Choosing the Right ChatGPT Model: The Ultimate Stob.AI Guide

Why Does Choosing the Right AI Model Matter?

Selecting the optimal ChatGPT model isn’t just a technical decision – it directly impacts your marketing output. Different models excel at different content tasks:

• Long-Form SEO Blog Posts: For in-depth articles, you’ll need a model that can handle large context, maintain coherent structure, and retrieve factual details. The right model will produce well-structured, SEO-optimized blogs with minimal human editing.

• Social Media Content: Crafting catchy tweets, LinkedIn posts, or Instagram captions at scale requires a model that’s fast and creative. Tone and brevity are key here – a model that’s too verbose or formal might miss the mark.

• Email Marketing: From newsletter copy to personalized drip campaigns, email content demands clarity and personalization. A model that follows instructions closely can ensure your emails hit the right tone and include dynamic fields (like customer names or product details) correctly.

At Stob.AI, we’ve seen firsthand that using the appropriate model can make AI-generated content nearly indistinguishable from human work. It can be the difference between a generic-sounding message and one that engages and converts. Now, let’s explore each model and where it shines.

Breaking Down the ChatGPT Models

1. Budget Models (GPT-4.1 Nano, GPT-4o Mini)

Perfect for simple tasks like tagging, short-form social media, or bulk content that doesn't require intricate creativity. For example, GPT-4o Mini is excellent for repurposing long-form content into smaller posts.

2. Standard Models (GPT-4.1 Mini, GPT-4.1, o1 Standard, GPT-4o)

This tier is ideal for scalable SEO blogging, detailed product descriptions, and repeatable content frameworks. At Stob.AI, we predominantly utilize the o1 Standard model due to its reliability in structured content like automated SEO blogs.

3. Premium Models (o3, o1 Pro)

These high-end models are tailored for complex logic tasks, coding automation, and enterprise-level research. Unless you're performing R&D or building sophisticated AI products, these models may exceed your requirements and budget.

Real-World Use Cases and Recommendations

• SEO Blog Automation: o1 Standard – consistent, structured, and efficient.
• Social Media Post Generation: GPT-4o Mini – cost-effective and dynamic for short content.
• Detailed Product Descriptions & Reviews: GPT-4o – engaging, creative, and natural.
• Advanced Coding & Logic Tasks: o3 – precision and power for developers and data scientists.

GPT-4o: The Multimodal Workhorse

GPT-4o (think “GPT-4 OpenAI, version o”) is the flagship ChatGPT model introduced in mid-2024 – essentially an evolution of GPT-4 designed for broader capabilities. It’s the default model in ChatGPT today and a real all-rounder for content tasks. Key features of GPT-4o include:

• Multimodal Mastery: GPT-4o can handle text, images, and even audio inputs/outputs in one model. This means you could feed it an image (say, a product photo) and ask for a description, or generate responses with voice output. For marketers, this opens up creative uses like generating infographic text or video scripts with image references.

• Natural and Fast: It’s optimized for fluid, human-like interactions. GPT-4o responds almost as fast as a human, making it feel very responsive in chat. This speed is useful when you’re using it in live chatbots or iterative content brainstorming.

• Broad Knowledge & Multilingual: With a knowledge cutoff of late 2023 and optional web access, GPT-4o has up-to-date information. It works in 50+ languages, covering 97% of global web users. So if you need a blog post in Spanish or a product description in French, GPT-4o can deliver.

• Strong General Performance: Benchmarks show GPT-4o is slightly smarter than the original GPT-4 on broad knowledge tests. For example, it scored 88.7 on the Massive Multitask Language Understanding (MMLU) exam, compared to 86.5 for GPT-4. In verbal reasoning tasks it greatly outperformed the older GPT-3.5 Turbo model (69% vs ~50% accuracy). In practice, this means GPT-4o does better at understanding instructions, staying coherent, and retrieving facts than previous-gen models.

