Artificial intelligence is no longer a competition in which one chatbot does everything better than every other chatbot. The major AI systems are increasingly developing different strengths.
Anthropic’s Claude is a particularly interesting example. Created by Anthropic, Claude is an AI assistant designed for conversation, reasoning, analysis, writing, coding and working with large amounts of information. Anthropic describes Claude as a system built to help people understand difficult material, solve complex problems and turn ideas into useful projects. (Anthropic)
The important question, therefore, isn’t simply “Is Claude better than ChatGPT?”
A better question is:
What kinds of work bring out Claude’s particular strengths—and how should you use it to get the best results?
That distinction is important because Claude and ChatGPT overlap considerably, but they can feel quite different when you use them for substantial projects.
What exactly is Claude AI?
Claude is a family of large language models developed by Anthropic.
Like ChatGPT, Claude can understand natural-language instructions and generate responses based on what you give it. You can ask it questions, have conversations with it, provide documents, analyse information, write and rewrite material, generate code and work through complicated problems.
Claude is particularly oriented toward working with information and producing substantial pieces of work rather than simply answering isolated questions.
Anthropic currently positions Claude around several broad capabilities:
- Writing and editing
- Reasoning
- Research
- Coding
- Document analysis
- Data analysis
- Learning and explanation
- Creative work
- Building interactive applications and tools
Claude can also work with uploaded documents and images, while its Projects and Artifacts features are designed to make longer-running work more organised and reusable. (Anthropic)
In other words, you can think of Claude less as a question-and-answer machine and more as a collaborative workspace for complex intellectual work.
Where Claude really shines
There are several areas where Claude can be particularly impressive.
1. Working with very large amounts of information
One of Claude’s most significant strengths is its ability to work with extremely large contexts.
Current Claude models support context windows of up to 1 million tokens, depending on the model and plan. Anthropic says this can be used for large documents, extensive codebases and long conversations. (Claude Help Center)
That is potentially enormous.
It means you can give Claude a substantial amount of source material and ask it to reason across that material rather than repeatedly feeding it small pieces.
For example, imagine you have:
- a 400-page book
- 100 pages of research
- interview transcripts
- notes
- character profiles
- previous drafts
- a style guide
Instead of treating each document as a completely separate conversation, you can build a project around the material and ask Claude to work with the information collectively.
This is where Claude becomes particularly interesting for authors, researchers, programmers, consultants, students and business users.
2. Long-form writing and editing
Claude has a particularly strong reputation as a writing partner.
This doesn’t simply mean:
“Write me an article.”
The more interesting use is giving Claude your existing material and asking it to work as an editor.
For example:
“Here are the first five chapters of my novel. Study the author’s voice, sentence rhythm, dialogue style and characterisation. Don’t rewrite anything yet. First identify the characteristics of the writing style.”
Then you can continue:
“Now edit Chapter 6 so that it is consistent with the established voice. Don’t change the plot or character motivations.”
This is a much more sophisticated use of AI.
Claude’s ability to maintain a large amount of contextual information can be particularly useful here.
Anthropic’s Projects feature allows users to create dedicated workspaces containing documents, knowledge and project-specific instructions. (Claude Help Center)
3. Understanding and analysing documents
Claude can be extremely useful when the problem isn’t creating information, but making sense of information you already have.
You might give it:
- a lengthy report
- a contract
- a business plan
- research papers
- meeting transcripts
- interview notes
- technical documentation
- a collection of articles
- a spreadsheet
- source code
Then ask it to identify patterns, contradictions, weaknesses, important themes or actionable conclusions.
This is often much more useful than simply asking:
“Summarise this document.”
A better prompt might be:
“Analyse this document as if you were a critical reviewer. Identify its five strongest arguments, five weaknesses, unsupported claims, contradictions and information that is missing. Then tell me what I should investigate further.”
Now you’re using Claude as an analytical partner, not merely a summarisation tool.
4. Coding and technical work
Claude is also particularly strong at programming.
Anthropic explicitly positions Claude around code generation, debugging, code analysis and optimisation. (Claude Platform Docs)
For a developer, one of the most powerful approaches is to give Claude the existing system, rather than asking it to write an isolated piece of code.
For example:
“Here is my application’s architecture, database schema and relevant source files. Understand how the system works first. Then identify why users are experiencing this error.”
That gives the AI the surrounding context necessary to reason about the problem.
Claude can then become useful as something closer to a technical collaborator.
5. Creating things with Artifacts
One of Claude’s particularly distinctive features is Artifacts.
Instead of simply giving you text inside the conversation, Claude can create substantial standalone outputs in a separate workspace.
Artifacts can include things such as:
- documents
- websites
- diagrams
- flowcharts
- code
- SVG graphics
- interactive React components
- visualisations
- tools and small applications
You can then ask Claude to modify the artifact and iterate on it. (Claude Help Center)
This changes the experience considerably.
