Custom GPTs vs. Claude Projects: Which One Does Your Firm Actually Need?
A practical comparison for professional services firms — what each tool does well and when to use it.
Both Custom GPTs and Claude Projects solve the same basic problem: giving AI persistent context so your team doesn't have to re-enter instructions every conversation. You configure a persona, upload reference documents, set behavioral constraints - and anyone who opens the tool inherits that setup automatically.
Beyond that, they diverge. They live in different ecosystems, have different strengths, and suit different kinds of work. If you're choosing between them, the decision is less about which AI is "better" and more about what your firm is actually trying to build - and where your team already spends time.
The basics
What each tool actually does
A Custom GPT is a ChatGPT configuration you build inside your OpenAI account. You give it a name, a system prompt, optional uploaded documents, and optional integrations - web browsing, image generation, or connections to external APIs via GPT Actions. Once built, it lives in ChatGPT and can be shared with your team by link. Anyone with access opens it and starts working with no setup on their end.
The configuration layer is substantial. You can define what the GPT will and won't do, set a specific voice and persona, and restrict it to certain tasks. GPT Actions also let you connect to external data sources - a meaningful advantage if you need the tool to pull from live systems.
A Claude Project is Anthropic's version of the same concept. You create a project, write a system prompt that applies to every conversation inside it, and upload documents into a shared knowledge base. Every conversation in the project inherits those settings.
The notable difference: Projects maintain conversation history across sessions. You can return to a thread from two weeks ago and continue it - the uploaded files and project context are still there. Setup is simpler too. There's no builder interface or API configuration to navigate; you write a system prompt and upload your files, and it's ready to use.
Where GPTs tend to win
When Custom GPTs are the better fit
If your firm is already on ChatGPT and your team is comfortable with the interface, there's real value in staying in that ecosystem. Custom GPTs are easy to share at scale - a link goes out, people click it, and they're in. No project invitations or account migrations.
External integrations
GPT Actions let you connect to live databases, internal tools, or third-party APIs. If your use case involves pulling from a system outside the AI platform itself, Custom GPTs have significantly more surface area for that kind of build.
Simpler, repeatable tasks
For standardized output types - intake forms, meeting note summaries, quick research briefs - Custom GPTs are fast to deploy and easy for non-technical team members to maintain.
Team already in ChatGPT
Adoption is a real constraint. A tool people already log into every day will get used more than a better-configured tool they have to switch to. If ChatGPT is your team's default, Custom GPTs are the path of least resistance.
Where Projects tend to win
When Claude Projects are the better fit
For work that requires sustained context across sessions, Projects hold up better. If you're working through a long engagement, a multi-week research process, or anything where the model needs to carry forward nuance across many conversations, the persistent history matters.
Complex drafting and analysis
For professional services tasks that require judgment - drafting client memos, synthesizing research, working through legal or financial analysis - Claude tends to produce output that needs less post-edit cleanup. That difference compounds over many uses.
Sustained, multi-session work
Conversation history that persists across sessions is a practical advantage for long engagements. You can return to a thread, reference what was decided earlier, and continue without rebuilding context from scratch.
Fast setup, no technical overhead
There's no builder UI, no API configuration, and no integration layer to manage. Write a system prompt, upload your files, share the project link. For firms that want something working quickly without a technical person involved, that matters.
The real question
What actually decides it
Most firms frame this as a capability question: which AI is better? For the majority of professional services work, both will produce usable output if the configuration is solid. The thing that actually decides it is where your team already spends time.
A well-configured Custom GPT that your team opens every day will produce more value than a more capable Claude Project that they have to remember to switch to. Adoption is a real constraint, and it depends on workflow fit more than on feature comparisons.
If your team is split - some on ChatGPT, some on Claude - that's worth addressing separately. Running two different AI ecosystems without a deliberate reason is a coordination cost that tends to grow.
For professional services
Where most firms actually are
Firms we talk to are usually in one of two places.
Some are still doing everything ad hoc - no shared context, no standard configurations, everyone prompting from scratch every time. For these firms, the tool choice is secondary. The first step is establishing the basics: a shared prompt library, reusable context blocks for common deliverable types, a shared sense of what good output looks like. Either platform can support that. Start with the one your team is already using.
Others have gotten past the basics and want to build something that scales - a client intake workflow, a research process multiple people run the same way, a set of standard templates any team member can execute consistently. At that point, the specific capabilities of each platform start to matter more, and a closer look is warranted.
Either way, the tool configuration is only as good as the prompt inside it. A Custom GPT or a Claude Project with a vague system prompt produces inconsistent output. The work is in getting the prompts right first - role, context, task, format, constraints - and then wrapping them in a tool that makes them easy for your team to reach.
Most firms don't need to make the perfect tool choice up front. They need to pick one, configure it well, and watch whether their team actually uses it. That feedback tells you more than any comparison article will.