When to use GPT-4o: For most everyday content tasks, GPT-4o is a reliable go-to. It’s great at copywriting, blog drafting, answering customer inquiries – anything that isn’t extremely specialized or logic-heavy. An internal cheat sheet from an AI marketer community boils it down nicely: if your task is not very complex or logic-intensive, “use GPT-4o for everyday content, casual queries, and productivity tasks”. In a marketing context, GPT-4o is ideal for drafting articles, brainstorming campaign ideas, writing product descriptions, or creating social posts that need a bit of creativity. Its multimodal talent also means you can use it in creative ways – for example, generating a social media image caption and suggesting an image idea in one go.

However, GPT-4o isn’t perfect for everything. Notably, it can struggle with deep logical reasoning or extended code. If your content workflow demands multi-step logic (like analyzing data or writing complex SQL queries) or extremely long outputs, GPT-4o might hit its limits. That’s where other models come in (more on those soon).

Example use case: Suppose you need a 1,500-word SEO blog post about the benefits of AI in marketing. GPT-4o can take your outline and produce a coherent draft with introduction, subheadings, and even meta description suggestions. It will generally maintain a friendly, professional tone suitable for marketing. Because of its large context window (up to 128k tokens in ChatGPT for Plus/Pro users), GPT-4o can incorporate a lot of input material – you could give it an entire content brief or multiple source articles to draw facts from. The result is a solid first draft that likely only needs minor editing for style.

(Pro tip: Internal tests at Stob.AI found that enabling ChatGPT’s “Deep Research” tool with GPT-4o helps produce reports or blogs with citations and up-to-date facts automatically – super useful for credible content pieces)

GPT-4o Mini: Scaling Content on a Budget

As powerful as GPT-4o is, you might not need its full capability for every task – especially if you’re generating thousands of short pieces or operating under tight budgets. GPT-4o Mini is the solution for high-volume, cost-sensitive workload.

GPT-4o Mini is essentially a distilled, smaller version of GPT-4o. It’s optimized for speed and cost-efficiency while still handling text, images, and basic voice tasks. Here’s why GPT-4o Mini is a favorite for scaled marketing operations:

• High Throughput, Lower Cost: The model was designed for mass deployment – think e-commerce sites with AI chatbots chatting to hundreds of users, or an automation generating thousands of product descriptions. Its pricing is dramatically lower than full GPT-4o. One report cites GPT-4o Mini at around $0.15 per 1M input tokens and $0.60 per 1M output tokens (a tiny fraction of GPT-4o’s cost). In practical terms, that means you can generate a million words of content for just a few dollars in output cost. This makes it viable to use AI for large-scale content like e-commerce listings or social media updates without breaking the bank.

• Fast and Lightweight: GPT-4o Mini sacrifices a bit of raw “brainpower” to gain speed. It’s snappier and can handle rapid-fire requests, which is perfect for real-time systems (like a Facebook Messenger bot responding to FAQs) or bulk content generation pipelines. It may not think as deeply, but it responds quickly – a trade-off often worth it for straightforward tasks.

• Typical Use Cases: According to industry analysis, GPT-4o Mini excels in roles like high-traffic chatbots, simple voice assistants, automated image captioning, and internal team Q&A bots. For marketers, imagine a retail chatbot that can answer product questions 24/7, or an AI that generates Instagram captions for a library of product photos. These don’t require complex reasoning, just decent writing and understanding – exactly what GPT-4o Mini provides.

Of course, there are compromises. Because it’s smaller, GPT-4o Mini has weaker complex reasoning and sometimes a less polished tone. In practice you might see it occasionally produce a more generic or off-brand style if not carefully prompted. It’s also limited in the advanced tools/features that the full GPT-4o has – for example, the mini model does not get access to things like code execution or browsing within ChatGPT. Essentially, GPT-4o Mini sticks to the basics: it can read/write text (and images/audio in simple ways), but it won’t do fancy data analysis or intricate logic on its own.

