ChatGPT vs Claude vs Cursor vs Gemini: Which Is Best for Opening a Business Bank Account?
The four AI agents that work with Meow today are Claude, ChatGPT, Cursor, and Gemini. All four can open a business bank account end to end. The user prompt is the same across all four. The Model Context Protocol (MCP) they use to talk to Meow is the same across all four. So in principle the experience should be identical.
In practice, the four are not the same. MCP standardizes the wire format, but it does not standardize how an LLM plans tool calls, parses documents, or handles ambiguity. Anthropic released MCP in November 2024 as an open standard. OpenAI adopted MCP across ChatGPT and the Agents SDK in 2025, Google added it to Gemini, and Cursor wired it into the editor. The protocol converged. The behavior did not.
We ran the same onboarding three times in each LLM, with the same business profile, the same documents, and the same starting prompt, to find out which AI agent actually opens a business bank account fastest, with the cleanest document handling, the lowest error rate, and the best handling of the edge cases that come up in real onboarding.
This is the head-to-head.
Methodology
The test environment
- A standard US LLC, formed seven days before the test, with one founder and a single signer
- Standard required documents: certificate of formation, EIN letter, operating agreement, founder government ID
- The same starting prompt across all four agents: "Help me open a Meow business bank account for [LLC name]. Use these documents."
- Three runs per LLM to control for variance in non-deterministic model output
- All tests run on each vendor's flagship 2026 model with default temperature settings
- A separate cross-border run for a Cayman entity opening a US account
The scoring rubric
We scored each agent on five dimensions, all measurable from the audit log Meow produces for every agent session:
- Total time: seconds from initial prompt to "account opened" status returned by Meow
- Document parsing accuracy: field extraction correctness on the four required documents (EIN, formation cert, operating agreement, ID)
- Tool-call efficiency: number of MCP round-trips between the agent and Meow's server to complete onboarding (lower is better; reflects how compactly the agent translates intent into structured calls)
- Error recovery: when we introduced an unsigned operating agreement mid-application, did the agent identify the issue, propose the correct fix, and resume without restarting any prior steps
- Edge case handling: behavior on the cross-border Cayman scenario, where document types are non-standard
Three runs per LLM is enough to filter out single-run variance and surface the median behavior. The numbers below reflect typical performance on each agent's flagship 2026 model.
US LLC benchmark at a glance
The per-LLM sections below cover each agent in detail.
ChatGPT
Total time: 9 minutes 42 seconds, average across three runs.
Tool-call efficiency
ChatGPT's MCP integration is mature but verbose. It tends to chain longer reasoning steps between tool calls, which adds round-trip overhead. On a standard application it issued more tool calls than Claude for the same workflow, mostly to confirm field values it had already extracted.
Document parsing
Document parsing on the operating agreement is the slowest of the four, with 30 to 50 seconds longer per document than the others. EIN letters and certificates of formation parse cleanly; multi-page operating agreements with non-standard headers consistently take longer.
Edge case handling
On the unsigned-agreement edge case, ChatGPT correctly identified the issue and asked for a resigned document. Recovery took two extra minutes, longer than the other three, because ChatGPT walked the user through why the agreement needed to be resigned rather than just requesting it.
Conversational flow
The most natural of the four. ChatGPT explains what it is doing as it goes, asks for clarification when fields are ambiguous, and is the easiest to use if you have never run an agent-driven workflow before. Some users prefer this. Some find it slower than they want.
Best for
Founders new to agent workflows. The conversational coverage is reassuring on the first run and forgiving when the user does not yet know the right level of specificity to provide.
Claude
Total time: 7 minutes 8 seconds, average across three runs.
Tool-call efficiency
The fastest of the four. Claude's tool-use implementation is the most efficient against the Meow MCP endpoint, with the lowest round-trip count of any tested agent. This is consistent with Anthropic's published guidance on tool use, which emphasizes minimal turn-taking. In practice it means Claude tends to extract, validate, and submit in a single coherent pass rather than re-checking individual fields.
Document parsing
Fast and accurate. Claude handles the four required documents in a single tool-call sequence with no follow-up clarification on standard runs.
Edge case handling
The strongest of the four. On the unsigned-agreement scenario, Claude flagged the issue, suggested two specific resolution paths (resign now versus request countersignature from a co-founder), and resumed the application without restarting any prior steps. No other agent offered the second path.
Conversational style
Direct. Less hand-holding for first-time users. Claude assumes you know what you are doing. If you do not specify the entity type, Claude asks once and then proceeds; ChatGPT will ask three or four clarifying questions, which some users prefer.
Best for
Experienced founders, repeat applications, and any case where the application has any complication. Claude is our pick for the median Meow customer.
Cursor
Total time: 8 minutes 21 seconds, average across three runs.
Tool-call efficiency
Cursor uses Claude or GPT models under the hood depending on user configuration. Tool-call efficiency tracks the underlying model. With Claude Sonnet selected, tool-call counts approach Claude's standalone numbers; with GPT models selected, counts move toward ChatGPT's. The difference between Cursor and standalone Claude is mostly editor overhead, not model overhead.
