logo
ChatGPT models explained: How to use each, according to OpenAI

ChatGPT models explained: How to use each, according to OpenAI

Digital Trends07-05-2025

Table of Contents
Table of Contents Why are there six models in the first place? GPT-4o GPT-4.5 OpenAI o4-mini OpenAI o4-mini-high OpenAI o3 OpenAI o1 pro mode How to use different ChatGPT models
Although the entire AI boom was triggered by just one ChatGPT model, a lot has changed since 2022. New models have been released, old models have been replaced, updates roll out and roll back again when they go wrong — the world of LLMs is pretty busy. At the moment, we have six OpenAI LLMs to choose from and, as both users and Sam Altman are aware, their names are completely useless.
Most people have probably just been using the newest model they can get their hands on, but it turns out that each of the six current models is good at different things — and OpenAI has finally decided to tell us which model to use for which tasks.
Recommended Videos
Why are there six models in the first place?
LLMs are unpredictable — users never know what kind of responses they will get, and the developers don't really know either. Sure, it might be more convenient if we had all of the capabilities available rolled up into one model, but that isn't as easy as it sounds.
As OpenAI tweaks its models, some things get better and other things get worse — and sometimes unexpected side effects occur. There's no telling how long it would take to balance things out perfectly, so it makes more sense to just release new versions even when improvements are only focused on a few areas.
The results of this approach are the six main models we have right now: GPT-4o, GPT-4.5, OpenAI o4-mini, OpenAI o4-mini-high, OpenAI o3, and OpenAI o1 pro mode. And I'm just going to say it again — these names really are useless. OpenAI may have given us a document explaining what each one does now, but that doesn't mean you'll be able to remember which name matches which capabilities — so consider saving this little cheat sheet from the document if you need to remember.
Part of the latest 4o family of models, GPT-4o 'excels at everyday tasks.' This includes:
Brainstorming
Summarizing
Email writing/checking
Creative content
You can search the web with it, generate images, use advanced voice features, analyze data, and create custom GPTs. You can also upload various file types to aid your prompts.
According to OpenAI's own research, however, 4o does have a bit of a hallucination problem. It's not the worst of the bunch, but it did hallucinate around twice as much as o1 during testing.
This can be problematic if you're using it to search the web or learn new things — the trickiest aspect of hallucinations is that they often sound entirely plausible, making it harder to just 'check when something sounds off.' Instead, the only way to be sure is to check just about everything that you don't already know to be true.
According to OpenAI, GPT-4.5's strong suit is emotional intelligence. This means it should be good at helping you communicate with other people, with official recommendations including:
Social media posts
Product descriptions
Customer apology letter
With other strengths such as clear communication and creativity, GPT-4.5 is better equipped to help you find the perfect tone or phrasing for specific situations — and make sure everything still sounds human.
OpenAI o4-mini
One of the more terribly named models, o4-mini drops the 'GPT' element of the naming scheme and awkwardly swaps the 4o around to o4. It's a smaller model, which means it's not stuffed to the brim with as much random internet information as a full-sized model.
The upside of this is that it's quick and less expensive to run, and the downside is that the model has less 'world knowledge' and is prone to hallucinating to make up for that.
Instead of asking it questions about the world, OpenAI recommends using o4-mini for fast technical tasks. Examples include:
Extracting key data from a CSV file
Generating quick summaries of articles
Checking or fixing errors in small code blocks
OpenAI o4-mini-high
Here's another terrible name when viewed in isolation, but fairly easy to understand if you already know what OpenAI o4-mini is. It's still a small model, but it's a step up from the normal o4-mini because it 'thinks longer for higher accuracy.'
This makes it better at more detailed coding tasks, math, and scientific explanations. Here are OpenAI's examples:
Solving complex math equations with explanations
Drafting SQL queries for data extraction
Explaining scientific concepts in simple terms
OpenAI o3
This is technically an older model (because it doesn't have a '4'), but because the o4/4o family didn't make improvements in every area, it's still very relevant. o3 is particularly good at complex, multi-step tasks — the kind of projects that need to be done in multiple stages with multiple prompts.
This includes strategic planning, detailed analyses, extensive coding, advanced math, science, and visual reasoning. If you want to start a task that you know will take a multiple-prompt session to finish, using o3 will help minimize the chances of the model losing track of the context or hallucinating halfway through.
OpenAI suggests use cases like:
Developing a risk analysis
Drafting a business strategy based on data
Running multi-step data analysis tasks
OpenAI o1 pro mode
OpenAI o1 is now considered a 'legacy model,' though it isn't even a year old yet. The 'pro mode' version is tuned for complex reasoning — which means it takes more time to think, but in return gives better thought-out responses.
o1 also gets the best scores on OpenAI's PersonQA evaluation, which measures the rate of hallucination. During testing, o1 hallucinates around half as much as o3 and three times less than smaller models like 04-mini. If you're a big ChatGPT user and your sessions tend to run long, then minimizing the rate of hallucinations could save you a decent chunk of time in the long run.
Here are OpenAI's examples:
Drafting detailed risk analyses
Generating a multi-page research summary
Creating an algorithm for financial forecasting
How to use different ChatGPT models
Unfortunately, you can only access GPT-4o and GPT-4o mini on OpenAI's free tier. If you're a Plus, Pro, Team, or Enterprise user, you can use the model selector to choose which model you want to use.
ChatGPT is also integrated into various other third-party products, both free and paid, so it's worth checking which models different products use. For example, my paid search engine, Kagi, gives me access to multiple OpenAI models. There are also lots of other AI aggregate services out there that give you access to multiple models from OpenAI and other companies for a more affordable price than subscribing to each company separately.
While this information about the different models is useful to have, it doesn't affect everyone. If you mostly use ChatGPT to generate images, search the web, and send general queries, then the default GPT-4o is totally fine. It's only if you're into programming, math, science, or particularly large projects that you might want to think about which model is best for the job.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