When to use GPT-4o Mini: Use this model for simple, repetitive content tasks at scale. It’s perfect when you need lots of content, fast, and absolute precision isn’t critical. Some scenarios: generating hundreds of variant ad copy snippets, translating product descriptions into multiple languages quickly, powering a FAQ bot that answers common questions, or summarizing batches of articles. Many startups and small businesses choose GPT-4o Mini for initial AI adoption because it delivers quick wins without a huge cost.

Example use case: You run an e-commerce site with thousands of products. By connecting GPT-4o Mini via the API, you can automate the creation of product descriptions. Feed the model basic details (features, specs, etc.) and let it generate a unique description for each item. Given the low cost, you could afford to do this for your entire catalog. While the prose might not be as vivid as GPT-4o’s output, it will be consistent and correctable. You can then have a human quickly review for brand voice. This kind of workflow, heavy lifting by GPT-4o Mini, final touches by a human – is extremely efficient for large-scale content needs.

(Integration tip: Stob.AI’s platform can hook GPT-4o Mini into your CMS via Make.com integration, so product descriptions or social posts get generated and uploaded automatically. This kind of AI-driven content pipeline is a game-changer for resource-stretched teams.)

GPT-4.1 (and GPT-4.1 Mini): The Engineer’s Choice for Precision

One of the newest additions to the OpenAI lineup is GPT-4.1, launched in April 2025, along with its little sibling GPT-4.1 Mini. GPT-4.1 represents a different branch of the ChatGPT family – it’s a model built for developers and enterprise use, emphasizing coding ability, precision, and following complex instructions. Think of GPT-4.1 as the specialist compared to GPT-4o’s generalist.

What makes GPT-4.1 special: OpenAI tailored this model for tasks like software engineering and agent-like behavior. A few highlights:

• Coding and Technical Prowess: GPT-4.1 excels at coding tasks, scoring 55% on a challenging software engineering benchmark (SWE-Bench) – notably higher than GPT-4o’s performance. If your marketing involves coding (for example, generating HTML/CSS for emails, writing Python scripts for data analysis, or creating website code snippets), GPT-4.1 is the model to choose. It’s adept at producing syntactically correct, functional code and can debug or improve code when asked.

• Strict Instruction-Following: This model was tuned to follow complex instructions to the letter. It’s less likely to go off on a tangent or inject extra verbosity. In fact, enterprise users reported GPT-4.1 is about 50% less verbose by default than other models, it gets to the point. For marketers, this is gold when you need content in a very specific format or style. For instance, if you need a product description in JSON format or an email template with placeholder tags, GPT-4.1 will likely adhere more precisely to the required structure.

• Large (Huge) Context Window: Perhaps one of GPT-4.1’s biggest advantages is its ability to handle extremely large inputs. Via the API, GPT-4.1 can process up to 1 million tokens in context (roughly 750,000 words!) This is orders of magnitude larger than most models. In practical terms, you could feed an entire marketing knowledge base or a huge batch of customer feedback to GPT-4.1 in one go. (Currently, the ChatGPT interface doesn’t expose the full million-token capacity – Plus users have 32k, Pro users 128k context in the UI – but via API, that huge window is available for custom workflows.) This capability is ideal for tasks like analyzing long-form content or producing a mega-report that draws on numerous sources. For example, GPT-4.1 could read a 200-page PDF market research report and generate a summary or recommendations out of it, all in one prompt.

• Speed and Cost Improvements: Despite its power, GPT-4.1 was designed to be efficient and accessible for business use. It actually has lower latency (faster responses) than GPT-4o in many cases, and OpenAI priced it about 26% cheaper than GPT-4o for API calls. Moreover, GPT-4.1 comes in variants: the standard model and smaller ones like 4.1 mini (and an even smaller 4.1 nano) that offer huge cost savings (the mini is ~83% cheaper than full 4.1). GPT-4.1 Mini has quickly become the new default model for many users – OpenAI even made it the fallback model for free ChatGPT users once they exhaust their GPT-4o messages. The mini version is faster and “good enough” for a lot of day-to-day tasks, while the full GPT-4.1 is there for heavy-duty uses.

