How to Use ChatGPT, Claude & Gemini in One Place

If you want ChatGPT, Claude and Gemini in one place, you are really choosing an aggregation layer. The goal is not merely seeing three model names in a menu. The useful question is whether the workspace preserves context, instructions, files and organization as you switch.

Option 1: Magai

Magai’s current model directory includes model families from OpenAI, Anthropic and Google alongside many others. Its differentiator is model switching inside a conversation plus reusable personas, files, folders, integrations and team workspaces.

This is the best fit when you want to hand the same project between model families without rebuilding the prompt and context every time.

Option 2: Poe

Poe provides models and bots from many AI companies in one interface. It also supports user-created bots, group chat and app/bot creation. Poe is especially attractive when you want breadth and experimentation rather than a tightly managed business workflow.

Option 3: Perplexity Pro

Perplexity Pro gives access to top AI models inside a research-first product. That can be ideal when model choice is secondary to the job of finding, synthesizing and citing information.

Option 4: Keep the native apps

There is a legitimate case for keeping ChatGPT, Claude and Gemini separate: each provider controls its own native features, rollout timing and account experience. If you depend on provider-specific tools, a third-party workspace can introduce compromises.

The workflow test

Before choosing an aggregator, run the same real project through this checklist:

1. Upload or attach the core project files.
2. Set the reusable instructions once.
3. Ask one model for the first draft.
4. Switch model families and request a critique without restating the project.
5. Switch again and request a final synthesis.
6. Check whether sources, files and instructions still behave as expected.

If that handoff works cleanly, the workspace is solving something real. If you still copy/paste context every time, “all models in one place” is mostly a billing convenience.

What a good aggregator should preserve

When you evaluate a multi-model workspace, test more than whether the model names appear in a selector. The useful layer should preserve the parts of a project that are expensive to reconstruct: system-style instructions, uploaded reference material, prior decisions, conversation history and the organizational structure around the work.

Also check how the platform measures usage. Access to a premium model can be technically included while still consuming a large share of a plan’s allowance. A workflow that feels inexpensive during light testing can look different once you run long contexts, image generation, video generation or premium reasoning models every day.

Five questions to ask before migrating

• Can I move my existing conversations or project files into the workspace?
• Do reusable instructions work across the model families I care about?
• Can I see or estimate how much each model consumes?
• Will my team share the same project context, or only the same account?
• Which native-provider features would I still need to keep separately?

Those answers determine whether the platform truly consolidates your AI stack or merely adds another subscription to it.

How to avoid paying for duplicate access

Start with the smallest number of paid products that preserve your must-have native features. One common structure is one native subscription plus one aggregator, not three native subscriptions plus an aggregator.

Use our subscription break-even framework before canceling anything.

Best choice by priority

• Context continuity and repeatable work: Magai.
• Bot exploration and variety: Poe.
• Research and citations: Perplexity.
• Provider-specific native features: keep the native apps.