“Wait, would anyone pay $200 per month when you can pay $20 — or even use ChatGPT for free?” That’s the first reaction we had when we heard about ChatGPT Pro and its price tag.
Two hundred dollars is a lot for a single AI tool, especially when alternatives like Anthropic’s Claude are available for much less. What could possibly justify a 10x cost?
We’ve tested the most advanced features available to Pro users — including OpenAI’s latest o3 model — to help answer this question. We’ve fed it books’ worth of data, used it for everything from analyzing sales call transcripts to technical SEO audits, and compared it to Claude 3.7 Sonnet.
Surprisingly, we’re finding more cases where ChatGPT Pro justifies its price. Thanks to recent improvements, it’s becoming worthwhile for an ever-growing number of people.
Note: We’ll keep testing new features as they’re released and update this article accordingly. Last updated: May 1st, 2025.
The Breakneck Pace of AI Progress
Just a few months ago, Claude’s Sonnet model still edged out OpenAI’s lineup when it came to creativity. But with the release of GPT-4o and 4.1, we now prefer those OpenAI models for writing tasks, while o3 stands out for creative and strategic thinking.
The previous flagship for ChatGPT Pro was o1 pro: a model that could “reason,” spending several minutes thinking through your query and exploring multiple solution paths before replying.
o3 builds on that with a new level of agentic autonomy. It picks its own tools for each step and passes results along between them. In a single response, o3 might:
- Browse the web for fresh statistics or competitor pages.
- Run Python to clean a CSV, generate a chart, or scrape a table.
- Inspect an uploaded image or PDF, zooming and cropping to focus on details.
- Generate or edit images (static or animated GIFs) using the 4o image engine.
- Store findings in Project files or Memory for follow-up questions.
This combination of advanced reasoning and tool use unlocks workflows that simply weren’t possible before.
What to Use ChatGPT Pro For
With the latest upgrades, ChatGPT Pro has shifted from a specialized tool into a true generalist. It feels more like an all-purpose assistant than ever before, especially for content marketers and knowledge workers.
Deep Research
Deep Research — launched in early 2025 and now powered by o3 — is one of ChatGPT Pro’s standout features for marketers. Acting like a research analyst, it can sift through hundreds of sources, synthesize findings, and even ask clarifying questions before delivering a report.
The output quality is impressive. In our hands-on testing, Deep Research delivered reports on par with what you’d expect from a capable junior analyst or research assistant, even when tackling multi-part research questions or large, complex tasks.
For content marketers, it’s especially valuable for:
- Competitor analysis.
- Building audience personas.
- Researching industry trends and expert opinions.
- Developing detailed content briefs or doing interview preparations.
- Enriching or informing strategy.
As with any generative AI, hallucinations can occur, but in practice, we’ve found Deep Research to be reliably accurate.
Not-So-In-Depth Research (AKA Mini Deep Research With o3)
A standard Deep Research query can run anywhere from 10 to 30 minutes as it combs the web for sources and analyzes all the information it finds.
With o3, this approach is now built in: on any prompt that calls for a web search, o3 may spend several minutes working through sources until it finds exactly what it needs. It’s like having a mini version of Deep Research running in the background on almost every query.
This makes o3’s answers noticeably more accurate and practical. It also opens up new use cases. For example, we’ve tasked the AI with finding references for a design brief. Instead of simply generating a shortlist of possible options, it delivered a handpicked set of highly relevant examples, each one directly matching the requirements of the brief.
Another useful upgrade is that o3 now gives inline source attributions for any information it pulls from the web, making it much easier to verify its findings.
Processing Large Documents and Datasets
The o3 model comes with a 128K-token context window — about the length of a 150-page book. This lets it handle hours of transcripts, massive CSVs, and other dense, data-heavy materials with ease.
| Prompt (≈ words*) | Max response (≈ words*) | What you can comfortably do |
|---|---|---|
| ≈ 15 000 | ≈ 81 000 | Feed a short white-paper or research deck and have the model draft a full-length report or book chapter. |
| ≈ 30 000 | ≈ 66 000 | Hold a day-long “research assistant” chat, with plenty of room each turn for detailed drafts, code, or marketing copy. |
| ≈ 60 000 | ≈ 36 000 | Drop in a mid-size codebase and get back architecture diagrams, migration scripts, or a detailed refactor plan. |
| ≈ 75 000 | ≈ 21 000 | Paste eight hours of meeting transcripts and receive a multi-section synthesis with action items, decisions, and follow-ups. |
| ≈ 90 000 | ≈ 6 000 | Upload an entire 300-page nonfiction book and get a chapter-by-chapter summary plus a brief critique. |
o3 is also far better than previous models at “holding it together” when you throw lots of information at it (the technical term is “long-context retention”). These features make ChatGPT Pro exceptionally effective for managing huge amounts of data. We’ve found it can:
- Identify and organize insights from dozens of interview transcripts (we built our season 1 podcast AI playbook this way).