GPT-4.1 vs GPT-4o – what’s the difference? In short, GPT-4.1 is better for structured, logical tasks and GPT-4o is better for open-ended creative tasks. OpenAI themselves noted that GPT-4.1 “excels at coding and instruction following compared to GPT-4o”. It’s the model you pick if you want an AI to act like a reliable analyst or developer in your workflow. On the flip side, GPT-4.1 is not explicitly a reasoning model like the o series (more on those next) – OpenAI stated GPT-4.1 “doesn’t surpass o3 in intelligence” on deep reasoning. So for complex problem-solving or analytical reasoning, you might still use the o-series model. And for highly creative writing (a whimsical ad copy, a poetic tagline), some users prefer GPT-4o or even a model like GPT-4.5 (a creativity-tuned model) because GPT-4.1 might come off a bit too concise or dry.

When to use GPT-4.1: In marketing and content automation, use GPT-4.1 when precision and structure matter most. Some great use cases: generating code snippets for a web project, producing structured reports (e.g. an analytics report in markdown or a JSON data summary), handling long documents (legal contracts, technical whitepapers) to extract insights, or acting as an AI agent that needs to follow multi-step instructions reliably. For example, if you wanted an AI to read your entire CRM database and draft a detailed email campaign plan with segmented strategies, GPT-4.1 could ingest all the data and produce a well-organized plan (where GPT-4o might lose track or get verbose). Additionally, if you integrate AI through tools like Make.com, GPT-4.1 is fantastic for workflow automation that involves conditional logic. It’s less likely to “hallucinate” steps and can be trusted to output machine-readable text that subsequent automation steps can consume.

And what about GPT-4.1 Mini? GPT-4.1 Mini deserves a callout because it’s rapidly becoming one of the most used models in everyday content workflows. As mentioned, GPT-4.1 Mini is now available to everyone (including Free tier) as the default once GPT-4o usage is maxed out. It’s fast, efficient, and surprisingly capable given its size. OpenAI says 4.1 Mini “excels in instruction-following, coding, and overall intelligence” for a small model. In practice, we find GPT-4.1 Mini often outperforms the old GPT-3.5 model on many tasks, while being just as speedy. It has the same 128k context window in ChatGPT, can do text and images, and even voice, but notably it lacks the advanced tool usage of GPT-4o (no browsing or file uploads in the UI for this model). Essentially, GPT-4.1 Mini is a workhorse for everyday content: use it to draft emails, generate social content, or do quick data cleaning in text. If you’re a marketer on the Free plan, you’ll end up using 4.1 Mini frequently due to GPT-4o message limits – and the good news is, it’s up to the task for most standard needs. Consider it the best bang-for-buck model right now, since it’s both high-performing and widely accessible.

Example use case: You want to automate weekly reporting on your content marketing performance. You have Google Analytics data, social media stats, and sales numbers. Using Make.com, you pull all that data and feed it into GPT-4.1 in one prompt (the combined data might be tens of thousands of words). You ask GPT-4.1 to generate a concise report highlighting trends, anomalies, and recommendations, formatted in Markdown. GPT-4.1 will follow these complex instructions and output a nicely structured report (e.g. with bullet points for each channel, tables of key metrics, and a brief analysis) because it’s tuned for exactly this kind of task. Meanwhile, if you only needed a quick summary of one data source, GPT-4.1 Mini could do that on a smaller scale almost instantaneously.

(Fun fact: When GPT-4.1 was first released via API, OpenAI wasn’t sure about adding it to ChatGPT UI. Developer demand changed their mind – they “built it for developers” but users loved its accuracy so much that it got added to ChatGPT Plus. Now marketers can access this dev-grade model without any coding!)

