ChatGPT-4 vs ChatGPT-5: Full Comparison Breakdown

ChatGPT and GPT models behind it are no longer new terms to many people. Since its first launch in late 2022, the chatbot has gone viral, changing our perception and understanding of the term AI. In 2025, OpenAI continued to debut the fifth version of the GPT model, marking new advancements in this technology. But are there any differences between Chat GPT-4 vs 5? Does GPT-5 truly improve on its predecessor? Let’s find the answer in today’s article!

Introduction

Before diving into the key differences between the two latest GPT versions: GPT-4 and GPT-5, let’s take a quick look at what they are:

Chat GPT-4

GPT-4 was introduced for the first time on March 14, 2023. It’s considered a large multimodal model that can receive text and image inputs and return text outputs. The GPT model performs well on many academic and professional tasks like humans, proving more creative, reliable, and capable of processing more nuanced instructions like GPT-3.5. These tasks range from sales, content moderation, and programming to assisting humans in AI output assessment. It also outperforms its predecessors and other language models (e.g., PaLM, Chinchilla) in non-English tasks. 

As mentioned, GPT-4 can handle a wide range of visual inputs (e.g., documents with text and photographs, screenshots, and diagrams). This capability is augmented by test-time techniques, such as few-shot and chain-of-thought prompting. Further, developers can guide how the model behaves (example below) thanks to a “system message.” This is a special instruction that shapes the AI’s behaviors at the start of the chat, hence giving users more personalized experiences. 

In late 2023, OpenAI continued to introduce the GPT-4 Turbo and GPT-4 Turbo with Vision. On May 13, 2024, the company released GPT-4o, which was a significant advancement of GPT-4. This new version could handle and generate outputs across text, image, and audio modalities in real time. It also responds faster, shows improved performance on non-English languages, and receives praise from the developer community for its coding support. 

Chat GPT-5

GPT 5 was released on August 7, 2025. It was introduced as a multi-model system that unifies different internal models (or variants of GPT-5) to handle different tasks.

• A smart, efficient model processes most everyday queries quickly and effectively.
• A deeper reasoning model (GPT-5 thinking) handles more complex or harder reasoning tasks.
• A real-time router decides which model to use based on your explicit intent (e.g., when you say, “think harder about this problem,” the router will automatically switch to the GPT-5 thinking), conversation type, complexity, and tool needs. The router also learn from real-time signals, like when you switch models, how you prefer responses, and how accurate answers are, to improve over time.
• Mini models handle the remaining queries when the usage limit of each model is reached.

According to OpenAI, all these capabilities will be integrated into a single system in the future. So, is GPT-5 free to use now? The answer is yes. Like its predecessors, GPT-5 is available for free-tier users. Like GPT-4, GPT-5 is trained on Microsoft Azure AI supercomputers and proves more reliable when reasoning on complex, open-ended questions.

Details comparison between GPT-4 and GPT-5

This table compares the key differences between GPT-4 and GPT-5:

How does GPT-5 improve on GPT-4?

According to OpenAI’s announcement, GPT-5 has more significant improvements than its predecessor (GPT-4). Now, let’s dive deep into GPT-5’s capabilities to see how it can handle longer text, offer more context-aware responses, reduce hallucinations, and outperform in other tasks.

The ability to process much longer text

GPT-5 is capable of processing approximately 400K tokens for both input and output in API versions. Meanwhile, GPT-4’s context window is much smaller and not optimized for such long text. OpenAI expanded the maximum token limit of its improved version (GPT-4.1) to up to 1M tokens, allowing this model to better comprehend such long text. Having said that, GPT-5 still maintains the high ability in handling extensive documents while integrating routing and reasoning capabilities across its different versions. 

Better Memory and Contextual Awareness

In comparison with GPT-4, GPT-5 improves its memory-like behavior through a real-time routing system. If you have a long conversation that exceeds the limits of the model’s reasoning capacity, the router will automatically direct your chat to different “mini versions” of the model to continue to process. This keeps the dialogue smooth and avoids dropping context, hence ensuring the continuity across your conversation. 

