I have spent more than 100 hours testing different models across creative writing, research, analysis, and structured assignments using both ChatGPT Work and Claude Cowork. In this series, I will share what I have learned—and the practices I have adopted—to select the right model for the right task. 💬 As we know, Chat is generally used to ask questions, explore ideas, and work through something one response at a time. 🛠️ Work and Cowork are designed for broader assignments that may involve files, tools, several steps, and a finished deliverable. But there is another important choice underneath both experiences: 🔑 Which model is actually doing the work? That choice can affect the quality, speed, structure, creativity, and cost of the finished result. ❓WHAT IS A MODEL? A model is the AI engine interpreting your instructions and completing the assignment. ChatGPT and Claude are the products. Inside those products, different models may be available. Each model may approach the same assignment differently: - Some models are designed for fast, everyday work. - Others spend more time reasoning before responding. - Some may be better suited to highly structured or analytical assignments. - Others may provide more flexibility and variation when the work is creative. 💡 The most powerful model is not automatically the best model for every assignment. Before deciding which model to use, we first need to understand how to manage the models available to us. 🔍 KNOW WHICH MODEL IS ACTIVE Before beginning an important assignment, take a moment to check which model is selected. Depending on the product, your plan, and any workplace settings, you may be able to choose: • The model • How much reasoning or effort it applies • Whether extended thinking is turned on • A faster or more capable option The choices available to you may not be the same as those available to someone else. They may also change over time as companies update their models, plans, and product settings.

In Part 1 and Part 2, we used 🐠 Dory and 🖖Spock to explain the difference between Chat and (Co)work. That leads to the next logical question: 🤔 Okay, but which Spock do I use—ChatGPT’s Work or Claude’s Cowork? Personally, I use and like both. The honest answer is that ChatGPT Work and Claude Cowork are built around the same core idea: Give the AI a goal, provide the right context, and let it complete the work. Both can research information, analyze files, create documents, build spreadsheets and presentations, use connected tools, and work through assignments involving several steps. The meaningful difference is where each one feels most at home. Claude Cowork: Best When Your Work Lives in Files and Folders Claude Cowork is especially comfortable when your work lives in files and folders. You can connect a folder on your computer and ask Claude to: - Read and compare several files - Edit or create documents - Rename and reorganize files - Build spreadsheets and presentations - Research missing information - Deliver the finished materials for your review It can work directly with the material inside that folder rather than requiring you to open each file, copy the information into a conversation, and direct every step. Claude Cowork is no longer limited to the desktop. Cowork sessions can also run remotely and be accessed through web and mobile as availability rolls out (more on that later). However, its ability to work with selected local folders remains one of its most recognizable strengths. Claude can also use connectors and plugins: A Claude plugin can package skills, connectors, and sub-agents into a reusable workflow. When a direct connector or tool is not available, Claude can use your computer by navigating applications, clicking, and typing. ⚠️ Note: Claude Cowork requires a Pro level subscription. ChatGPT Work: Best When Your Work Lives Across Connected Apps ChatGPT Work is especially comfortable when your work is spread across connected business tools.

In Part 1, we compared Chat to Dory and (Co)work to Spock. Chat feels conversational and creative. (Co)work feels structured and logical. But this is not just a personality difference. It is also a feature difference. 🐠 Chat is primarily where you talk with ChatGPT or Claude. You ask a question. Explore an idea. Refine responses. Chat can search the web, use enabled connectors, analyze uploaded files, and create content. However, you are driving the process one response at a time. 🖖 (Co)work is designed to complete work. You can point it toward a working folder, give it access to relevant tools, and assign a broader task. For example, (Co)work can: - Read multiple files from a connected folder - Create new documents and reports - Update or reorganize existing files - Rename, sort, and categorize documents - Gather information from connected applications - Use a browser or desktop application when required - Complete a task involving several steps - Run recurring scheduled tasks The practical difference is this: Chat may help you write the plan; where (Co)work can open the working files, follow the plan, and produce the deliverable. Imagine that I am working on a book. In Chat, I might say: “Help me develop the central argument for this chapter.” We can brainstorm, challenge ideas, test language, and find the story. In (Co)work, I might say: “Review the chapters in this folder, identify repeated sections, update the chapter outline, and create a revised draft.” One helps me think through the work. The other can work across the actual materials. They are designed for different purposes, that is why they feel different. Chat is optimized around the conversation - scratch pad of ideas. (Co)work is optimized around the assignment - execution of ideas. Of course, the lines continue to overlap. For example, Chat can use tools, and (Co)work can brainstorm. But their centers of gravity are different.

Your feedback is helping shape the future of AI Bits & Pieces, and I appreciate everyone who took the time to vote. Top Three Topics, by votes: 🥇 Introduction to ChatGPT Work / Claude Cowork — 5 votes 🥈 What Is an AI OS (Second Brain)? — 3 votes 🥉 How I Used Obsidian to Create an AI OS — 3 votes We’ll begin creating content around all three topics. 🎁 Giveaway Winner! Congratulations to @Garry Cole, who was randomly selected by AI from all eligible participants! Garry will receive a $25 Amazon eGift Card. ❤️ Winning Charity The winning charity is Winning Futures (@Kristina Marshall)! As promised, AI Bits & Pieces will make a $25 donation to Winning Futures in honor of this giveaway. Thank you again for being part of this community. Congratulations to Garry Cole and Winning Futures! 🎉

Since I started using Claude Cowork more regularly, I noticed something strange: My creative writing seemed to suffer. The ideas were still there. The information was accurate. The structure was often better. But the writing felt more formal, organized, and buttoned up. It did not always sound like me. So I asked Claude Cowork: “Why does my creative writing seem to suffer in Cowork compared with Chat?” The answer helped me realize that I was not necessarily using the wrong AI. I was using the wrong environment for that stage of the work. Chat is Dory. 🐠 It follows ideas wherever they go. It: explores, wanders, changes direction, and occasionally stumbles into something unexpectedly good. That makes Chat especially useful for brainstorming, finding your voice, testing language, and developing an idea before you know exactly where it will end. Cowork is Spock. 🖖 It wants the facts, the files, the instructions, and the desired outcome. It is, more structured, logical, organized, and focused on completing the assignment. In other words: Chat is often better for finding the voice. Cowork is better for turning that voice into finished work. Neither one is better. They are simply better at different stages of the process. My current approach is simple: - Use Chat to discover what I want to say. - Use Cowork to organize, refine, research, and complete the work. The mistake was expecting Spock to think like Dory. In Part 2, I will look at why Chat and Cowork feel so different from a feature and product-design perspective.