – have introduced major improvements over the previous GPT-3 and GPT-3.5 generation.

These improvements span context length, multimodal inputs/outputs, accuracy, and overall performance, fundamentally enhancing the ChatGPT user experience.

Illustration: GPT-3 vs GPT-4 – Newer GPT-4-based ChatGPT models exhibit greater intelligence, multimodality, and context awareness than the GPT-3-based predecessors.

In this article, we’ll explain in clear terms what GPT-3, GPT-3.5, GPT-4, GPT-4 Turbo, and GPT-4 Omni are.

We’ll dive into their features and capabilities, compare their context limits and accuracy, discuss how GPT-4’s new multimodal powers and GPT-4 Turbo/Omni updates impact ChatGPT in 2025, and provide real-world examples of how GPT-4 improves over GPT-3.

A handy comparison table is included to summarize the key differences. By the end, you’ll understand “GPT-4 vs GPT-3” and what’s new in ChatGPT 2025 – and why it matters. Let’s get started!

GPT-3: The Original Language Titan (2020)

GPT-3 is the third-generation Generative Pre-trained Transformer model from OpenAI, released in 2020. This was the model that originally stunned the world with its ability to generate human-like text.

With 175 billion parameters, GPT-3 was a significant leap forward in language AI at the time. It can produce essays, answer questions, translate languages, and more – all from plain text prompts.

However, GPT-3 had its limitations. It was limited to text-only input/output (i.e. it could only read and write text). Its context window – the amount of text it could remember in one go – was only on the order of a few thousand tokens (about a couple pages of text).

That meant GPT-3 would lose track of longer conversations or documents. It also wasn’t specifically fine-tuned for dialogue, so using it in a chatbot required careful prompting. GPT-3 could sometimes produce incorrect facts or nonsensical answers if a prompt was complex.

In short, GPT-3 laid the groundwork as a powerful text generator, but it lacked the fine-tuning and extended memory that later versions would achieve.

It’s the foundation upon which ChatGPT was built, but by today’s standards GPT-3’s capabilities are relatively basic.

GPT-3.5: Bridging the Gap (ChatGPT’s Foundation)

GPT-3.5 refers to an intermediate generation between GPT-3 and GPT-4. Not a single model per se, “GPT-3.5” is a term often used for the improved models that OpenAI developed in 2021–2022, including the one that powers the original ChatGPT.

This version was fine-tuned for dialogue and instructions, making it far better at following user prompts than GPT-3. In fact, ChatGPT’s initial launch in late 2022 was based on a GPT-3.5 model (sometimes known as GPT-3.5 Turbo).

GPT-3.5 improved upon GPT-3 in accuracy and nuance of text generation. It was better at understanding context and producing more relevant, coherent responses.

The model could handle subtle prompts or conversational cues that often confused GPT-3. Additionally, GPT-3.5 came with a larger context window – roughly up to 4K tokens (around 8,000 words) in the early versions, and later up to 16K tokens in some 2023 updates.

This meant it could remember and process more conversation history or longer prompts than GPT-3 before.

Importantly, GPT-3.5 (especially the ChatGPT-tuned model) was optimized for speed and cost. It became the default model for the free ChatGPT service, offering users a taste of AI assistance at no cost. Its responses are fast and it’s very capable for everyday questions and tasks.

However, it still has notable differences from ChatGPT-4: it may miss the depth of understanding or complex reasoning that GPT-4 can handle, and it cannot process images or other modalities.

GPT-3.5 is essentially the “good enough” AI for many simple applications – and extremely budget-friendly – but it set the stage for the breakthroughs to come with GPT-4.

GPT-4: A New Era of Intelligence (2023)

GPT-4 is the fourth-generation model that marked a huge leap in capability when released in March 2023. This model introduced massive improvements in reasoning, context handling, and even the ability to accept images as input.

OpenAI describes GPT-4 as “10 times more advanced than its predecessor, GPT-3.5,” with a much larger (though undisclosed) number of parameters. In simple terms, GPT-4 is a far more powerful and sophisticated brain behind ChatGPT.

Key advancements of GPT-4:

Better Understanding & Accuracy: GPT-4 can handle much more nuanced instructions and complex tasks than GPT-3.5. For example, in OpenAI’s evaluations, GPT-4 passes a simulated bar exam in the top 10% of test-takers, whereas GPT-3.5 only scored around the bottom 10%. This highlights GPT-4’s dramatic improvement in reasoning and knowledge application. Overall, GPT-4 is more reliable, creative, and less likely to get confused by tricky prompts.
Larger Context Window: GPT-4 initially came with an 8,192-token context (about 4x GPT-3’s), with an optional 32K-token extended version. In practice, this means GPT-4 can remember and consider much longer conversations or documents. You can feed it longer articles or have extended dialogues without it forgetting the context as quickly. (For perspective, 8K tokens is roughly ~6,000 words.) This larger “memory” enables use cases like analyzing long reports or holding deep multi-turn conversations that GPT-3/3.5 would struggle with.
Multimodal Input (Vision): A headline feature of GPT-4 is that it’s multimodal – it can accept images as inputs in addition to text. While initially this feature was in limited beta, it demonstrated GPT-4’s ability to analyze visuals. For instance, GPT-4 can describe an image, explain a meme, or interpret a chart you upload. This was a first for GPT models. (At launch, the image understanding was basic and rolled out carefully, but it paved the way for the fully multimodal GPT-4 Omni later.)
Improved Safety: GPT-4 was trained with an emphasis on being more aligned and factual. OpenAI reported GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5. It also handles instructions more precisely. This made ChatGPT (when using GPT-4) a safer and more trustworthy assistant compared to the earlier model.