OpenAI also introduces features like `previous_response_id` in the API. This feature allows a developer to send the ID of a previous response to the next request. Therefore, GPT-5 can “recall” what it already created rather than re-handling everything from scratch. This lowers redundant computation, enhances consistency across chats, and enables faster response time. 

Enhanced Integration with Agents and Tools

GPT-5 excels at following instructions and chaining various tool calls sequentially or concurrently to perform complex, evolving tasks efficiently. These capabilities allow GPT-5 to reliably process multi-step requests, coordinate across multiple tools, and adapt to ever-changing contexts. 

In OpenAI’s experiment with GPT-5, OpenAI o3, and GPT-4o in Function calling, the benchmark indicates that GPT-5 (without thinking) can handle industry-specific tasks (airline, retail, telecom) better than GPT-4o. This is attributed to its reasoning-first approach that helps choose the right function call, reduce mistakes, and allow for smoother automation. 

Another research from Salesforce’s team stated that GPT-5 outperforms other peers (including GPT-4o and GPT-4.1) in accessing external data sources and tools and completes 43.72% of tasks successfully (mainly related to financial analysis, 3D designing, and web search).

Lower Hallucinations or Few Errors

GPT-4, despite its improvements, still generates hallucinated, unreliable responses. It also makes reasoning mistakes, even on simple tasks. Therefore, human review is necessary for high-stakes use cases (e.g., law, healthcare, or safety-critical tasks). In an internal test of OpenAI with GPT-4, GPT-4 still ignores subtle details (e.g., mistakenly saying Elvis Presley was the son of actor). The creator also admitted that GPT-4 still includes various biases in its outputs, gives harmful advice, generates buggy or insecure code, and spreads misinformation. 

With significant effort, OpenAI’s GPT-5 significantly reduces hallucination rates. In comparison, its responses are approximately 45% less likely to contain factual errors than GPT-4o. This gap is even larger for GPT-5 thinking, even when it’s assigned to generate consistently precise long-form text. Further, only 11.6% of GPT-5’s responses (without thinking) contain at least one error, much lower than that of GPT-4o.  

Safer, Faster & More Honest Responses

Alongside improved factuality and reduced hallucinations, GPT‑5 (with thinking) more honestly communicates its actions and capabilities to the user, especially for tasks that are impossible, underspecified, or missing key tools. About this case, OpenAI conducted an experiment with GPT-5 (with thinking) and OpenAI-o3 by assigning GPT-5 to impossible coding tasks and missing multimodal assets (e.g., images).

Further, GPT-5 requires less thinking time and produces fewer output tokens across tasks, from graduate-level scientific problem solving and agentic coding to visual reasoning. For this reason, it’s considered to generate responses faster. 

GPT-5 is also introduced to handle safety differently from its past versions. When a user asks something harmful or risky, earlier models just refuse to answer. This refusal-based mechanism works well if the intent is obviously bad (e.g., “teach me to make a bomb”). But when the user’s intent is unclear or the information may have a dual use, it doesn’t work well. GPT-5 resolves this problem by offering the most helpful but still safe response. Even when it must refuse, it also explains why and suggests safer alternatives rather than only saying “no.” Below is an example:

Superior Multitasking

GPT-5 outperforms its predecessors in real-world queries and benchmarks, offering faster responses. Its capabilities mainly revolve around three tasks: coding, writing, and math.

Coding

GPT-5 performs better in generating complex front-end code and debugging large-scale repositories. It can also create aesthetically appealing websites or apps. According to earlier testers, the model presents a more polished and human-like design with elements like typography, white space, and spacing.

OpenAI illustrated several games created by GPT-5 using one prompt, like Rolling Ball, Pixel Paint, Typing Speed Race, Virtual Drum, and Lofi Visualizer. Here’s an example of how the model created the Rolling Ball minigame with one prompt:

Prompt:

Create a single-page app in a single HTML file with the following requirements:

• Name: Jumping Ball Runner
• Goal: Jump over obstacles to survive as long as possible.
• Features: Increasing speed, high score tracking, retry button, and funny sounds for actions and events.
• The UI should be colorful, with parallax scrolling backgrounds.
• The characters should look cartoonish and be fun to watch.
• The game should be enjoyable for everyone.