- Plow through hours of meeting notes to extract actions, insights, and feedback for every attendee.
- Review many drafts or code versions in a single session without getting confused.
- Reformat or extract information from very large CSVs.
- Proofread and copy edit long-form content, including newsletters, articles, or eBooks, all to a specific style guide.
None of these tasks are extremely complex, but other models struggle as the data piles up, mostly because their limited output windows get clogged.
Planning, Advising, and Guiding
Thanks to its advanced reasoning abilities, o3 excels as a planner, advisor, and coach. We’ve used it to review briefs, strategies, and drafts. It surfaced flaws, insights, and actionable improvements you’d expect from an expert.
Here some examples where those capabilities have made a difference for us:
- Surfacing weak spots, redundancies, and missing evidence in a messy blog draft, then reorganizing it section by section.
- Turning a four-minute, rambling voice memo into a punchy LinkedIn outline with hook, stake, CTA, and alternative metaphors.
- Converting a table of 14 potential podcast guests and scheduling constraints into a phased calendar and prep checklist.
- Running a campaign pre-mortem: reviewing a promo plan and budget sheet, it flagged distribution bottlenecks (like PR freelancer capacity) and suggested practical fixes.
Special mention: vibe coding
ChatGPT Pro is a fantastic “vibe coding” partner. Specialized coding tools like Cursor, Lovable, Windsurf, and Replit have their own integrated AI chat features, but ChatGPT Pro with o3 is especially helpful as an external CTO or coding consultant when you get stuck.
o3 outperforms o1 pro for coding work, and the Pro subscription means you won’t bump into usage caps. (Generating code can burn through Plus tier credits surprisingly quickly.)
See Code Is Now Content: How We Built Our SEO Calculator With AI for a detailed walkthrough of how we used this approach to “vibe code” our SEO forecasting tool.
Sales Discovery Calls → Decks
We’ve always created tailored proposals for potential customers after first sales discovery calls. In 2024, we brought Perplexity and Claude into this process and saw 10–20% time savings, mainly by speeding up research and drafting.
At the start of this year, the o1 pro model surprised us when we ran a sales call transcript through it. The AI essentially drafted a complete strategy for the customer with a nuanced analysis of their requirements, including “unspoken” client needs it inferred from the call.
Just four months later, o3 is even more capable than o1 at both analyzing customer needs and outlining proposals. Even more impressively, it can now also update the actual slides of your deck.
The formatting isn’t perfect yet, but given these features didn’t exist just three months ago, it’s clear where this is headed: ChatGPT is on its way to becoming a fully capable AI agent for knowledge worker tasks, nearly start to finish.
Content Audits
Sara Coggin, Associate Head of Content at Animalz, originally tested o1 pro on a range of technical SEO audit tasks, including internal link analysis and generating a link graph visualization.
She found the model could walk her through complex processes that would typically require specialized SEO expertise: “I’m now confident I can do technical tasks like analyzing the link structure of a website in Python in less than one hour, compared to not being able to do this at all before.”
At the time, quite some manual work was still necessary because o1 pro couldn’t handle file attachments. That limitation is gone with the latest models (o3 and o4-mini), which dramatically speeds things up: Tim was able to replicate the hardest part of Sara’s audit in just ten minutes.
Podcast Interview Preparation
ChatGPT Pro is now a core part of our workflow as we prepare for new Animalz podcast seasons and interviews.
True to our approach of always beginning with human input (see Stay Strong: Never Let AI Fill Your Blank Page), we start by outlining our show goals and topic ideas. Only then do we hand off the background research: ChatGPT’s Deep Research takes in our goals and topic ideas, then works for 15–30 minutes to return a detailed report on our guest.
We run that output through a sequence of o3 and GPT-4o prompts to surface the most engaging interview questions.
Where o1 pro’s suggestions often felt stiff or overly formal, the 4o-powered questions generally feel more natural and sometimes even witty, like “How do you keep everyone aligned without spending your life in status meetings?” Occasionally, though, they become overly dramatic (“think org charts, cross-functional ‘games of thrones,’ and massive editorial calendars that hinge on a single stakeholder”) and need some toning down.
Remember Everything
Previously, ChatGPT’s memory feature felt clunky. You had to actively nurture it, and the results were hit or miss. Now, though, ChatGPT quietly scans your past chats and automatically surfaces relevant details whenever you need them.
The feature is still early, but if you’re paying attention, it already changes the feel of using ChatGPT day-to-day, for example:
- You don’t have to keep reminding the AI who you are or where you work.