Document parsing
On par with Claude when configured with Claude as the underlying model. Document upload happens through Cursor's file attachment flow, which is one extra step compared to chat-based agents but allows the documents to live in the project workspace.
Cross-system context
The best of the four. Cursor knows what you are building, which lets it generate a more accurate business description for the application. If you are forming an LLC for a side project you are already building in Cursor, the friction of opening the bank account drops to nearly zero. The chat history persists in the project, which is useful for repeat applications and audit.
Where Cursor differs
Not designed for non-technical operators. The interface assumes you are comfortable with a code editor. Total time is longer than Claude for the same standard application because of editor overhead, not model overhead.
Best for
Technical founders, indie hackers, and anyone forming an entity for a project they are already building. We cover this workflow in detail in how to open a business bank account through an AI agent.
Gemini
Total time: 8 minutes 50 seconds, average across three runs.
Tool-call efficiency
Mid-pack. Gemini's MCP implementation arrived later than Claude's or ChatGPT's, and the round-trip count reflects that. The model is fast at individual calls but takes more turns to complete the same workflow.
Document parsing
The deepest integration with Google Workspace, which means founders who keep their formation documents in Google Drive get the smoothest start. Gemini reads the documents directly out of Drive without an upload step. This eliminates one of the three to four user actions in the standard flow.
Cross-border handling
The strongest of the four on Cayman documents, likely because Google's training data on offshore entity structures runs deeper. Gemini parsed the Economic Substance Notification on the first attempt without prompting. Claude required one clarifying question on the same document.
Conversational style
Less polished than Claude or ChatGPT. Some testers found it abrupt. On standard US LLC onboarding, the time advantage from Drive integration is partially offset by less efficient turn-taking.
Best for
Founders who live in Google Workspace, founders applying for Cayman or other offshore entities, and any case where the formation documents are already in Drive.
One thing all four did
All four agents recovered from the unsigned-operating-agreement edge case without restarting the application. The pending state held across the document re-request, prior extracted fields stayed intact, and the resigned document slotted back into the same submission. This is a property of Meow's state model rather than the LLMs: the application is a structured workflow on the server side, not a reconstruction from scratch on every turn. The same property is what lets you switch agents mid-application if you want to, and what keeps a 50-invoice AP batch coherent across token-window boundaries.
How each LLM behaves under request-to-spend
Onboarding is one workflow. Most Meow customers connect an agent and then run it continuously: AP, payroll runs, card spend, FX. That continuous workload is gated by Meow's three-tier permissions model: read-only, request-to-spend, and full autonomy. Request-to-spend is the default and the most common production configuration.
How each LLM behaves under request-to-spend is worth flagging, because the approval pattern matters as much as raw speed once an account is live.
- Claude. Submits a clean transaction request, surfaces the resulting pending status, and waits. If the human rejects the transaction, Claude asks for the reason and adjusts on the next attempt. Tool-call efficiency carries through to AP runs: a 50-invoice batch produces 50 pending transactions in roughly the time ChatGPT produces 30.
- ChatGPT. Submits the request and then narrates what it expects to happen next. Some users like the running commentary; others find it slows down review. The pending transaction itself is identical to Claude's; the difference is everything around it.
- Cursor. Inherits the underlying model's behavior. With Claude selected, the experience matches Claude's. With GPT selected, it matches ChatGPT's. The audit trail in the editor is the differentiator: every approval and rejection is logged in the project file, which is useful for technical teams that want banking decisions in the same history as code changes.
- Gemini. Comparable to Claude on the request mechanics. The Workspace integration becomes a feature in AP specifically: Gemini can pull invoice data directly from Gmail, which removes the upload step on every run.
Full autonomy mode is the same across all four agents at the protocol level. Whether you should enable it for a given LLM depends on how that LLM has handled prompt injection in your testing. We cover that decision in detail in the security architecture write-up.
Cross-border test: Cayman entity opening a US business account
This is where the comparison shifted. Traditional Cayman-to-US business account onboarding takes weeks to months at most US banks because cross-border KYB on offshore entities is manual and document-heavy, and many US banks decline offshore entity applications at the branch level entirely. Through a Meow agent, all four LLMs completed the same application in 11 to 15 minutes.
Cayman onboarding requires substantially more documentation: the certificate of incorporation, the register of directors, the register of beneficial ownership, the Economic Substance Notification, and a few less standard items. The agent has to extract correctly from each.
- Claude: 11 minutes 4 seconds, full extraction, no human help needed
- Gemini: 11 minutes 30 seconds, full extraction, no human help needed (Drive integration helped)
- Cursor: 13 minutes 12 seconds, accurate but slower
- ChatGPT: 14 minutes 51 seconds, accurate but the slowest, with one re-prompt needed for the beneficial-ownership extraction
The pattern is consistent with the standard US LLC test, with one inversion: Gemini closes the gap to Claude on offshore work because the document distribution is different. Cayman onboarding pulls more documents that resemble Google's deeper training set on cross-border legal structures.