OpenAI o1: The First “Thinking” AI Model

While GPT-4.1 focuses on practical skills, OpenAI’s o-series models are all about deep reasoning. The first of this family, OpenAI o1, marked a major shift when it was previewed in late 2024. Unlike the GPT-4.x models which aim to be fast and versatile, o1 was designed to think longer and harder before responding. For users, that meant responses that might take a bit more time, but could solve more complex problems step-by-step.

Key characteristics of o1:

• Enhanced Reasoning: OpenAI internally referred to o1 as a “reflective” model – it essentially performs a kind of internal chain-of-thought. In plain terms, o1 is better at multi-step logic, complex math, and tasks like debugging reasoning errors. Early tests showed it could tackle math and science problems at a level previous models struggled with. The New York Times even reported OpenAI claimed o1 had PhD-level performance on certain academic tasks at launch. For content creators, this capability translates to more accurate analytical writing – o1 can weigh pros and cons, perform calculations, or outline a complex argument more effectively than GPT-4o.

• Strict Safety & Factuality: Because it “thinks” more, o1 was observed to better follow safety instructions and avoid contradictions in its answers. If you give it a list of rules or a style guide in the prompt, it’s quite meticulous in adhering to them (great for maintaining brand voice or compliance in content). It’s also useful when you need the AI to not be tricked by tricky prompts – e.g. if doing content moderation or analysis, o1 will be less likely to get confused by subtle inputs.

• Use in ChatGPT Pro: When OpenAI fully released o1 in December 2024, they made it part of a higher tier ChatGPT Pro subscription (around $200/month). Pro users got an “o1-pro” model that used more computing power per answer, meaning even deeper reasoning. This indicates that o1 was computationally heavier – it worked best with more CPU/GPU cycles to think through problems. In practical terms, o1 was slower and costlier to run than GPT-4o. It wasn’t intended for rapid-fire content generation, but for those tough nuts that other models couldn’t crack. (In an API setting, you’d only call o1 for high-value tasks that justify the cost.)

When to use OpenAI o1: Today, o1 has largely been superseded by o3 (discussed next). But it’s worth understanding because it laid the groundwork for reasoning models in content workflows. You’d reach for a model like o1 when your task requires strategic thinking or complex analysis. For a marketer, this might mean generating a thorough market research report, where the AI has to interpret data trends, compare competitor strategies, or simulate user reasoning. Another use – creating a detailed project plan with multiple conditional steps: o1 could reason about the sequence of tasks, potential pitfalls, and mitigation strategies. Basically, if you have an AI task where you’d normally break it into multiple prompts or steps, a reasoning model like o1 can often handle it in one go by internally breaking down the problem.

In content terms, o1 could be useful for long-form thought leadership pieces – e.g. “analyze the future of AI in our industry with references to economic theory and recent research.” GPT-4o might produce a decent article by pulling facts, but o1 would more likely produce a cohesive, logically structured analysis, almost like a consultant’s report. The trade-off is time: o1 might take noticeably longer to generate its output as it’s literally doing more reasoning under the hood.

(Note: With o1 being an older model now, you might not directly use it if o3 is available. However, understanding o1 is useful because some current tools or third-party services refer to using “OpenAI’s reasoning mode” which started with o1. For instance, GitHub’s Copilot experimented with o1-preview for better code reasoning. In marketing AI tools, you might see options like “enable reasoning” – essentially invoking an o-series model.)

OpenAI o3: The Logical Powerhouse

Meet OpenAI o3, one of the newest and most advanced models as of 2025 – the undisputed champion of logic, analysis, and problem-solving in the ChatGPT lineup. If GPT-4o is the creative writer and GPT-4.1 the disciplined coder, o3 is the strategist and analyst. OpenAI released o3 alongside a smaller sibling (o4-mini) as the next leap in the o-series.