- Key details and ideas from earlier meetings come up naturally in new chats.
- It quickly adapts to your writing style.
- Need a meeting agenda or a recap of outstanding action items? Just ask ChatGPT to pull from your recent discussions.
All of this is just the beginning, and the potential is enormous. OpenAI CEO Sam Altman, in a recent chat with Lex Fridman, described a not-too-distant future where models have “trillions and trillions” of context length, essentially able to recall anything you’ve ever written, read, or stored.
Generate Amazing Images With 4o + o3
You’ve likely seen countless eye-catching images created with the new 4o image generation model in your social feeds. (If not, check out this link o3 found for us. The article is packed with examples and offers a practical guide on prompting the new model.)
Besides pretty pictures, ChatGPT’s image generation can actually solve problems that matter for content teams. For example:
- You can add or swap in text.
- It nails precise tweaks, like moving an object, fixing alignment, or updating colors to match your brand’s hex codes.
- The model can interpret detailed brand guidelines and stick to them (especially when you loop in o3 for planning).
- Working with o3 turns image brainstorming into a genuinely collaborative process. The suggestions are tailored and creative.
The main image in this article is a good example. We sketched out what we wanted with o3, iterated on the concept, and let the 4o image engine handle the details. The result: a visual that captures the essence of the refresh and fits with our new visual direction.
Drawbacks You Should Know About
o1 pro had its quirks, some of them hard to justify for a $200 product (especially the inability to attach most file types). o3 has fixed most of these, though it has introduced a few new issues, too.
| Issue ✅ = solved/no problem ⚠️ = partly improved ❌ = problem | o1 Pro | o3 |
|---|---|---|
| Attach text / PDF / CSV files | ❌ | ✅ |
| Verbosity | ❌ | ✅ |
| “Plays advisor” instead of deciding | ❌ | ⚠️ |
| Slow responses on big jobs | ❌ | ⚠️ |
| Data-privacy* | ✅ | ✅ |
| Sora video quality is weak | ❌ | ❌ |
| Higher hallucination rate | ✅ | ❌ |
| Tight usage caps for Plus & Team plans | ✅ | ❌ |
| Context-window instability at 80-100k tokens | ✅ | ⚠️ |
| Instruction-following drifts on very long prompts** | ✅ | ❌ |
| “Jagged frontier” (big swings in output quality)** | ✅ | ❌ |
* We got this wrong in a previous version of this article: ChatGPT includes a toggle that lets you prevent your data from being used for model training.
** While we haven’t run into these much in our own use, lots of users online have flagged them as ongoing issues.
There’s one other “drawback” that’s really a plus: OpenAI has made the $20/month Plus tier more generous, so you don’t have to jump straight to Pro. Try Plus first and see what it can do, then upgrade if you really need the extra bandwidth.
| Category | Free | Plus ($20 / mo) | Pro ($200 / mo) |
|---|---|---|---|
| Core models | GPT-4o-mini (unlimited) + GPT-4o (limited) | GPT-4o (≈ 80 msgs / 3 h) GPT-4 (≈ 40 msgs / 3 h) o3-mini / o1 | All reasoning models, GPT-4o, o1, o1-mini — no hard cap |
| pro mode | — | — | ✅ exclusive |
| Context window | 8 K tokens | 32 K tokens | 128 K tokens |
| Deep Research | Lightweight only (5 tasks /mo) | Full + lightweight (25 tasks /mo combined) | Full + lightweight (250 tasks /mo) |
| File uploads & Data analysis | Limited | Extended | Extended |
| Vision & Image generation | Limited | Unlimited | Unlimited |
| Sora video generation | — | Limited | Extended |
| Custom GPTs | Use only | Create + share | Create + share |
| Memory (long-term) | Basic | Expanded | Expanded |
| Privacy | Content may be used for training (opt-out) | Same opt-out | Same opt-out |
Should You Pay $200 per Month for ChatGPT Pro?
Our previous answer was “ChatGPT Pro is not for everyone,” and that’s still true. Previously, forking out the $200 made sense if you had some very specific needs and use cases, and could put up with Pro’s quite annoying limitations.
Now, the decision is a lot more straightforward:
- Most content marketers will get a lot of value from o3 and 4o’s capabilities.
- The biggest headaches from o1 pro are gone, and the new quirks are less of a dealbreaker.
- You can access almost all of Pro’s core features with a Plus subscription, just with much tighter usage caps (fewer Deep Researches, stricter o3 limits).
So start with ChatGPT Plus. If you regularly run into its limits and you’re already getting real value, Pro will probably justify its $200 a month price tag.
We’ll keep testing new features as they’re released and update this article accordingly. Last updated: May 1st, 2025.