Writing and Q&A

GPT-5 also improves its writing capabilities to turn even vague and messy ideas into compelling, impactful writing. It proves more reliable in processing writing that involves structural ambiguity, like free verse or unrhymed iambic pentameter. This makes its writing more emotional, natural, and stylistically rich instead of just sounding formulaic and structural. Here’s an example of how GPT-5 produces free verse poetry in comparison with GPT-4o:

In other examples, GPT-5 offers more actionable, adaptable, and “authored” responses. For instance, GPT-5 helps create a witty, personal, and well-thought-out wedding toast with fresh, vivid language, recurring motifs, and layered callbacks. Meanwhile, GPT-4o uses reusable lines and standard structures (e.g., “We’re here today to celebrate love…”), making the response generic and template-like.

Health

More importantly, GPT-5 also acts as a healthcare partner to offer reliable, precise responses based on a user’s knowledge level, context, and geography. This allows it to offer safer and more helpful guides for a variety of scenarios. Like in this case, GPT‑5’s response uses a safer, criteria-based progression with clearer monitoring and regression rules, making it more adaptable and protective for the athlete.

GPT-5 also responds empathetically, organizes and explains information clearly for non-experts, and proactively flags key factors.

This capability helps GPT-5 achieve significantly higher scores than other models on HealthBench:

• This benchmark measures how different models handle realistic health conversations. Without thinking, GPT-5 performs worse than OpenAI-o3, but significantly better than GPT-4o. However, GPT-5 with reasoning can achieve much higher accuracy.

• This benchmark measures how different models handle challenging health-related conversations. Without thinking, GPT-5 performs worse than OpenAI-o3, while GPT-4o isn’t designed to possess this capability. However, GPT-5 with reasoning can achieve much higher accuracy.

• This benchmark measures the probability of generating inaccurate or hallucinated responses in challenging conversations. GPT-5 with or without reasoning achieves relatively low scores.

How GPT-5 Compares to GPT-4 in Real Use Cases

We’ve seen how OpenAI’s GPT-5 is promised to handle different tasks better and more efficiently than its predecessor. But how do real users think of this innovative model? Does it meet their expectations and truly improve their work efficiency? Let’s take a look:

First, GPT-5 works well in real-life tasks that require practicality, adaptability, and even empathy. In Tom’s Guide tests on seven different prompts, GPT-5 consistently generates more authentic, adaptable, human-like, and faster responses than GPT-4. This reflects its true capabilities as OpenAI claimed. Accordingly, GPT-5 comes out ahead in:

• Practical Planning: GPT-5 created a realistic, cost-effective plan that was more grounded than GPT-4’s response.
• Creative Writing: GPT-5 produced a much sharper and emotionally resonant opening for the novel in comparison to GPT-4.
• Beginner Coding: GPT-5 crafted clean, beginner-friendly code with detailed explanations when assigned to build simple HTML/CSS. Meanwhile, GPT-4’s response was overcomplicated.
• Emotional Support: GPT-5 showed its genuine empathy and warmth to cope with the user’s feeling of failure, while GPT-4 felt nonchalant and vague.
• Personalized Task Lists: GPT-4 built a concise, motivating to-do list for an ADHD-aware person, whereas GPT-4 missed the mark with too-long explanations.

Coding

Apart from such positive feedback, GPT-5 also received much criticism from the developer and writer community. Some users found GPT-5’s coding performance worse than older series, let alone significantly higher latency. Particularly, some prompts took GPT-4.1 around 2 or 3 seconds to run, yet this response time increased significantly to 30 to 70 seconds with GPT-5, which, however, generated weaker coding output and unreliable problem-solving. 

Content Creation

Content creators also share both good and terrible experiences with GPT-5. The model technically crafts shorter and more direct answers, with tighter logical reasoning. This capability makes the model ideal for business use, factual Q&A, and problem-solving. 

