What is GPT 5? How OpenAI is upgrading your ChatGPT experience

OpenAI has released GPT-5, the next major step in its line of large language models, and with it a new way of working: built-in “thinking” models that kick in when a problem needs deeper, multi-step reasoning. This is not a mere speed bump. It is a change in how the system decides when to answer quickly and when to pause and deliberate, and that will alter what people expect from ChatGPT in everyday use, at work, and in developer products. 

The basics: one system, multiple minds

GPT-5 is presented by OpenAI as a unified system made up of three kinds of components. First, a fast and efficient model that handles routine questions. Second, a deeper reasoning model, what OpenAI calls GPT-5 (with thinking), that performs extended, structured computation for hard problems. Third, a smart router that decides which component to use for each query, and can route a conversation between them dynamically. For developers, the API exposes multiple sizes, including gpt-5, gpt-5-mini and gpt-5-nano, so teams can trade off cost, latency and capability. 

Why this matters

Models until now were mostly a single response generator tuned for many tasks. GPT-5 formally separates “quick answers” from “deep thought”. That means short queries will stay fast and cheap, while complex tasks such as deep debugging, lengthy mathematical derivations or clinical case summarisation are automatically handled by a different, heavier engine that is allowed to take more internal compute and produce more careful outputs. 

What “thinking” actually means

When OpenAI talks about thinking models, it refers to several technical and product changes. The reasoning model runs longer internal chains of computation and uses scaled parallel test time compute to reduce errors on multi-step problems. In product terms, it produces answers that are more methodical, that show intermediate steps where useful, and that are better at admitting uncertainty.

A router evaluates the nature of an incoming request and chooses whether to answer with the fast model, the thinking model, or to call external tools such as calendars, web connectors or code execution agents. Users can also ask explicitly for reasoning by selecting GPT-5 Thinking in the picker or by telling the model to “think hard about this”.

In the API, developers get explicit variants so they can use the reasoning model when they want higher accuracy and the mini or nano variants for scale and latency.

Put simply, “thinking” is a managed trade-off between latency, cost and correctness. For the user, it should mean fewer confident but wrong answers and more transparent, stepwise solutions when those are needed.

What improvements will you notice?

OpenAI and multiple early reports highlight a few areas where GPT-5 moves the needle.

• Better reasoning: Benchmarks and early tests show substantial gains on mathematical, coding and domain-specific tasks. OpenAI reports improvements across multiple standard evaluations, and independent early benchmarks find that enabling the thinking mode reduces error and hallucination rates noticeably on hard prompts.
• Stronger coding and agentic behaviour: GPT-5 is tuned for end-to-end coding tasks and for running agentic flows that coordinate tools. Expect better generation of complete projects, fewer missed edge cases, and models that can chain API calls and checks more reliably. This is why several developer products signalled quick adoption. 
• Multimodal and practical skills: GPT-5 improves image, video and audio understanding alongside text. That makes it better at tasks requiring visual context and at workflows where text and media mix. OpenAI says the model handles much larger token contexts, which helps with long documents and multi-file projects. 

How the user experience changes in ChatGPT

For everyday users, the change will usually be subtle and positive.

Search and short chats remain fast. The system will default to the fast model for common queries, so the snappy feel of ChatGPT is preserved.

For complex prompts, ChatGPT will either automatically “think” or offer a clear option. Users who need rigorous outputs can pick GPT-5 Thinking or Pro tiers that give higher usage and access to GPT-5 Pro, which is OpenAI’s most intensive reasoning variant.

Tooling and agents will be smoother. When a request involves calendars, code execution or multi-step web lookups, the router can hand the job to the reasoning model and orchestrate tool calls, reducing the manual back and forth that used to be necessary. Early integrations from large platform partners show this working across email, calendar and coding tools. 

The net effect is that people who already rely on ChatGPT for light tasks will not be inconvenienced, while users who need deeper work will get an experience that feels more like collaborating with a careful human partner.

Limits, safety and the AGI question

OpenAI frames GPT-5 as its most capable model so far, but not Artificial General Intelligence (AGI). The company emphasises reductions in hallucinations and improved safety measures, and says the model is better at admitting limits and recommending human experts when necessary. That is important because higher capability models can make more persuasive mistakes if left unchecked.

What this means for the industry

GPT-5 is likely to accelerate how AI is used inside products. Better out-of-the-box reasoning and built-in agentic capabilities make it easier for companies to ship features that previously required heavy engineering. Microsoft and other platform partners are already signalling broad product-level integration. That will widen the gap between teams that can use high-quality reasoning models and those that cannot. It also intensifies debates on auditing, provenance and responsibility because models will be making more consequential, automated decisions.