Zilliz Transforms Video Surveillance Industry with AI-Powered Vector Database Solutions
Zilliz Transforms Video Surveillance Industry with AI-Powered Vector Database Solutions

Associated Press

time34 minutes ago

  • Associated Press

Zilliz Transforms Video Surveillance Industry with AI-Powered Vector Database Solutions

Leading surveillance providers leverage Zilliz Cloud to overcome data overload challenges, enabling real-time threat detection and sub-second video search across enterprise installations. REDWOOD SHORES, Calif., June 15, 2025 /PRNewswire/ -- Zilliz, creator of the world's most widely adopted open-source vector database, Milvus, today announced significant adoption of its vector database solutions by video surveillance providers addressing critical industry challenges. As the global video surveillance market, valued at over $62 billion and growing rapidly, evolves from passive monitoring tools to intelligent, proactive security solutions, organizations using Zilliz Cloud and Milvus report breakthrough capabilities in overcoming data overload, eliminating manual monitoring inefficiencies, and achieving real-time threat detection. Traditional video surveillance systems struggle with data overload from underutilized footage, time-consuming manual monitoring processes, and data silos that prevent cross-domain intelligence integration. Surveillance platforms implementing Zilliz Cloud vector databases are solving these persistent challenges by transforming vast amounts of video data into actionable intelligence through vector embeddings that represent visual context and meaning. 'The video surveillance industry is undergoing a massive transformation from reactive monitoring to proactive intelligence,' said Charles Xie, CEO of Zilliz. 'Traditional surveillance systems create bottlenecks with manual processes prone to human error and data overload that limits effectiveness. Our customers are building AI-native security solutions that can process vast amounts of visual data in real-time, detect anomalies instantly, and provide actionable insights that were previously impossible to achieve.' Major Surveillance Platforms Report Breakthrough Performance Leading cloud-based surveillance providers report transformative results after transitioning from traditional databases to Zilliz Cloud's vector search technology. A major multi-tenant surveillance platform serving businesses from retailers with thousands of locations to small enterprises managing just a few sites achieved significant improvements in video retrieval performance. The platform now delivers sub-second search capabilities across months of footage using natural language queries such as 'person in red jacket near loading dock' or 'vehicle parked longer than 30 minutes.' 'Vector databases have solved the fundamental challenge of extracting meaningful insights from massive video datasets,' said a technology executive at the surveillance provider. 'We can now provide our customers with instant, accurate search results that would have taken hours of manual review with traditional systems.' Organizations implementing these AI-powered capabilities report security teams can now detect threats and respond up to 50% faster than traditional monitoring methods, while automated anomaly detection significantly reduces false positives and operational costs by eliminating extensive manual monitoring requirements. Enterprise customers across retail operations, industrial safety, transportation infrastructure, and smart city initiatives are deploying these capabilities to achieve advanced loss prevention, automated safety monitoring, passenger flow analysis, and privacy-compliant public safety tracking. Zilliz Cloud Delivers Enterprise-Scale Performance Zilliz Cloud's distributed architecture addresses enterprise-scale video processing demands through several key capabilities. The platform processes billions of video vectors with sub-100ms latency while auto-scaling to accommodate traffic spikes and varying workloads. Built-in security, encryption, and compliance features protect sensitive video data, while multi-tenant support enables surveillance providers to serve diverse customer bases efficiently. The vector database approach converts video footage into high-dimensional mathematical representations, enabling sophisticated AI analysis and semantic understanding that supports both real-time monitoring and forensic investigation. This technology eliminates traditional surveillance limitations by breaking down data silos and integrating video insights with other operational systems. Industry Adoption Accelerates with Responsible AI Focus As AI becomes more embedded in surveillance systems, Zilliz emphasizes responsible development practices aligned with privacy regulations such as GDPR. This