However, it fails in creative tasks. In fiction writing or storytelling, GPT-5 struggles to expand character arcs, carry scenes longer, or weave relationships. When the users edit and resend the previous response, GPT-5 continues to work with the original version, not the updated one (this phenomenon didn’t happen in GPT-4). GPT-5 is complained for ignoring important details at times, as it aggressively prioritizes what it thinks is “most crucial.” This speeds up summaries and analysis, but makes GPT-5 less comprehensive than GPT-4.

Overall Verdict

Facing such feedback, Sam Altman still claimed that “GPT-5 is the smartest model” OpenAI has ever done, but the model focuses on mass accessibility. Therefore, it may lack capabilities in some specific tasks. 

Despite some bad experiences, we still see notable advancements of GPT-5 across capabilities, especially in health. One research indicated that GPT-5 surpasses human-level performance on multimodal reasoning on some benchmarks, like MMLU, MedXpertQA, and MedQA, across medical categories (e.g., clinical knowledge, medical genetics, or anatomy). 

There’s a long way for GPT models to become perfect, and we look forward to seeing new advancements in GPT-5’s successor models.

FAQs

Is ChatGPT 5 bad?

The answer depends on what you expect from the model and what you compare it against. In comparison to GPT-4, the fifth version is capable of producing more natural, empathetic, and safer responses with improved reasoning capabilities. However, GPT-5 still receives many complaints for its noticeable weaknesses, especially in sophisticated coding and content creation tasks.

How will GPT-5 change the future of AI-powered tools?

The way GPT-5 was created reflects OpenAI’s directions and expectations toward AI’s future. For a traditional chat tool, GPT-5 is a unified platform that gathers different models to play specific roles. Instead of only responding, GPT-5 can call the right tools and act autonomously across workflows, from scheduling calendar events to managing documents without explicit instructions. 

Its capabilities also extend to processing multimodal data beyond text, including images, videos, and audio. This removes the need for users to juggle various model types for multimodal tasks. Further, GPT-5’s built-in chain-of-thought reasoning enables it to understand multi-step problems and create logical solutions better. This turns it into a valuable tool for tasks, like scientific reasoning, legal analysis, and healthcare diagnostics. 

With all these capabilities, GPT-5 paves the way for tools to become more intelligent and even take autonomous action effectively in the future.   

What is GPT 5 mini?

GPT-5 mini is a faster and more cost-effective version of OpenAI’s flagship GPT-5 model. It accepts text and images as input but returns text outputs. Below is some key information you may want to know about this variant:

What are the key features and specifications of GPT-5 mini?

• This variant uses advanced inference techniques (e.g., kernel fusion and tensor parallelism) to handle well-defined tasks and precise prompts with lower latency and compute power. This capability makes it useful in real-time and high-throughput use cases. 
• It has a large context window of up to 400,000 input tokens and produces up to 128,000 tokens of output. This allows the variant to process long documents or input prompts effectively.
• GPT-5 mini supports different tools through the Responses API for code interpreting, web search, and file search.

What are the limitations of GPT-5 mini?

• GPT-5 mini is engineered for speed and efficiency, not for complex reasoning (e.g., multi-step math proofs or legal reasoning).
• The mini model doesn’t excel at handling sensitive or crucial use cases (e.g., legal documents, financial data, or medical advice). So you need to verify its generated outputs before using. 
• If the input is very long or deeply interdependent, GPT-5 mini may lack details. For example, in a 500-page legal document, if clause 7 depends on clause 25, GPT-5 mini might miss this important detail. Therefore, for huge documents with multiple dependencies, using the full GPT-5 or its “thinking” variants is still better.  

How much does the GPT-5 mini cost?

• Input: $0.250 / 1M tokens
• Cached input: $0.025 / 1M tokens
• Output: $2.000 / 1M tokens

What are the common use cases or applications of GPT-5 mini?

• High-volume chatbots that need to handle various queries fast and at scale, but deep reasoning isn’t necessary.
• Tasks (like summarization or info search) with moderately long input.
• Developer tooling (e.g., code generation, auto-reviews, or lightweight CI checks).