What sets o3 apart:

• Deep Reasoning & Tool Use: o3 is trained to “think for longer” and solve complex problems in a very structured way. It not only reasons deeply, but it also knows when to use tools. In the ChatGPT interface, o3 has full tool usage enabled – it can autonomously decide to search the web, run Python code, use vision recognition, or other plugins to arrive at the best answer. This is a big deal: o3 can perform multi-step tasks without needing the user to prompt each step. For marketing tasks, this means o3 could, say, fetch real-time data or images as part of generating content. It could look up the latest stats from the web while writing a report, or execute calculations in the middle of an analysis - all by itself.

• Top-Tier Intelligence: OpenAI calls o3 (and its companion o4-mini) “the most intelligent models we have ever released” Indeed, o3 has set new records on academic benchmarks and real-world tasks. It’s the model that currently wins or ranks near the top on things like math competitions, logic puzzles, and comprehensive exams. For example, on a challenging math test (AIME 2025), o3 drastically outperformed o1 when factoring in cost-efficiency. In one internal eval, o3 even strictly improved over o1 on the cost-performance curve – meaning it’s not just smarter, it’s often cheaper per unit of result. In plain terms: if you have a really hard task, o3 will get it right more often than any other model.

• Use Cases – Complex and Technical: o3 is built for specialists. Ideal use cases mentioned include research, development, engineering, data analysis, and other expert domains. In marketing and business, this could translate to AI doing things like: auditing a complex Excel financial model for errors, conducting a SWOT analysis based on lengthy reports, or even writing portions of a technical whitepaper that require domain expertise. If you have a project where accuracy and reasoning are paramount – say, a legal analysis or a scientific content piece – o3 is the model to trust over others.

• Limitations: The very power of o3 comes with a few caveats. It’s slower than lighter models – it takes its time to reason step-by-step. So for long interactive chats or tasks where quick responses are needed, it might feel laggy. Also, OpenAI initially limited o3 usage for users (e.g. Plus users might get ~100 messages per week with o3) because of the computational load. This means you wouldn’t use o3 for every little task – you save it for the big challenges. Cost is higher too (one source lists o3 at around $0.01 per 1K tokens input and $0.04 per 1K output, roughly 10x the smaller models’ cost). So, you apply o3 when it’s worth it.

When to use OpenAI o3: Use o3 for your most complex, critical tasks – the ones where a mistake is costly or the problem is highly complicated. For instance, in a marketing automation scenario, you might use o3 to generate an extensive strategy document: imagine asking it to assess a new market entry strategy, referencing economic indicators, competitor moves, and internal company data. o3 can chew through large context (it also supports very large token windows, comparable to GPT-4.1’s million-token scale) and produce a nuanced, well-reasoned strategy complete with justifications. Another example: using o3 to analyze sentiment from a huge dataset of customer reviews and then automatically formulating an action plan for product improvements. That kind of multi-step reasoning – read data, derive insights, recommend actions – is o3’s playground.

In content creation, o3 might help with quality control too. You could have o3 review a batch of content generated by other models, checking for logical consistency or factual accuracy, since it’s better at spotting subtle errors. It can then refine or correct the content as needed (essentially acting as an AI editor or analyst on top of your AI writers).

It’s worth noting that if your task is overkill for o3, you’re just spending extra time/money for no gain. As one guide put it: if you only need a simple FAQ answer, o3 is “completely oversized and expensive” – a GPT-4o Mini or o4-mini would be better. Always match the model to the task complexity.

Example use case: A B2B company wants a comprehensive annual report on industry trends to use as lead-gen content. They have a trove of data: market research reports, internal survey results, financial statements, expert interview transcripts, etc. By feeding these into o3 (perhaps via an automated pipeline), the model can produce a draft of a polished, insight-rich report that weaves together all the information. It might highlight correlations (e.g. “Company data shows X, which aligns with broader industry trend Y”), perform calculations or charts via its tool use, and even suggest strategic recommendations. The final output reads like a high-end consulting analysis. This is the kind of heavy lift o3 was made for – something you’d hesitate to attempt with other models for fear of factual errors or shallow reasoning.

