How to Use OpenAI and Claude Together on the Same Project

The short answer

You can't literally connect the ChatGPT app to the Claude.ai app — they are separate consumer products that don't talk to each other. What you can do is use OpenAI's models and Anthropic's Claude models on the same project, either by working in both tools manually or through a multi-model workspace that calls each provider's API, with the workspace providing relevant project context to each model call so teammates can work from a common project context.

People do this because the models genuinely differ: one may untangle a thorny analysis more reliably while the other writes cleaner prose or follows long instructions more faithfully. Running the same question past both — or giving each a different role on one project — surfaces disagreements a single model would quietly hide.

First, what you are actually combining

The language around this is sloppy, so let's get precise — precision here saves you billing surprises:

• ChatGPT is OpenAI's consumer app (web, mobile, desktop). It has its own subscription billing, memory, and interface. It cannot call Claude.
• Claude.ai is Anthropic's consumer app. Same story in reverse.
• The OpenAI API and the Anthropic API are the developer interfaces to the underlying models (each provider's current models — names and pricing change often, so check the provider's pricing page before budgeting). Third-party workspaces call these APIs with your API keys — see BYOK AI.

When this article says "use OpenAI and Claude together," it means: one project, both providers' models, accessed through APIs inside one workspace — never the two consumer apps wired together. (For the full app-vs-API split, see ChatGPT vs. the OpenAI API and Claude vs. the Anthropic API.)

Why people pair OpenAI and Claude models

• Complementary strengths. Practitioners commonly route heavy reasoning, tool-heavy analysis, or structured output to one provider's flagship and long-form writing, editing, or instruction-following nuance to the other's. Which is which shifts with every model release — treat this as an empirical question, tested on your own tasks, not a permanent ranking.
• Disagreement as a feature. Ask both models the same hard question. Where they agree, your confidence rises; where they diverge, you've found the exact point that needs human judgment. A single model never shows you its uncertainty this clearly.
• Redundancy. Providers have outages, rate limits, and price changes. If a project is set up to run on either provider's models, you can switch a teammate to the other provider's model when one of them has a problem — a manual step, not automatic failover.
• Cost tuning. Route routine subtasks (summarization, extraction) to the cheaper model and reserve the flagship for reasoning that justifies its price. With per-provider API billing, you see exactly what each leg costs.

The copy-paste workflow (and what it costs)

This is how most people do it today, and it's worth describing honestly because it works — just expensively:

1. You work in one tool until you want a second opinion or a different strength.
2. You copy the output, switch tools, and paste it in.
3. You restate the context — the goal, the constraints, what's already been decided — because the second tool starts cold.
4. You compare the answers, decide, and carry the result back to the first tool.
5. Repeat for every round. You are the project's memory, its router, and its version control.

The hidden costs:

• Context decay. Every hop, you abbreviate. Details that don't survive the paste — the caveat in paragraph three, the constraint you mentioned once — silently vanish from the second model's reasoning.
• No shared history. Neither tool sees the full conversation. Each one reasons from a fragment, so "the same project" exists only in your head.
• Comparison is manual. Diffing two answers, reconciling contradictions, and deciding what to keep is skilled work — and it's all on you, every round.
• Double billing, zero synergy. You pay both providers and get two isolated sessions instead of one team.

A better pattern: shared project context

The alternative keeps the two-model idea but removes you as the glue. In a multi-model workspace:

• Teammates are persistent, models are interchangeable. You define e.g. "Mara the analyst" once — role, instructions, style — and assign her a Claude model; "Dev the researcher" gets an OpenAI model. If a new release changes which model is best for a role, you swap the model without necessarily redefining the teammate from scratch (see interchangeable AI models) — though capabilities, tone, and reasoning may still differ between models in the same role.
• One project context, assembled by the workspace. ModelCrew can provide relevant project context and conversation information to different model calls so that teammates can work from a common project context — the brief, the documents, prior decisions — so nothing gets lost in a paste. Provider models remain separate: the workspace assembles the context, context-window limits still apply, and different models may interpret the same context differently.
• A Project Lead orchestrates. You give the goal; the lead delegates to the right teammate, reviews the outputs, and synthesizes one answer — including an explicit reconciliation when the two models disagree (see AI agent orchestration).

The comparison work you used to do by hand — diffing, reconciling, deciding — becomes the lead's synthesis step, with you at the final checkpoint instead of the copy buffer.

Example: one project, both models

Goal: a defensible pricing recommendation for a new B2B product tier. Setup: a project with a Project Lead, an analyst teammate on a Claude model (strong at nuanced reasoning through trade-offs), and a researcher teammate on an OpenAI model (strong at structured data gathering).

1. You brief the lead once: target segment, constraints, the decision you need to defend, deadline.
2. The lead delegates in parallel: the researcher gathers competitor pricing and packaging evidence; the analyst builds the willingness-to-pay model from your internal data.
3. Cross-check round: the lead has each teammate critique the other's draft — the analyst stress-tests the researcher's competitive claims; the researcher checks the analyst's assumptions against market evidence. This is the "disagreement as a feature" step, built into the workflow.
4. Synthesis with explicit reconciliation: the lead produces one recommendation, and where the models' judgments diverged — say, on price elasticity — it says so and explains which evidence tipped the call.
5. Your checkpoint: you review one synthesized recommendation with the disagreement surfaced, not two chat logs you have to reconcile yourself.

Costs and billing, honestly

• Two billing relationships. OpenAI usage bills to your OpenAI account; Anthropic usage bills to your Anthropic account. A ChatGPT Plus or Claude Pro subscription does not cover API-key usage — API billing is separate from consumer subscriptions.
• Batch discounts exist. Both OpenAI and Anthropic offer ~50% off for batch (non-urgent) API processing — worth knowing for research legs that don't need to be instant.
• Two models cost more than one. Cross-checking doubles the tokens for the rounds you duplicate. The question is whether the disagreements it surfaces are worth it — for high-stakes decisions, usually; for routine drafting, rarely.
• Multi-model is not inherently cheaper. Multi-model workflows can reduce cost when lower-cost models are deliberately assigned to suitable tasks, but using multiple agents or models can also increase total API usage.

Tradeoffs

Frequently asked questions

Sources