In everyday use, ChatGPT powered by GPT-4 is noticeably better for complex tasks – from writing a detailed essay or debugging code, to giving more accurate advice.

The trade-off is that GPT-4 is slower and more computationally expensive than GPT-3.5. Initially, ChatGPT Plus users experienced longer generation times and usage caps (e.g. a limit of messages per 3-4 hour window) due to GPT-4’s heavy compute needs.

OpenAI addressed some of this later with model optimizations (GPT-4 Turbo) and by charging a premium for GPT-4 access (ChatGPT Plus subscription or higher API pricing).

But there’s no doubt that GPT-4 set a new standard for what an AI chatbot can do, greatly narrowing the gap between AI and human-level performance on many tasks.

GPT-4 Turbo: Speed and Scale Upgrades (Late 2023)

As more users and developers adopted GPT-4, the demand for a faster and more cost-effective version grew. Enter GPT-4 Turbo, introduced in November 2023 at OpenAI’s DevDay.

GPT-4 Turbo is essentially an optimized version of GPT-4 that offers speed, scale, and affordability – without sacrificing much capability. Think of it as GPT-4’s racecar-tuned variant.

What’s special about GPT-4 Turbo:

Much Larger Context: GPT-4 Turbo supports a huge 128,000-token context window. Yes, 128k! That’s about the equivalent of reading 300 pages of text in a single prompt. This was a massive upgrade from GPT-4’s 8k/32k context. In practical terms, GPT-4 Turbo can ingest entire books or datasets at once, enabling new possibilities like lengthy document analysis or feeding in extensive conversation history. It vastly improves ChatGPT’s ability to “remember” earlier parts of a conversation or handle long-form content in one go.
Faster and Cheaper: As the name implies, GPT-4 Turbo is tuned for efficiency. OpenAI managed to improve performance and reduce costs. In fact, GPT-4 Turbo’s API pricing was announced at about 3× cheaper for inputs and 2× cheaper for outputs compared to original GPT-4. (For example, if GPT-4 was $0.03 per 1K tokens input, Turbo was around $0.01 for the same.) This cost reduction made using GPT-4-level AI more accessible for developers and allowed ChatGPT to potentially handle more queries with less expense. Additionally, latency (response time) is better – GPT-4 Turbo is generally quicker to respond than the original GPT-4.
Up-to-date Knowledge: GPT-4 Turbo came with an updated knowledge cutoff (training data through about April 2023, later extended to end of 2023). This meant it was aware of more recent events and information than GPT-4 which had a Sept 2021 cutoff. Users noticed that GPT-4 Turbo could answer questions about more recent happenings out-of-the-box (though anything beyond its training still required the browsing tool or plugins). This reduced the gap in knowledge recency.
Vision Integration: OpenAI also enabled vision capabilities in GPT-4 Turbo. In the DevDay update, GPT-4 Turbo was described as having enhanced multimodal abilities, including the ability to use the new DALL·E 3 for image generation and to handle image inputs better. Essentially, while GPT-4 Turbo itself outputs text, it could interface with image-generation and had improved image understanding compared to the original GPT-4. (ChatGPT, by late 2023, allowed Plus users to upload images and the model – likely GPT-4 Turbo behind the scenes – could analyze them.)

In summary, GPT-4 Turbo made ChatGPT faster and more scalable. For users, this meant less waiting and potentially higher message limits.

For developers, it meant being able to use GPT-4’s power in applications with far lower cost and the ability to process vastly more data.

The user experience on ChatGPT Plus improved with Turbo – long conversations became smoother, and advanced use cases (like analyzing a long PDF by pasting it in) became feasible.

While on very complex tasks the original GPT-4 might still have a slight edge in careful reasoning, GPT-4 Turbo is nearly as capable in most situations and the go-to choice for high throughput scenarios.

GPT-4 Omni (GPT-4o): The Multimodal Powerhouse (2024)

The evolution didn’t stop at Turbo. In May 2024, OpenAI unveiled GPT-4 Omni (often abbreviated as GPT-4o), heralding it as the new flagship model of the GPT series.

The “o” in GPT-4o stands for “Omni,” hinting at its all-in-one, multi-capability nature. GPT-4 Omni represents the next major leap, combining all the modalities (text, vision, and voice) into a single model and improving performance even further.

Highlights of GPT-4 Omni:

Truly Multimodal (Text, Image, Audio): GPT-4o can handle multiple types of input and output in one model. It processes text, images, and audio inputs, and can generate text or even speak responses with a human-like voice. This is a significant upgrade from GPT-4 which, while it had image input in beta, did not natively handle audio. GPT-4 Omni essentially combines modalities that previously might require separate systems. For example, you can speak a question to ChatGPT (voice input), perhaps include a photo in your prompt, and GPT-4o can understand both, then answer you with a spoken response. It’s a unified model for voice, vision, and language.
Natural, Real-Time Conversations: OpenAI demonstrated GPT-4 Omni’s ability to have fluid voice conversations. The model introduced a rapid audio processing capability – it can process spoken input and respond almost as fast as a human replies in conversation, with an average response time around 320 milliseconds for voice input. Moreover, GPT-4o’s text-to-speech for outputs can generate an AI voice that sounds impressively human. In practical terms, ChatGPT with GPT-4 Omni can function like a personal voice assistant, talking with you in real time. This made ChatGPT feel much more interactive and lifelike by 2025.
Full Integration of Vision: Unlike the “enhanced” but still partial image understanding in GPT-4/Turbo, GPT-4 Omni has advanced vision capabilities built-in. It can interpret complex images or even sequences of images (some sources even note video capability). It can answer detailed questions about pictures, perform visual reasoning (e.g. analyzing a graph or diagram you provide), and combine image context with text seamlessly. For instance, you could show it a graph and ask for insights, or upload a screenshot of an error and have it help troubleshoot – GPT-4o can handle it. This is full multimodal AI, where it “sees” and “hears” in addition to reading.
Memory and Context Improvements: GPT-4 Omni inherited the 128K token context window from Turbo, so it maintains that enormous capacity for long content. This means it can sustain very long sessions or analyze very large inputs. Additionally, OpenAI improved the model’s contextual accuracy – GPT-4o is even better at remembering context over long conversations. One report noted GPT-4o retains context with ~92% accuracy over 10 turns, compared to GPT-4’s 88% and GPT-3.5’s 83%. That translates to more coherent, on-topic answers deep into a chat. The model is also generally faster and more efficient, thanks to optimizations.
Higher Performance & Less Bias: GPT-4o made gains in various accuracy benchmarks. It slightly outperforms GPT-4 on many metrics – e.g., one set of metrics showed GPT-4o with ~89% accuracy on complex queries vs GPT-4’s 84%. It also has reduced bias in outputs compared to previous models, thanks to training on more diverse data and improved alignment techniques. OpenAI positioned Omni as not just adding modalities, but refining the intelligence. It’s essentially GPT-4 “version 2.0,” benefitting from all the lessons and fine-tuning since GPT-4’s original release.
Cost Efficiency: Despite being more capable, GPT-4 Omni became more cost-effective to operate. OpenAI managed to halve the costs relative to GPT-4 Turbo in the API. For example, if Turbo was $10 per 1M input tokens, Omni was about $5 per 1M; Turbo $30 per 1M output vs Omni $15 per 1M, according to OpenAI data. In other words, Omni is 50% cheaper and 2× faster than Turbo by mid-2024. Such efficiency gains meant ChatGPT could offer the top model to users with less concern of running up huge costs, and developers could integrate the best model more broadly. OpenAI even released a GPT-4o mini – a smaller version of Omni – in mid-2024 that runs faster and even more cheaply, for applications that need speed over maximum accuracy.

By late 2024 and into 2025, ChatGPT Plus uses GPT-4 Omni as its default model (replacing the earlier GPT-4).

This upgrade transformed the user experience: now you can talk to ChatGPT with voice, show it images, and get multimodal help in one place.

For example, you might snap a photo of a math problem and ask ChatGPT (via voice) to explain how to solve it – it can. GPT-4o in ChatGPT feels like a true AI assistant that sees, hears, and speaks, rather than just a text chatbot.

It’s worth noting that as of 2025, GPT-4 Omni is essentially the pinnacle of the GPT-4 series, and OpenAI has even started hinting at GPT-4.5 or GPT-5 in the future.

But for now, GPT-4o (Omni) represents the cutting-edge, powering the most advanced ChatGPT experience.

Comparison Table: GPT-3 vs GPT-3.5 vs GPT-4 vs GPT-4 Turbo vs GPT-4 Omni

To recap the differences between these models, the table below summarizes key comparisons:

Model	Release	Max Context	Multimodal Support	Capabilities & Usage	Pricing/Access
GPT-3	June 2020	~2K tokens (≈1,500 words)	Text only	175B parameters; strong text generation, but limited memory and no fine-tuned chat. Good at basic Q&A, text completion.	API access (paid); basis of early AI demos.
GPT-3.5	2022 (ChatGPT launch)	~4K tokens (≈3,000 words) Later: up to 16K	Text only	Fine-tuned for chat (ChatGPT); more accurate & nuanced than GPT-3. Fast responses, good for simple tasks, instruction-following.	Free on ChatGPT (default); API very low cost (~$0.002/1K tokens).
GPT-4	Mar 14, 2023	8K tokens standard (32K extended context)	Text + limited images	Major leap in reasoning & creativity – passes exams in top 10% (e.g. Bar). Handles complex prompts, fewer hallucinations, some vision input (beta). Slower and costly.	ChatGPT Plus ($20/mo) required; API expensive (~$0.03–$0.06/1K tokens).
GPT-4 Turbo	Nov 2023	128K tokens (huge, ~100k words)	Text + images (enhanced vision)	Optimized GPT-4 – 3× faster and cheaper. Nearly same quality for most tasks. Updated knowledge (trained to 2023). Great for long documents, high-volume chatbots.	Included in ChatGPT (Plus) as behind-the-scenes upgrades; API ~3× cheaper than GPT-4.
GPT-4 Omni (4o)	May 13, 2024	128K tokens (extended)	Text, images & audio (full multimodal)	Flagship model: understands all modalities (vision+voice) in one. Engages in real-time voice chat, advanced image analysis. Most accurate model (improved context retention, least bias). Powers ChatGPT’s voice and vision features.	ChatGPT Plus default (2025); API costs reduced (50% cheaper than Turbo); “mini” version available for faster, cheaper use.

