Stop Asking ‘Claude or ChatGPT’

In recent months, if you asked me, “Which AI is worth using long-term?” my answer would likely be very direct: Claude.

This isn’t because it excels in every capability, but because in real work scenarios, it resembles a stable, reliable partner willing to collaborate long-term.

When you integrate context, folders, workflows, and connectors, Claude becomes more than just a chat interface; it evolves into a system that continuously handles your tasks.

Many people have naturally formed the judgment that choosing one AI is sufficient, and that choice has often been Claude.

However, recent changes have stirred the situation. This isn’t because Claude has suddenly faltered, but because ChatGPT has recently addressed several critical shortcomings in high-frequency scenarios:

• Images
• Search
• Coding
• Writing
• Even Google Sheets

This isn’t a single-point update; it’s a series of capabilities that have suddenly come together.

This leads to a realistic conclusion: Relying on one AI to solve all problems is becoming increasingly unrealistic.

The Fantasy to Abandon

Many people crave a clean answer, a single tab, or a standard solution that can be reported to a boss and easily procured by a team. I completely understand this mindset. Businesses want simplicity, and individuals do too. No one wants to reassess their tool stack every couple of months.

The problem now is that the speed of model iteration has reached a point where it almost doesn’t allow for complacency. Previously, you could say, “I’ll choose the strongest one and look later.” Now it feels more like, “Different models are pulling ahead in different work segments; if you insist on choosing just one, the costs will become increasingly apparent.”

Thus, the most important takeaway from this article isn’t to declare a winner but to convey that the more practical approach moving forward is not to choose a champion but to establish a division of labor.

Recommended AI Division of Labor

If you prefer to skip the analysis and want an actionable version, here’s my recommendation:

• Claude for daily operational tasks
• ChatGPT for images, search, and spreadsheets
• Gemini for non-English tasks
• Gamma for presentations

This isn’t a combination that will always be correct, but at this current moment, it is practical, realistic, and easy to implement.

Note that I emphasize “division of labor,” not “ranking.” Once you shift to this perspective, many dilemmas will vanish, as you no longer force yourself to answer the wrong question: “Who is the strongest?” Instead, you only need to answer a more useful question: “Which AI should handle this task?”

Why Claude Remains the Strongest Operational Brain

If your core work involves:

• Writing emails
• Drafting reports
• Creating contracts
• Composing copy
• Writing memos
• Continuously advancing complex tasks based on context

I would still place Claude at the forefront. The reason is straightforward: context management.

Many believe that the secret to effectively using AI lies in prompts. However, the quality is determined not by how elaborate your writing is but by whether the model has sufficient, stable, and long-term context.

Claude’s consistent performance in work scenarios is primarily due to its compatibility with your file system, folders, long-term data, and connectors. You can prepare a comprehensive “work environment” for it:

• Who you are
• What your company does
• Your writing style
• Team skills
• Historical outputs
• Relevant project documents

This way, it doesn’t start from scratch guessing who you are and what you want to do; it first understands the environment before beginning work. This leads to a fundamental difference: you are not repeatedly prompting a model; you are training a system that can continuously take over tasks.

This is why I still prefer to place Claude in the primary position for pure work writing and collaboration.

ChatGPT’s Real Comeback: Not Just Chatting but Three High-Frequency Scenarios

When we say, “ChatGPT is back,” that statement can feel vague. What truly deserves clarification is what it has returned with. In my view, the three most significant aspects are:

1. Images

The most noticeable change is in image generation. ChatGPT has evolved from merely being able to generate images to approaching a genuine design capability that can integrate into workflows.

Its strength lies not just in producing attractive single images but in the following simultaneous capabilities:

• Stronger text control
• More stable style understanding
• Usability in multilingual design
• Ability to create infographics, brand drafts, posters, educational pages, and mockups
• Outputs that resemble a clearly directed draft system rather than just AI-generated images

This signifies a substantial leap from being a “toy” to becoming a “production tool.” If your work frequently involves:

• Social media visuals
• Brand drafts
• Educational illustrations
• Infographics
• Mockups
• Posters
• Covers

It’s hard to ignore it now.

2. Search

Another significant change is in search capabilities. Many previously opted for a separate search AI or jumped between different products. However, ChatGPT has increasingly become a system that can genuinely function as a research assistant.

It doesn’t just provide links. The improvement lies in its ability to assist in completing a comprehensive research task:

• First, it finds current answers
• Then, it informs you of recent changes
• Next, it lists the best sources
• It points out uncertainties
• Finally, it offers next steps

This is what truly usable search looks like. If your work often involves:

• Industry research
• Competitive analysis
• Tracking recent developments
• Data collection
• Quickly forming judgments

Then ChatGPT is worth reintroducing to your main toolset.

3. Google Sheets

This might be the most easily underestimated yet most broadly applicable improvement. In the past, there was a frustrating gap between AI and spreadsheets: you could have the model help you think of a structure or even export a spreadsheet file, but once you entered Google Sheets, it felt like the power was cut off.

Now, this issue is beginning to be resolved. Once AI can directly engage with spreadsheet workflows, everything changes. This means you can finally let it assist you in real business scenarios with:

• Task planning
• Data organization
• Workbook creation
• Dashboard structure design
• Formula linking
• Logic checking
• Bulk research and filling

These tasks are precisely what many middle managers, operations, analysts, marketers, and project managers encounter daily. If images and search feel more like “wow factors,” spreadsheets represent something that will genuinely permeate weekly work.

Gemini Still Has Its Place, But Not Everyone Needs It

Many Chinese users might overlook an important point: the ranking of models in the English-speaking world doesn’t necessarily represent your real experience in a local language environment. Especially when the tasks you need to handle are not in English but in languages like Chinese, French, Hebrew, Arabic, Japanese, Korean, or Thai, the differences can be significant.

For small languages and non-English tasks, many models don’t have problems of “incorrectness” but rather of sounding “unnatural.” They read like a hard translation of English thought. The sentence structure may be correct, but the nuance is off. The logic may flow, but it lacks the natural expression of a local speaker.

Therefore, if your core work isn’t in English, you really can’t just look at the rankings. The most realistic method of judgment is simple: test it with your actual tasks.

In this regard, Gemini still deserves to retain a position. Not because it is the best for everyone, but because it indeed feels more natural in many language tasks.

Don’t Overestimate AI for Presentations, but Don’t Underestimate Existing Products Either

Another type of task worth discussing separately is presentations and slides. Many people immediately think, “Since AI is so strong now, can it create a complete deck from scratch for me?” Theoretically, yes. However, in practice, most people don’t need a “god-level PPT” generated entirely automatically from zero. Instead, they require:

• First, help clarifying the content structure
• Then, create a decent-looking product
• Finally, allow for quick edits

In this case, tools like Gamma, which specialize in presentations, remain more suitable. The best process isn’t to forcefully create everything in Gamma. Instead, it’s to first use ChatGPT for research, structuring, and material formation, and then hand that content over to Gamma to generate the presentation. This type of “model + specialized tool” combination is often more practical than insisting on a single platform to do it all.