US-based company's solutions maintain transparency and security standards while designing AI enhancement to augment human capabilities rather than replace security professionals, enabling teams to focus on complex decision-making while automation handles routine monitoring tasks. The integration of AI and vector databases positions surveillance systems for continued evolution, including edge computing implementations, AI-powered video summarization, and autonomous surveillance capabilities that will further improve security operations while reducing operational costs. Solutions Available for Enterprise Implementation Organizations implementing AI-powered video surveillance can learn more about Zilliz Cloud at or contact sales for deployment consultations. About Zilliz Zilliz is a US-based global leader building next-generation vector database technologies, helping organizations unlock the value of unstructured data and rapidly develop AI and machine learning applications. By simplifying complex data infrastructure, Zilliz brings the power of AI within reach for enterprises, teams, and individual developers alike. Zilliz offers a fully managed, multi-cloud vector database service powered by open-source Milvus, supporting major cloud platforms such as AWS, GCP, and Azure, and is available across more than 20 countries and regions. Headquartered in Redwood Shores, California, United States, Zilliz is backed by leading investors including Aramco's Prosperity7 Ventures, Temasek's Pavilion Capital, Hillhouse Capital, 5Y Capital, Yunqi Partners, Trustbridge Partners, and others. View original content: SOURCE zilliz

VEPO Solutions Appoints Kayla Schultz as Vice President of Sales
VEPO Solutions Appoints Kayla Schultz as Vice President of Sales

Associated Press

time34 minutes ago

  • Associated Press

VEPO Solutions Appoints Kayla Schultz as Vice President of Sales

VEPO Solutions appoints Kayla Schultz as Vice President of Sales to lead national growth, bringing 10+ years of utility tech sales experience and a customer-focused approach to expand VEPO's software and smart metering services across the U.S. New York, NY, United States, June 15, 2025 -- VEPO Solutions, a leading provider of utility software solutions and smart metering installation services, proudly announces the appointment of Kayla Schultz as Vice President of Sales. With a strong background in sales and a customer-first approach, Kayla will lead VEPO's national sales strategy as the company continues its rapid growth across the utility sector. Kayla brings over 10 years of experience in sales strategy, client engagement, and team development within the utility technology and infrastructure industries. 'We are excited to welcome Kayla to VEPO's leadership team,' said Alan Seiler, Managing Partner of VEPO Solutions. 'Her deep understanding of utility operations, combined with a proven ability to lead high-impact sales teams, makes her an excellent fit. Kayla's leadership will play a key role as we continue expanding our innovative software platform and installation services to support utilities nationwide.' In her new role, Kayla will oversee VEPO's sales initiatives across all markets, focusing on growing utility partnerships, strengthening customer retention, and advancing adoption of VEPO's solutions. These include VEPO's cloud-based work order and asset management software, as well as turnkey smart metering installation services. She will also collaborate closely with internal teams to ensure VEPO's sales strategies align with the company's mission to deliver operational efficiency, compliance support, and measurable value to utilities and municipalities. 'I'm honored to join VEPO Solutions at such an exciting time in its evolution,' said Schultz. 'VEPO is not only modernizing how utilities manage infrastructure and service delivery—it's doing so with a clear focus on customer outcomes and long-term success. I'm excited to lead our sales efforts and support the outstanding work our team is doing across the country.' For more information, please visit Contact Info: Name: Media Relations Email: Send Email Organization: VEPO Solutions Website: Release ID: 89162344 Should any problems, inaccuracies, or doubts arise from the content contained within this press release, we kindly request that you inform us immediately by contacting [email protected] (it is important to note that this email is the authorized channel for such matters, sending multiple emails to multiple addresses does not necessarily help expedite your request). Our dedicated team will promptly address your concerns within 8 hours, taking necessary steps to rectify identified issues or assist with the removal process. Providing accurate and dependable information is at the core of our commitment to our readers.