Table: Key differences between GPT-3, GPT-3.5, GPT-4, GPT-4 Turbo, and GPT-4 Omni.

(Sources: OpenAI documentation and announcements, TechTarget/Techtarget overview, Cointelegraph metrics, and OpenAI partner reports.)

As the table shows, each step in the GPT series brought notable enhancements – from GPT-3’s introduction of powerful language generation to GPT-4’s sophistication and then GPT-4 Omni’s multimodal mastery. Next, let’s look more closely at some of these difference areas and what they mean for ChatGPT users.

Context Length: From Short Chats to Remembering a Book

One of the biggest practical differences between GPT-3 era models and GPT-4 (and its variants) is the context window size. This determines how much text the model can keep “in mind” during a single prompt or conversation.

GPT-3 had a context of only a few thousand tokens (~2k tokens). In usage, this meant it could easily lose track of earlier conversation points or couldn’t handle long texts in one go. If you exceeded the limit, it would forget the earliest parts.
GPT-3.5 improved this to around 4k tokens by default, which is about 3,000 words of memory (good for moderate conversations). Some versions of GPT-3.5 even offered up to 16k tokens. This was a doubling of the “memory”, making ChatGPT better at holding context and following longer instructions than GPT-3.
GPT-4 launched with 8k tokens by default, and an extended 32k-token option. This was a dramatic upgrade – ~4–8 times GPT-3.5’s typical context. An 8k token window allows roughly 6 pages of text input. With 32k tokens (~24,000 words, about 40-50 pages of text), one could feed entire short reports or long conversations without issues. GPT-4 could “think” with more information at once, leading to more coherent and contextually relevant responses over long dialogues.
GPT-4 Turbo shattered previous limits with a 128k token context window. That’s over 100,000 words – roughly equivalent to a 300-page novel loaded into memory in one prompt! For users, this is nearly limitless in day-to-day terms: you could ask ChatGPT (with Turbo) to summarize a full book if you provided the text, or a developer could supply an entire codebase file set for analysis. ChatGPT can carry on extremely long discussions now without forgetting the early parts. It’s like having a conversation where the AI remembers everything you’ve ever said to it that session. This was transformational for use cases like legal document analysis, lengthy research summarization, or multi-session interactive stories.
GPT-4 Omni also supports the 128k context, maintaining that vast memory. In addition, Omni’s improvements in context handling (as noted, better retention accuracy over many turns) mean it is especially skilled at using that large window effectively. ChatGPT with Omni can keep track of complex threads of discussion, various references and callbacks in conversation, even if the dialogue spans hours or involves large inputs like combined text and images.

What it means for you: In 2025, ChatGPT can effectively remember and process more information than ever.

If you have a very detailed question that involves multiple parts or a large text, the newer models can handle it in one go, whereas older GPT-3-based ChatGPT might require chunking the input or it might start forgetting earlier details.

This extended context makes ChatGPT much more useful for things like analyzing lengthy PDFs, going through long transcripts, or solving problems that require referencing many prior details. The conversation flow is more natural – you don’t hit a “wall” where the AI resets its memory as easily as before.

For example, a lawyer could paste a huge legal brief into ChatGPT (with GPT-4 Turbo/Omni) and get a summary or critique that considers the entire document at once. A student could have ChatGPT (Omni) go through a full textbook chapter and ask questions about it.

These scenarios simply were not possible with GPT-3 or even GPT-3.5 due to their short memory spans. Now, with up to 128k tokens of context, ChatGPT can be truly context-aware over long sessions.

Multimodality: From Text-Only to Seeing and Speaking

Another revolutionary change is multimodality – the ability of the AI to handle inputs and outputs beyond just plain text. Here’s how it evolved:

GPT-3 and GPT-3.5: Unimodal (text-only). These models can only read text and output text. ChatGPT built on GPT-3.5 could not “see” images or “hear” audio. Every prompt had to be typed, and every response was written. This limited its use in contexts where visual data or spoken interaction might be useful.
GPT-4: Multimodal input (text + image). GPT-4 introduced the capability to analyze images. Though initially not widely available, it demonstrated the model could interpret visual inputs. For example, testers showed GPT-4 images and it could describe them or reason about them. However, GPT-4’s output was still text-only (no built-in image generation or audio output from the model itself). It was a big step forward, but not a complete multimodal experience yet for most users until it was gradually integrated (e.g., ChatGPT Vision feature rollout).
GPT-4 Turbo: Enhanced vision and integration. Turbo further solidified image handling and also facilitated integration with tools like DALL·E 3 for creating images. In ChatGPT, by late 2023, users could click a button to attach an image to their query. The model (GPT-4 under the hood, likely the Turbo variant) could then see that image and respond accordingly. This was the beginning of ChatGPT becoming more than a text chatbot – it could act like a basic visual assistant (e.g., describing images, reading screenshots, analyzing charts). Still, audio was not handled directly by GPT-4 Turbo itself – any voice features (like the text-to-speech in the ChatGPT mobile app) were separate from the model.
GPT-4 Omni: Fully multimodal – text, images, and audio, both input and output. This is where it all comes together. GPT-4o can accept images, listen to audio, and output in text or even spoken voice. In the ChatGPT interface, this means you can use the microphone to talk to ChatGPT (your voice is converted to text via OpenAI’s Whisper, and then fed to GPT-4o) and GPT-4o can reply with a realistic synthesized voice. Additionally, GPT-4o can understand audio inputs directly – for instance, you could upload an audio clip and ask ChatGPT to transcribe or summarize it (combining Whisper tech with GPT’s understanding). And of course, image input is fully supported, so you can have multi-turn conversations about a visual (e.g., “What is in this photo? … Now based on that, what do you think happened?”). The model even has some capability with video (likely by interpreting frames or via integrated tools) as suggested by reports, though the ChatGPT UI doesn’t yet directly accept video files.