Instead of:
You: “Give me the code for a calculator.”
You can move toward:
You: “Build me a working calculator with this functionality.”
Claude can create the artifact, you inspect it, and then you tell it what needs changing.
That is much closer to building something together.
Claude vs ChatGPT: what’s the difference?
This is where things become more interesting.
OpenAI’s ChatGPT and Claude are both highly capable general-purpose AI assistants. There is substantial overlap between them.
ChatGPT can write, reason, analyse documents, work with images, browse the web, analyse data, generate images, work with files, use Canvas and conduct deep research. (OpenAI Help Center)
So it would be misleading to say:
“Claude does X while ChatGPT cannot.”
The reality is much more nuanced.
The difference is often how the systems are designed and where their workflows are particularly strong.
Claude tends to be especially attractive for:
Large bodies of text and context
Claude’s current models can provide very large context windows, including up to 1 million tokens on supported models. (Claude Help Center)
Long-form writing
Claude can be excellent when you want to develop, edit and maintain consistency across substantial amounts of prose.
Document-heavy analysis
If your work involves feeding an AI a large quantity of source material, Claude can be particularly useful.
Coding
Claude has become a serious tool for software development, particularly when the AI needs to understand a substantial existing codebase.
Artifacts
Claude’s ability to turn conversations into standalone interactive creations is one of its distinctive strengths. (Claude Help Center)
ChatGPT tends to be particularly attractive when you need:
A broad ecosystem of capabilities
ChatGPT combines conversation with web search, deep research, image understanding, image generation, data analysis, file handling, Canvas, Projects, memory and other tools, depending on the user’s plan and configuration. (OpenAI Help Center)
Research involving the live web
ChatGPT’s Deep Research is specifically designed for multi-step research across online sources and produces cited reports. (OpenAI)
Multimodal work
ChatGPT can work across text, images and other modalities, including image generation and visual analysis. (OpenAI Help Center)
A broad general-purpose assistant
If you want one AI that can move rapidly between writing, research, images, data, coding, brainstorming and other tasks, ChatGPT’s breadth can be a major advantage.
Don’t think of Claude and ChatGPT as enemies
One of the biggest mistakes people make is choosing one AI and then trying to force it to do everything.
A more productive approach is to treat AI models like different specialists in your digital toolbox.
You might use ChatGPT to:
Research → analyse current information → generate images → brainstorm → develop strategy
Then use Claude to:
Absorb a large body of material → analyse it → structure it → edit it → turn it into polished long-form output
And then potentially bring the result back into ChatGPT for another stage of the workflow.
The smartest AI users aren’t necessarily those who have found the one best AI.
They are the people who understand which AI is best suited to which stage of the job.
How to get the best out of Claude
The biggest mistake you can make with Claude is treating it like Google.
Don’t simply type:
“Write an article about artificial intelligence.”
Give it context, a role, an objective, constraints and a definition of what success looks like.
A powerful Claude prompt can follow this structure:
1. Give it the role
“Act as an experienced technology journalist.”
2. Give it the context
“I am writing for readers who understand basic AI but don’t understand the differences between the major AI assistants.”
3. Give it the objective
“Explain the practical differences between Claude and ChatGPT.”
4. Give it your requirements
“Use practical examples. Avoid technical jargon. Don’t make unsupported claims.”
5. Define the desired output
“Create a 2,000-word article with a strong introduction, clear headings, comparison section and practical conclusion.”
This produces a much better result than:
“Write an article about Claude.”
Give Claude the material it needs
This is perhaps the most important principle.
The quality of an AI’s answer is heavily influenced by the quality and relevance of the context you give it.
If you’re working on a serious project, don’t make Claude guess.
Give it:
- previous drafts
- research
- notes
- examples
- style guides
- relevant documents
- your objectives
- constraints
- examples of what you like
- examples of what you don’t like
Claude Projects are specifically designed for this kind of workflow. You can provide project knowledge and instructions so that conversations within the project have the appropriate context. (Claude Help Center)
Use Claude as a collaborator, not a vending machine
This is another major difference between basic and advanced AI use.
Don’t expect to get the perfect answer from one prompt.
Instead, build a conversation.
For example:
Step 1
“Analyse this material. Don’t write anything yet.”
Step 2
“Identify the most important themes.”
Step 3
“Now identify contradictions and weaknesses.”
Step 4
“Develop three possible approaches.”
Step 5
“I prefer option two. Develop the structure.”
Step 6
“Now write the first section.”
Step 7
“Critique what you have written.”
Step 8
“Rewrite it based on your critique.”
This approach transforms Claude from an answer generator into an iterative thinking partner.
A practical example: using Claude to improve a novel
Let’s take a real-world example.
Imagine you’re writing a psychological thriller.
