OpenAI has launched GPT-5 with immediate availability for all 700 million users of ChatGPT, free and paid.
The company has built this model as a tool for both conversation and solving real enterprise problems in sectors like software engineering, financial services, and healthcare.
From day one, GPT-5 will replace its predecessors across ChatGPT platforms, and it’s being offered with expanded access tiers, including free usage, a $20/month Plus subscription, and a $200/month Pro tier.
Developers also now have access through the OpenAI API, with three variants: GPT-5, GPT-5-mini, and GPT-5-nano. These versions differ in how long they spend “thinking” about problems, with pricing ranging from $1.25 to $10 per million tokens.
While consumer interest in AI remains high, enterprise adoption has been slower. OpenAI is hoping GPT-5 can tip the scale. The model delivers what CEO Sam Altman called “software on demand,” capable of building fully functional apps from natural language prompts.
“GPT-5 is really the first time that I think one of our mainline models has felt like you can ask a legitimate expert, a PhD-level expert, anything,” Altman said during a press conference.
Behind the launch, OpenAI has struggled with the sheer technical demands of scaling up its models. The company has faced hardware failures during training, hit limits in the availability of new high-quality training data, and spent months waiting for results from high-cost training runs.
At the same time, it has had to justify its skyrocketing costs, including investor expectations built on a potential $500 billion valuation and signing bonuses of up to $100 million for top AI talent.
Back then, when GPT-4 was launched, it passed a simulated bar exam in the top 10%, compared to GPT-3.5’s performance in the bottom 10%. With GPT-5, the upgrades are more subtle but targeted.
Take code generation. On SWE-bench Verified, a real-world benchmark for software engineering tasks, GPT-5 scored 74.9% on first attempts, outperforming Claude Opus 4.1 from Anthropic (74.5%) and Gemini 2.5 Pro from Google DeepMind (59.6%). In healthcare, its error rate on HealthBench Hard Hallucinations is 1.6%, far below GPT-4o’s 12.9%.
In science, GPT-5 Pro achieved 89.4% accuracy on PhD-level science queries, slightly ahead of rivals from xAI and Anthropic. But it lags in other areas, including real-time web navigation tasks. On the Tau-bench airline site navigation test, GPT-5 scored 63.5%, slightly behind OpenAI’s earlier o3 model (64.8%).
Despite these nuanced results, OpenAI insists the model is “safer, smarter, and more useful.” Alex Beutel, the company’s lead on safety research, said GPT-5’s reduced deception rates are essential for building trust.
“It’s more transparent and honest in ways users can trust,” he said, adding that GPT-5 also more reliably filters out harmful queries while reducing false positives, unnecessary content rejections.
From a usability standpoint, GPT-5 also comes with new personalisation features. Users can now select from four built-in personalities, Cynic, Robot, Listener, and Nerd, which adjust the tone and structure of responses. Unlike earlier models, users no longer have to manually tweak settings to get different types of output.
Internally, OpenAI believes GPT-5 represents a shift in how people will use AI, not just to answer questions, but to act more like agents or assistants.
That includes handling schedules, creating research briefs, analysing financial documents, and building apps from scratch. “This idea of software on demand is going to be one of the defining features of the GPT-5 era,” Altman said.
But while the ambition is high, there’s still caution among experts. Some reviewers told Reuters they weren’t convinced GPT-5 is a major leap over GPT-4. Others, like Noah Smith, raised concerns about the financial sustainability of current AI development.
“Business spending on AI has been pretty weak, while consumer spending has been fairly robust because people love to chat with ChatGPT,” he said. “But the consumer spending on AI just isn’t going to be nearly enough to justify all the money that is being spent on AI data centres.”
Altman himself admitted GPT-5 still has limitations, especially around independent learning. It cannot, on its own, acquire new knowledge or skills without user input. And while test-time compute (a method of giving the model more thinking power when needed) helps in solving complex problems, it’s not a substitute for self-directed learning.
Still, the company believes in its innovation. With over 700 million weekly ChatGPT users and increasing partnerships with enterprise customers, GPT-5 may help OpenAI bridge the gap between consumer curiosity and business utility.
