ChatGPT vs Claude vs Gemini: stop choosing, give each a role
TL;DR
Endless "ChatGPT vs Claude vs Gemini" comparisons miss the point: each model has different strengths, so the best move is often to use all three, each in the role it suits. Put one model on strategy, one on building, one on critique, in a shared room with one memory, and let a human decide. You get the strengths of each without betting your whole workflow on a single model.
Search "ChatGPT vs Claude vs Gemini" and you get a thousand articles trying to crown a winner. The premise is the trap. For real project work you rarely have to choose one model, and choosing one means inheriting all of its blind spots.
The models are good at different things
Anyone who uses all three notices the pattern: one is a strong reasoner and planner, another writes and builds cleanly, another is fast and good at broad synthesis. The rankings shift with every release, so betting your entire workflow on today's "best" model is a bet that ages badly.
Roles beat rankings
Instead of asking which model is best, ask which model is best for which job:
- Strategist: frame the problem and weigh the tradeoffs.
- Maker: produce the actual artifact, the draft, the code, the plan.
- Critic: red-team the work and find what breaks before reality does.
Assign a different model to each role and the comparison question dissolves. You are no longer choosing; you are casting.
Why this needs a shared room
Casting roles only works if the members can actually see each other's work. Three separate tabs cannot do that. They need one shared thread and one shared memory, so the Critic is critiquing the Maker's real output and everyone is working from the same project state. A human stays the decision-maker and makes the final call.
Stop choosing
The "versus" framing sells comparisons; it does not get your work done. Put ChatGPT, Claude and Gemini in one room, give each the role it is best at, and let them cross-check each other. That is the whole idea behind a Bordet boardroom: mix the best models, give each a role, and keep one memory across the project.
See it for yourself
Open a multi-LLM boardroom and put your AI team to work.