Self-driving cars: Fewer accidents may not lead to cheaper insurance
Self-driving cars: Fewer accidents may not lead to cheaper insurance

Yahoo

timean hour ago

  • Yahoo

Self-driving cars: Fewer accidents may not lead to cheaper insurance

Autonomous vehicles could eventually lead to fewer car accidents and shake up the $400 billion U.S. auto insurance industry, but don't bank on lower premiums anytime soon. That's according to a recent Goldman Sachs research note cited by Bloomberg, which suggests the types of risks insurers cover in the future may shift rather than disappear. 'Autonomy has the potential to significantly reduce accident frequency longer-term and reshape the underlying claim cost distribution and legal liability for accidents,' Goldman Sachs analyst Mark Delaney and colleagues wrote in a client note, per Bloomberg. Goldman analysts predict insurance costs will decrease by more than 50 percent in the next 15 years, from around $0.50 per mile in 2025 to $0.23 in 2040. However, they still expect modest growth in auto insurance premiums for at least the next 10 to 15 years, Bloomberg reported. Part of that is because newer tech-heavy cars have pushed up repair expenses, leading to higher costs per claim. 'Even a minor fender bender is very expensive for many vehicles today,' Mark Friedlander, spokesman at the Insurance Information Institute, an industry trade group, told NewsNation. Auto insurance giant Progressive warns on its website that self-driving cars aren't likely to lower insurance rates and could even drive up costs due to expensive repairs. Self-driving cars may also pose new risks for insurers, such as cybersecurity threats, which could increase the need for cyber coverage, both Friedlander and the Goldman analysts noted. Ajit Jain, Berkshire Hathaway's insurance head, also sees major shifts ahead and recently said he expects the car insurance business to 'change dramatically' once self-driving cars become a reality. 'Most of the insurance that is sold and bought revolves around operator errors and how often they happen, how severe they are, and therefore what premium we are to charge,' Jain said at the company's annual meeting in May. If autonomous vehicles prove to be safer — and involved in fewer accidents — traditional auto insurance may become less necessary and could be replaced by product liability, Jain said. Liability for an accident becomes especially complex when a computer is behind the wheel, and it remains a central question in an ongoing debate. Who pays for damage caused by technology rather than human error? Is the carmaker or the software company responsible? And what if the accident results from a cyberbreach? These questions are still being hashed out. For now, autonomous vehicle regulations vary from state to state, though clearer federal standards may be coming soon. Widespread adoption of fully autonomous vehicles may still be years away, but progress is being made, as evidenced by Waymo's expansion of its robotaxi service to new cities. Earlier this week, tech billionaire Elon Musk said Tesla tentatively plans to start offering rides in its self-driving robotaxis in Austin, Texas, on June 22. 'We are being super paranoid about safety, so the date could shift,' Musk wrote Tuesday on social platform X. As for the future of car insurance, Friedlander cautioned against looking too far ahead and highlighted immediate challenges driving up insurance rates for consumers — namely, higher parts and labor costs. 'In the short term, we're looking at impacts of tariffs, which could significantly increase the cost of auto insurance,' Friedlander said. The latest Consumer Price Index, released Wednesday, showed that the cost of motor vehicle insurance rose 7 percent over the past year. Since the COVID-19 pandemic, those prices have jumped nearly 60 percent, according to a Bankrate analysis of CPI data. Copyright 2025 Nexstar Media, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store