In effect, ChatGPT in 2025 with GPT-4 Omni feels like a universal assistant. You’re not limited to just typing questions. You can show it something or speak to it naturally.

This opens up huge new use cases. For example:

You can snap a picture of a homework problem, and ask ChatGPT to explain how to solve it.
You can take a photo of a sign or menu in a foreign language and have ChatGPT (with vision) translate and explain it.
You can have a hands-free conversation with ChatGPT on your phone while driving, asking it to look up info or just chat (it listens and talks back).
People with visual impairments can use ChatGPT’s image understanding to get descriptions of images or their surroundings (much like the Be My Eyes app integration with GPT-4).
ChatGPT can help you analyze an image – e.g. “Here’s a screenshot of my code error, what does it mean?” or “Look at this chart, what trends do you see?”.

All these were impossible with GPT-3 or the initial ChatGPT. Now, multimodal AI is a reality, making ChatGPT far more engaging and useful. It’s like going from a blind, mute genius that can only text, to a genius that can see and converse with the world.

Accuracy and Performance: GPT-4’s Edge Over GPT-3

When it comes to raw capability – being correct, coherent, and helpful – GPT-4 and its successors represent a significant improvement over GPT-3/3.5. Here’s how they stack up:

Reasoning and Complex Tasks: GPT-4’s ability to handle complexity is much better. As mentioned, on standardized exams designed for humans, GPT-4 often performs at or above human average, whereas GPT-3.5 falls short. For instance, GPT-4’s bar exam performance was around top 10%, versus GPT-3.5 at bottom 10%. GPT-4 also scored higher on other exams (like SATs, LSAT, AP tests) and on hard logic puzzles where GPT-3.5 would fail. This means if you ask a very tricky question or multi-step problem, GPT-4 is more likely to get it right. It can follow long chains of thought, do math more reliably, and parse nuances in a prompt (like understanding what you really mean in a subtle request). GPT-3.5 might mess up those things more often.
Factual Accuracy: With each iteration, OpenAI reduced the “hallucination” tendency somewhat. GPT-4 is 40% more likely to produce factual responses than GPT-3.5 in OpenAI’s evaluations. GPT-4 Omni improves further on accuracy, with some metrics showing ~89% accuracy on complex queries vs 80% for GPT-3.5. In everyday terms, ChatGPT using GPT-4 has a better chance of giving you correct information and fewer random made-up facts. (Though it’s not perfect – all AI models can still produce errors, so one must always verify critical info.)
Steerability and Coherence: GPT-4 is more controllable and coherent in style. It’s better at following instructions like “Answer in a formal tone” or formatting outputs as asked. It also handles ambiguous queries more gracefully by asking clarifying questions or giving a nuanced answer, whereas GPT-3.5 might give a generic or slightly off response. Users find GPT-4’s answers tend to be more on-point and detailed. GPT-4 Turbo and Omni introduced features like a “JSON mode” to force well-formatted outputs and function calling improvements, which make the model’s outputs more predictable and easier to integrate into applications.
Creativity: GPT-4 has an edge in creative tasks too. It can produce more imaginative and contextually rich content – like stories, poems, or jokes – with better narrative consistency than GPT-3.5. GPT-4 Omni reportedly has an even higher “creativity index” (as one source measured, 85 vs 75 for GPT-3.5). So for content generation where creativity matters (writing scripts, brainstorming ideas, composing lyrics, etc.), GPT-4-level models give a noticeable boost in quality.
Consistency and Memory: As discussed, the larger context allows GPT-4 to be consistent over longer outputs. If you ask GPT-4 to write a 2000-word essay, it’s more likely to stay on track from start to finish, whereas GPT-3.5 might lose focus or contradict itself halfway. GPT-4 Omni’s context retention (92% over 10 turns vs GPT-3.5’s 83%) means in a long conversation it’s less prone to forgetting what was said. This yields a more human-like flow in chats.

To illustrate the difference: Suppose you gave both GPT-3.5 and GPT-4 a complex riddle or a task like “Plan a 5-step project to build a treehouse, considering budget constraints and safety regulations”. A GPT-3.5 response might be simpler or miss some nuances (maybe it forgets to account for a step or gives a generic plan).

GPT-4 would likely produce a more thorough plan with detailed steps, possibly citing the safety aspects and suggesting materials within budget, etc. The GPT-4 answer will just be more insightful and detailed, thanks to its advanced reasoning.

In real-world use, these accuracy improvements translate to higher success rates in applications: GPT-4-based ChatGPT is better at coding assistance (it writes code that runs more often on the first try), better at tutoring (gives correct explanations), and generally better at understanding what you ask (fewer “that’s not what I meant” moments).

Of course, GPT-4 is not infallible – it can still produce confident-sounding incorrect answers on occasion. But it’s a clear step up in trustworthiness.

When using ChatGPT for important tasks, the GPT-4 model is preferred for its reliability.

Many professionals (developers, researchers, writers) specifically use GPT-4 in ChatGPT Plus when they need the best quality outputs, even if it’s a bit slower, because it pays off in accuracy and depth.