You have:
- 500 pages of manuscript
- 30 character profiles
- a plot outline
- research notes
- previous chapters
- a timeline
- descriptions of important locations
Instead of opening a new chat and asking:
“Improve Chapter 20.”
you could create a Claude Project for the novel.
Upload your relevant material and establish project instructions such as:
You are my developmental editor for a psychological thriller. Your job is to help me improve suspense, pacing, character psychology, dialogue and narrative tension. Preserve my distinctive writing voice. Do not introduce plot changes without explaining them first. When editing, distinguish between grammatical corrections, stylistic improvements and substantive changes.
Now Claude has a much better understanding of its job.
Then give it a specific problem
Suppose Chapter 20 feels flat.
Instead of saying:
“Make Chapter 20 better.”
Try:
Analyse Chapter 20 against the preceding chapters. Identify where the tension drops, whether the characters behave consistently with their established motivations, whether the dialogue feels natural, and whether the chapter advances the central mystery. Do not rewrite it yet. Give me a detailed editorial assessment.
Claude can now analyse the chapter in context.
You might discover that the real problem isn’t the writing.
Perhaps the protagonist learns something important too early.
Or perhaps the antagonist becomes predictable.
Or perhaps the chapter repeats information the reader already knows.
That’s a much more valuable discovery.
Then make Claude rewrite selectively
Once you’ve identified the problem, you can say:
Rewrite the scene beginning with the restaurant confrontation. Increase psychological tension without adding violence. Keep the characters’ established personalities and preserve the key plot information. Make the dialogue sharper and more natural. Don’t make the scene melodramatic.
Now Claude has:
Context + objective + constraints + source material.
That’s where AI becomes dramatically more useful.
Claude can also critique its own work
Another powerful technique is to separate creation from evaluation.
For example:
“Now act as a ruthless literary editor. Review the version you just produced. Identify anything that feels clichéd, predictable, overwritten or inconsistent with the characters. Then provide specific recommendations.”
Then:
“Apply those recommendations and produce the revised version.”
This creates a miniature editorial loop:
Create → Critique → Improve → Critique → Refine
That is considerably more powerful than repeatedly saying:
“Make it better.”
Another excellent use: turning information into a product
Claude’s Artifacts make another workflow particularly interesting.
Suppose you have researched a subject and want to create a simple interactive resource.
You could tell Claude:
“Using the research contained in this project, create an interactive one-page guide. It should contain the key concepts, a visual flowchart, practical examples and a short self-assessment. Make it clean and professional.”
Claude can create an artifact that you can inspect and iterate on rather than simply returning another block of text. (Claude Help Center)
You can then say:
“Make the navigation simpler.”
“Add a progress indicator.”
“Make the explanations suitable for beginners.”
“Turn the final section into an interactive checklist.”
This is where Claude starts to feel less like a chatbot and more like a creative development environment.
The secret: give Claude a big problem, not a tiny question
Claude becomes especially interesting when the task has layers.
For example:
Weak use
“Give me five blog post ideas.”
Better use
“Analyse these 50 existing articles, identify gaps in the subject coverage, determine which topics appear overused, and suggest 20 new topics that address meaningful gaps.”
The second task gives Claude something to reason about.
Weak use
“Summarise my book.”
Better use
“Analyse the entire manuscript and identify the central themes, character arcs, major turning points, unresolved plot threads, contradictions and potential pacing problems. Then create a detailed editorial report.”
Again, you’re asking Claude to think across information, not merely generate text.
The Claude mindset
If you want to get the most out of Claude, adopt this mindset:
Don’t ask:
“What can Claude write for me?”
Ask:
“What complicated piece of work can Claude help me think through?”
That’s a much more powerful question.
Claude’s strengths become particularly valuable when you give it a substantial body of information and ask it to understand, analyse, organise, challenge, transform and build upon that information.
Its large context capabilities, Projects and Artifacts make this workflow particularly compelling. (Claude Help Center)
Claude and ChatGPT can complement each other
Ultimately, the Claude-versus-ChatGPT debate is somewhat misleading.
Both are extremely capable AI systems, and both are continually evolving.
ChatGPT has developed into a broad AI platform encompassing research, web search, multimodal analysis, image generation, data analysis, Canvas, Projects and other tools. (OpenAI Help Center)
Claude, meanwhile, is particularly compelling when you want to work deeply with information, substantial documents, long-running projects, code and polished standalone outputs.
And the most productive solution may be to use both.
Think of it this way:
ChatGPT can be your broad AI productivity platform. Claude can be your deep-work partner.
That doesn’t mean Claude will always produce the better answer, or that ChatGPT will always be better for everything else. AI models change rapidly, and capabilities increasingly overlap.
But understanding their different strengths lets you stop asking:
“Which AI is the best?”
and start asking the much more useful question:
“Which AI is best for the job I am doing right now?”
That is where the real productivity advantage begins.
