ChatGPT and Claude compared for the work a finance team actually does
The comparison people search for is ChatGPT against Claude, and most answers online compare them on general benchmarks that tell a bank nothing. This page compares them by how each one works on finance tasks: a long regulation text, a spreadsheet, a credit file, a codebase.
There is no verdict at the end, and that is deliberate. Both products do the work, they are built differently, and the decision in a regulated firm turns on a contract and a data rule more often than on either product's capabilities.
The two products and their makers
ChatGPT is made by OpenAI. It runs in a browser, on the desktop and on the phone, takes files, searches the web, writes and executes code in a sandbox, and connects to other software. OpenAI's model line carries the GPT name, and the company sells a separate coding product, OpenAI Codex.
Claude is made by Anthropic and runs in the same places. Its model line carries the names Opus, Sonnet and Haiku, and the company sells Claude Code for developers. Both companies also sell API access, which is how either model ends up inside software a bank already owns.
Both describe themselves as assistants that reason over what you give them. Neither should be described from the other's marketing, and neither is ranked here against the other.
How each handles the finance tasks
On long documents, Claude's design puts the emphasis on taking a very large input in one piece, which is why teams reach for it when the task is a whole prospectus or a supervisory consultation paper and the answer has to account for a passage on page 180. ChatGPT handles long inputs too and leans on retrieval and tools, which suits a working set of many documents more than one enormous one.
On spreadsheets and figures, both write and run code to compute instead of guessing arithmetic, and that is the property to insist on: a number produced by executed code can be rechecked, a number produced by prose cannot. On regulation text both do well, and the differentiator is whether the tool will cite the article it relied on when you ask it to.
On drafting, ChatGPT has the longer track record with a wider range of registers. On reasoning through a messy internal document where the structure is implicit, Claude's long-context behavior shows. On agents both now run multi-step work with tools, and AI agents in finance covers what that requires in controls.
Codex against Claude Code, the coding comparison
People searching the model comparison often want the coding one, as "openai codex vs claude code". Both are agents for a repository and both run from a terminal, an IDE extension and a cloud surface, and both ask before they act. Claude Code steers from terminal, VS Code, JetBrains, Slack and the web and asks permission before it changes a file or runs a command. OpenAI Codex runs as a CLI, an IDE extension, a cloud service and inside the ChatGPT apps, with sandboxing, agent approvals, auto-review and profile-based permissions.
Both read a repository-level instructions file, which is where a bank puts its own rules for what an agent may touch. For a platform team the decision usually comes down to which cloud and which identity provider the firm already runs, not to which agent writes nicer code.
What this comparison does not settle
The two things that decide the matter in a bank are absent from every feature comparison online. The first is the firm's own data rules: which classes of data may leave the building at all, under which agreement, and whether the provider may train on them. A product that fails that test is out regardless of how it performs.
The second is the contract the firm can actually sign. Terms on liability, on logging, on where processing happens, on audit rights and on notice periods vary, and they are negotiated by procurement and legal, not chosen in a settings menu. The EU AI Act adds duties that follow the use case, which means the same product can be acceptable in one process and not in another inside the same bank.
Anyone who reads a comparison article and skips these two is choosing a tool they may not be allowed to use.
How does a Frankfurt team test both before deciding?
With its own documents and a scoring sheet written before the test. Take ten real tasks the team does every month, run them through both products, and score each answer on whether it is correct, whether it cites a source you can check, and how much rework it needed. Ten tasks beat any benchmark because they are your tasks.
Do it on an account whose terms allow the data you are using, and keep the outputs. When procurement asks why one product was chosen, that record is the answer. AI tools for finance lists what else belongs on the shortlist, since the two products here are not the only options.
Model choice and Finance Loop
Finance Loop is where people who have run this comparison inside a bank say what the deciding factor turned out to be, which is rarely the one the articles name. Finance Loop is the meeting place in Frankfurt for that exchange and takes no vendor's side in it.
Finance Loop is a professional network and has the goal of driving the adoption of emerging technologies in finance, such as AI, tokenization, stablecoins, and DeFi. Finance Loop helps its members build skills and personal networks in these fields: Investment & Digital Assets, Payments & Digital Money, Digital Infrastructure & Sovereignty, and Risk & Compliance.