Pricing and Access Considerations

From a user standpoint, you might wonder: How do I get to use these different GPT versions? and what do they cost?. Here’s the landscape in 2025:

ChatGPT Free vs Plus: By default, the free version of ChatGPT still uses GPT-3.5. This gives you fast responses and decent capabilities for everyday queries, at no cost. However, it won’t have the advanced features like vision or the improved reasoning of GPT-4. To access GPT-4 (and GPT-4 Turbo/Omni), you typically need a ChatGPT Plus subscription (which has remained at $20/month). ChatGPT Plus users get the option to use GPT-4 models with enhanced capabilities. As of 2025, ChatGPT Plus runs on GPT-4 Omni by default, meaning Plus users automatically enjoy the multimodal, more accurate AI. This subscription also unlocks the voice conversation and image upload features in the ChatGPT interface (as those rely on GPT-4o).
ChatGPT Enterprise/Team: For businesses or power-users, OpenAI has ChatGPT Enterprise plans which offer even higher limits, sharing among teams, and possibly priority access to latest models. These plans would certainly include GPT-4 Turbo/Omni usage without the message caps that individual Plus might have. If you’re a professional relying heavily on ChatGPT, these options ensure you always get the GPT-4-level model and can use it extensively.
API Access: Developers who want to integrate these models into their own apps can use the OpenAI API (or Azure’s OpenAI service). Here, pricing varies by model. GPT-3.5 is extremely cheap – on the order of $0.002 per 1K tokens (that’s about ~$2 per million tokens, input or output). GPT-4 (original) was much pricier – around $0.03 to $0.06 per 1K tokens for input/output respectively. GPT-4 32K was double that. GPT-4 Turbo, introduced in late 2023, cut those prices significantly (roughly $0.01 and $0.03 per 1K tokens). By mid-2024, GPT-4 Omni’s API pricing was even more attractive: about $2.5 per 1M input tokens and $10 per 1M output tokens globally. In simpler terms, that is $0.0025 per 1K input and $0.01 per 1K output – making Omni nearly as cheap to use as GPT-3.5 for inputs, and only ~5x GPT-3.5’s cost for outputs, despite being the top model. Moreover, Omni has higher rate limits (you can send more tokens per minute) – it allows 5× the throughput of Turbo as per OpenAI’s specs.
Usage Limits: Initially, when GPT-4 came out, Plus users had message caps (e.g. 25 messages/3 hours, then raised to 50, etc.). With the introduction of Turbo and Omni and better scaling, OpenAI has relaxed these limits. As of 2025, Plus users can have a high number of messages with GPT-4o (some sources mention ~100 messages per 4 hours as a cap). Free users remain limited mostly by slower model response and occasional capacity issues at peak times, but otherwise can message GPT-3.5 as much as needed. Essentially, access to GPT-4’s power requires a paid tier in most cases, but the value you get (far better performance) can be worth it if you need those capabilities.
Which Model to Choose? OpenAI and community guides often break it down like this: for budget-conscious or very simple tasks, GPT-3.5 is fine (and free on ChatGPT). For serious projects or complex tasks, GPT-4/GPT-4o is preferable for its superior quality. GPT-4 Turbo vs Omni comes down to whether you need multimodal and absolute best accuracy (Omni) or just fast text processing (Turbo). In many cases, ChatGPT will automatically use Omni if you have Plus, unless you specifically use the API and request Turbo.

From a content publisher perspective (like websites integrating AI), the dropping costs of GPT-4 Turbo and Omni by 2024 made it possible to use these advanced models at scale.

For example, an AI writing assistant service could afford to use GPT-4 Turbo to generate content for users without exorbitant expense, whereas in early 2023 using GPT-4 for that might have been cost-prohibitive. So the trend is that GPT’s power is getting cheaper and more accessible.

For the end user, the key takeaway is: ChatGPT’s best features come with GPT-4 (Plus subscription), but the gap in cost and speed has narrowed.

If you haven’t tried ChatGPT with GPT-4 since it first launched, you might be surprised how much more responsive and capable it has become with Turbo and Omni updates – and how those are now integrated into the ChatGPT experience.

Real-World Use Cases: How GPT-4 Improves on GPT-3

Let’s illustrate some real-world scenarios where GPT-4 (and its later variants) show clear improvements over the earlier GPT-3/3.5-based ChatGPT:

Coding and Debugging: GPT-3 was decent at generating code, but often the code wouldn’t run or had logical errors that it couldn’t fix. GPT-3.5 (ChatGPT initial version) improved this somewhat by following instructions to adjust code. But GPT-4 is a game-changer for programmers. It can handle larger code snippets due to more context, understand the intent better, and even find bugs or suggest optimizations. For example, users have given GPT-4 an entire function or error trace, and it can pinpoint the bug and correct the code in one go – something GPT-3.5 might fumble. GPT-4’s higher reliability also means it’s better at writing complex algorithms or understanding new programming libraries/frameworks (given its training data and reasoning skill). Developers use GPT-4 in tools like GitHub Copilot X for an AI pair programmer that’s far more effective than the earlier GPT-3-based Copilot.
Writing and Content Creation: With GPT-3.5, you could generate essays, but often they needed heavy editing for factual accuracy or coherence. GPT-4 can produce longer, well-structured articles or stories with minimal guidance. It’s particularly good at maintaining a style or theme throughout a piece. If you need a 1500-word blog post on a technical topic, GPT-4 is more likely to produce one that’s logically organized and correct on details (assuming the info is in training data), whereas GPT-3.5 might wander off-topic or include some incorrect assertions. The creative writing ability is also richer – GPT-4 can emulate various literary styles, create more intricate plots in storytelling, and even compose lyrics or poetry with better rhyme and meter. Real users have employed GPT-4 to draft marketing content, professional emails, and even help with novel writing, thanks to its enhanced coherence and nuance.
Education and Tutoring: Many students and lifelong learners use ChatGPT as a study aid or tutor. GPT-3.5 could answer questions but might oversimplify or sometimes misstate facts it wasn’t confident on. GPT-4 excels at explanation. It can break down complex concepts (like calculus or quantum physics) into simpler terms more accurately. It’s also better at Socratic tutoring – asking the student questions and guiding them to the answer, rather than just spitting out the solution. Khan Academy noticed this and built a tutoring system around GPT-4, taking advantage of its ability to follow pedagogical strategies. Additionally, GPT-4’s broader knowledge (with updated info and more training data) means it can answer a wider range of academic questions correctly. GPT-4 Omni even adds the ability for a student to ask a question by voice or include an image (like a diagram or geometry problem) – making learning more interactive. This was not feasible with GPT-3. Using ChatGPT in 2025, a student can literally take a photo of a tricky math problem, and have the AI walk them through solving it step by step, with spoken guidance.
Professional Assistance: Professionals across fields use ChatGPT for research and productivity. GPT-4 has enabled tasks like: Legal research summaries (analyzing contracts, summarizing case law – GPT-4’s higher context means it can ingest more material at once and provide a cohesive summary, which GPT-3 couldn’t do due to context limits), Financial analysis (it can digest financial reports or market data and answer questions about them), Medical information (GPT-4 was found to score well on medical exams, so it provides more accurate medical explanations or diagnostic suggestions – always with a disclaimer to consult a doctor, of course). The reliability and depth of GPT-4’s answers instill a bit more confidence for these high-stakes uses, though human oversight remains crucial. GPT-4’s use in these areas is more like having a diligent assistant to offload initial drafts or research, which the professional then reviews. GPT-3’s outputs in such cases often required more extensive fact-checking.
Customer Service and Chatbots: Many businesses implemented GPT-3.5-based chatbots for customer support. GPT-4’s improvements (especially GPT-4 Turbo’s cost savings) allowed more companies to use the more advanced model. The result is customer service bots that are better at understanding customer queries (less frustrating “I don’t understand” replies) and can handle multi-turn troubleshooting. For example, a GPT-4 bot can guide a user through a technical support process, remembering details the user provided earlier in the conversation due to the larger context. GPT-4 Omni can even accept a screenshot from a user (say, an error message on their screen) and directly address it – an immense advantage in support scenarios. This level of service was unattainable with GPT-3 bots.
Visual analysis and creativity: New use cases emerged with multimodal abilities. Photographers or designers can ask ChatGPT (with GPT-4o) for feedback on an image or suggestions (“What could I improve in this photo’s composition?”). People on social media use it to generate alt-text or descriptions for images. Even for fun, users give GPT-4 images (like a doodle or a meme) and enjoy the AI’s interpretation or humor. GPT-3.5 had no such capability at all. Moreover, GPT-4’s integration with DALL-E 3 means ChatGPT can not only analyze images but help create them – e.g., you can ask ChatGPT to “draw an image of X” and it will behind the scenes use image generation to fulfill your request, something GPT-3 obviously never did.

These examples show that GPT-4’s advancements unlock a broader and deeper range of applications. It’s not just about doing the same old tasks a bit better; it allows AI to assist in ways that simply weren’t possible before (like interacting with images/voice or tackling very complex multi-step problems).

Users frequently comment on the difference. A common sentiment is: once you get used to GPT-4’s quality, going back to GPT-3.5 feels noticeable – things might get misinterpreted or the answers lack the detail you’ve come to expect.

GPT-4 (especially with Turbo/Omni) makes ChatGPT feel more like an expert partner rather than just a helpful tool.

Impact on ChatGPT’s User Experience in 2025

Thanks to GPT-4 Turbo and GPT-4 Omni, ChatGPT in 2025 is more dynamic, interactive, and powerful than ever before. Here are some of the most impactful changes in the user experience:

Conversational Flow: ChatGPT’s flow is smoother with GPT-4 Turbo’s speed and context. Even over a long back-and-forth session, the AI stays on topic and you rarely have to re-clarify what you meant. It feels more like a continuous conversation rather than fragmented Q&A. The faster responses (Omni is even 2× faster than Turbo in some cases) mean using ChatGPT is almost real-time, which is especially noticeable in voice mode.
Voice Conversations: Perhaps the most user-visible change is voice. Now you can talk to ChatGPT and it talks back in a human-like voice. This makes interacting with it feel dramatically different – more personal and accessible. People are using ChatGPT on their phones like they would use Siri or Alexa, but often finding ChatGPT more capable and intelligent in its answers. The voice feature also helps those who prefer speaking or have difficulty typing. It basically turns ChatGPT into a virtual AI assistant you can chat with naturally, fulfilling a bit of that sci-fi vision of conversing with your computer. OpenAI’s research indicated GPT-4o’s voice responses sound very human, which reduces the barrier of “talking to a robot”.
Image Understanding in Chat: Users can now upload images in the ChatGPT interface and ask questions. For example, one might upload a chart from a report and ask “Can you summarize the insights from this chart?” – ChatGPT will analyze the image and provide an answer. This visual understanding is a game-changer for many domains: medicine (diagnostic images), cooking (what can I make with these ingredients? [with a photo]), troubleshooting (photo of a device setup to ask what’s wrong), etc. It makes ChatGPT a multi-sense AI, not just text-based. The experience of showing rather than telling is novel for many users who were used to only typing to the AI.
Fewer Errors & More Trust: With GPT-4 Omni running under the hood, users notice that ChatGPT is more often correct or at least provides sources/citations when it has them (especially if using the browsing tool or plugins). While you should still double-check important info, the overall trust in ChatGPT’s answers has increased. It’s also better at saying “I’m not sure” if truly unsure, rather than guessing, thanks to alignment improvements. This builds user confidence in relying on ChatGPT for more things – whether it’s helping with research or making decisions (with caution). The model’s reductions in biased or inappropriate outputs also mean fewer instances of the AI saying something off-putting, which improves user comfort and safety.
Personalization & Tools: By 2025, ChatGPT also introduced the concept of Custom GPTs (letting users create tailored versions of the chatbot for specific tasks) and fine-tuning options. These leverage the GPT-4 models. For instance, a company can fine-tune GPT-4 Turbo on its product manuals, and then ChatGPT (with that fine-tune) will expertly answer customer questions about those products. Or an individual can create a “Travel Planner GPT” that remembers their preferences. The richer model allows these customizations to work well, because GPT-4 can absorb the fine-tuning instructions and still perform reliably. In contrast, GPT-3-level models sometimes struggled with maintaining quality after fine-tuning on new data. The user experience becomes more personalized and context-aware.
Use of Plugins/Tools: ChatGPT in 2025 has the ability to use plugins and external tools (like browsing, calculating, etc.). GPT-4 models are more adept at using these tools effectively. For example, GPT-4 will know when to invoke the web browser to get up-to-date information, whereas GPT-3.5 might have been less effective in doing so. The synergy of GPT-4’s understanding with external tools means ChatGPT can do things like live web searches for you, run code, or use third-party services, all within the chat – making it a more comprehensive assistant. The user doesn’t have to switch contexts; the AI figures out how to get the answer, which is a smoother experience.

In essence, GPT-4 Turbo and Omni have made ChatGPT more human-adjacent. It can hold a long conversation, see and talk, and generally feels more like interacting with an knowledgeable entity rather than a limited bot.

Many early users of ChatGPT (GPT-3.5) were impressed but often ran into its limits quickly – e.g., short memory, inability to handle images, obvious mistakes in logic. In 2025, those limits have been pushed much further out.

New users are often amazed that they can, say, have a half-hour spoken conversation with ChatGPT about a complex topic, show it relevant pictures during the talk, and get insightful, accurate answers throughout. That kind of rich interaction was science fiction just a couple years ago and is now real.

From a developer perspective, the user experience improvements mean that if you are building an app or service that uses ChatGPT or the API, you can deliver more value – voice interfaces, image analysis features, longer and more context-aware interactions – which can delight users.

Many apps (education, productivity, creative, etc.) have integrated GPT-4 capabilities to give their users a ChatGPT-like experience specialized to their domain.

Conclusion: The Future of ChatGPT and How to Try It

The evolution from GPT-3 to GPT-4 (and Turbo/Omni) marks a huge stride in AI capabilities in a short time. ChatGPT (2025) powered by GPT-4 Omni is far more than a chatting tool – it’s a versatile AI assistant that can understand context, visuals, and speech, and deliver accurate, coherent responses.

We’ve seen how GPT-4 introduced advanced reasoning and multimodality, how GPT-4 Turbo brought speed and scale, and how GPT-4 Omni unified voice, vision, and text into an all-in-one powerhouse.

The comparison GPT-4 vs GPT-3 clearly shows that today’s ChatGPT is smarter, more interactive, and more reliable than the ChatGPT of 2022.

For users, this means a better experience whether you’re a student, developer, professional, or just curious.

You can get more done with ChatGPT – be it solving complex problems, learning new concepts, generating creative content, or even just having a conversational companion that remembers and understands you better.

And with these improvements happening, it’s exciting to imagine what the next versions (GPT-4.5? GPT-5?) will bring in the coming years.

If you haven’t tried ChatGPT with its latest features yet, now is a great time to experience it. Try ChatGPT for free to get a sense of GPT-3.5’s capabilities, or upgrade to ChatGPT Plus to unlock GPT-4 Omni and see the difference firsthand in how it can see images and talk with you.

Many users find the Plus tier transforms what they can do with ChatGPT thanks to the GPT-4 model’s power.

Finally, if you’re interested in exploring more about GPT models and staying updated on future developments, consider visiting GPT-Gate.Chat and our resources.

We regularly share insights, guides, and tips on getting the most out of ChatGPT and OpenAI’s latest AI offerings. Whether you want to leverage AI for work or personal projects, or you’re just fascinated by the technology, we aim to be your gateway to all things GPT!

Ready to experience the new ChatGPT? Head over to ChatGPT and put it to the test – ask it to plan your next vacation itinerary with images, debug some code, or simply have a friendly chat.

With GPT-4 vs GPT-3, the difference is night and day. Embrace the future of AI conversation today, and happy chatting!