(Reality check: For most day-to-day marketing content like blog posts or social media, you won’t need o3. But it’s a game-changer to have it in your toolkit for those special projects. At Stob.AI, we often run o3 in our Make.com workflows as a background “AI analyst” that triggers only on complex tasks – ensuring we get top-quality analysis when we truly need it, while lighter models handle the routine content.)

Choosing the Right Model for Your Workflow

With an understanding of the current models – GPT-4o for general use, GPT-4.1 for structured tasks, the mini versions for volume work, and o3 for advanced reasoning – how do you decide which model to use in your marketing workflow? Here are some guidelines to summarize:

• For Everyday Content & Creativity: Use GPT-4o. It’s versatile and produces high-quality, engaging text for things like blog posts, social content, and customer interactions. If the task is open-ended or requires a bit of creative flair (but not deep logic), GPT-4o will likely give the best results quickly. Example: drafting an blog or ad copy, where a friendly tone and good flow matter more than rigorous logic.

• For High-Volume or Real-Time Tasks: Use GPT-4o Mini or GPT-4.1 Mini. When speed and scale trump nuance, the mini models shine. They’re perfect for automating large batches of content, powering chatbots that get heavy traffic, or any scenario where you need to maximize output per dollar. Example: generating personalized product recommendations text for 10,000 customers – a mini model can do this fast and cheap.

• For Structured Outputs & Integration: Use GPT-4.1 (full or mini). When your content needs to follow a format or you’re integrating AI into a larger system (e.g., pulling data via API, needing JSON or code outputs), GPT-4.1’s reliability is invaluable. It reduces errors and keeps things succinct. Example: automatically generating weekly marketing reports with charts – GPT-4.1 will stick to the template and even handle code (via tools) to produce charts if needed.

• For Complex Analysis & Critical Thinking: Use OpenAI o3. Reserve it for tasks that resemble a top-tier analyst or strategist’s work. It can synthesize information and reason in ways other models can’t. Example: performing a deep competitive analysis or writing a technical whitepaper with zero factual errors – o3’s your go-to.

• When in Doubt – Hybrid Approach: Often, the best solution is to combine models. For instance, you might use GPT-4o to draft a base blog post (fast and creative), then have o3 review and fact-check it for logical consistency. Or use GPT-4.1 to generate a data-heavy outline, then GPT-4o to expand it with a more engaging narrative. With tools like Make.com, you can chain these steps seamlessly: one model’s output can feed into another in an automated workflow. This way, you leverage each model’s strength.

Finally, always keep an eye on OpenAI’s updates. The landscape is evolving quickly (GPT-4.5 and o4-mini have emerged focusing on creative writing and ultra-fast reasoning respectively). The good news is that as a Make partner, Stob.AI ensures our clients stay at the cutting edge – we integrate new models as they become available and advise on the best choice for each use case. (For example, if OpenAI releases an even more creative model or a new specialized model, we’ll update our recommendations accordingly.)

Bottom line: The “best” ChatGPT model depends on the job to be done. By understanding each model’s niche, you can dramatically improve your marketing automation workflows – from content creation to analysis. The right model will produce better content faster, and often at lower cost, than a one-size-fits-all approach.

As AI capabilities grow, savvy marketers will treat model selection as a new form of craft. Much like a photographer chooses different lenses, you’ll choose different AI models for different projects. And with platforms like Stob.AI’s Content Engine (integrated with Make.com for workflow automation), you can orchestrate these models to work together – letting each do what it does best. Here’s to working smarter, not harder, with the power of the latest ChatGPT models at your fingertips.

How Stob.AI Can Help You Choose & Implement

At Stob.AI, we've designed an intelligent content marketing automation system that leverages the ideal model for your unique needs. Whether you're scaling your content marketing, automating product descriptions, or enhancing your customer engagement, our AI automation services can help you achieve remarkable efficiency.

Need help picking the right model or building your automated content system? Contact our team to explore your options.