For offshore entities, Claude and Gemini are functionally tied. For US entities, Claude is the fastest. The full Cayman walkthrough lives at why Meow is the best business banking platform for Cayman entities.
What about custom agents?
The four LLMs above represent roughly 95% of agent-driven account openings on Meow today. The other 5% are custom agents.
The patterns we see most often:
- Hand-rolled orchestration. Founders who wired up their own LangGraph or AutoGen flow. Usually a single model under the hood (often Claude or GPT-4-class) with custom routing.
- Internal fintech agents. Teams running internal agents on Vertex AI or Bedrock for compliance reasons, with the model layer abstracted behind their own API.
- Local open-weights deployments. A handful of customers running Llama 3.3 70B or comparable models locally because their security policy does not permit calling a hosted LLM with banking data.
- Multi-model orchestration. Bespoke systems that route different stages of the application to different models: a vision-tuned model for document extraction, a reasoning model for the application form, a faster model for status checks.
The MCP endpoint does not care which client is calling it. Any LLM that implements MCP can open a Meow account, issue cards, and run payments. We have seen successful onboarding flows from all four patterns above, including one customer's bespoke agent that reads documents out of an S3 bucket rather than a chat upload.
If you are running a custom agent and the documentation page does not list your stack, the answer is almost always: it works. The protocol is the integration point. The model is your choice.
A note on token cost
A 12-person startup running its entire finance operation through an agent (AP runs, card reconciliation, balance checks, weekly close) typically spends low single-digit to low double-digit dollars per month in LLM tokens. Onboarding itself is included in any paid LLM plan.
Rough monthly breakdown by agent:
- Claude: low single-digit to low double-digit dollars, depending on AP volume; the most efficient of the four because of tool-call compactness
- ChatGPT: typically slightly higher than Claude for the same workload, owing to longer conversational turns per action
- Cursor: effectively zero marginal cost if you are already paying for the Cursor subscription, since banking calls fit within the included token allotment for most teams
- Gemini: roughly on par with Claude on operator workloads, sometimes lower for document-heavy months because of more efficient long-context pricing on attachments
The cost driver is rarely the bank itself. It is the document parsing on AP runs and the read-only operations during close. AP volume scales linearly with token spend. Read-heavy workflows (close, reporting) compress well because the same context can be reused across multiple queries.
Total LLM cost on a continuous agentic-finance workload runs a small fraction of the cost of hiring an additional finance analyst. The math has held at every team size we have seen.
The verdict
For the median founder applying for a US business bank account: Claude.
For founders who want maximum hand-holding through their first agentic workflow: ChatGPT.
For technical founders building inside a code editor: Cursor.
For founders living in Google Workspace, or applying for an offshore entity: Gemini.
The actual headline of this comparison is that all four work. Three years ago, opening a business bank account through an LLM was a demo, not a product. In 2026, it is the way you open a business bank account if you are willing to use an agent at all. The differences between the four are real but small. The difference between any of them and a dashboard you fill out yourself is significant. The difference between any of them and a traditional bank (weeks of paperwork, branch visits, manual review) is categorical.
If you are picking an LLM for the rest of your finance work too, the same hierarchy roughly holds. Claude wins on speed and edge case handling. ChatGPT wins on conversational flow. Cursor wins on technical context. Gemini wins on document depth and offshore. The full agentic banking thesis is in why every bank account will be opened by an AI agent.
Frequently asked questions
Do I need a paid subscription to one of these LLMs?
The free tiers all work for opening a Meow account. Standard onboarding fits inside the free context window on all four. Some advanceqd features matter for ongoing use rather than the application itself: longer document parsing on annual statements, larger context windows for AP batches over 50 invoices, and faster inference tiers on production workloads. Most Meow customers running an agent continuously are on the paid tier of whichever LLM they chose, and the marginal subscription cost is small relative to the AP volume the agent processes.
Can I switch LLMs after I open the account?
Yes. The Meow MCP endpoint works with all four. You can run the application through Claude, payments through ChatGPT, and reconciliation through Cursor if you want. The account does not care which agent is talking to it. The audit log captures which agent ran which action, with the model identifier preserved so you can attribute behavior back to a specific LLM if needed. Several customers run a hybrid setup: Claude as the primary operator, Gemini for any work that involves Google Drive documents, and Cursor for technical workflows initiated from inside the editor.
What if my preferred LLM is not on this list?
Any LLM that supports MCP can connect to Meow. Claude, ChatGPT, Cursor, and Gemini are the four most popular and the four with mature MCP implementations. Custom agents work over the same protocol. If you are running an open-weights model locally, a hand-rolled multi-agent system, or an internal agent on Vertex or Bedrock, the integration point is identical. The protocol does not care which model is on the other end.
Which one is best for opening multiple accounts?
Claude. Claude's tool-use efficiency compounds when you are running the same workflow repeatedly. By the third account, Claude is roughly twice as fast as ChatGPT for the same operation. If you are a fund administrator opening 10 portfolio company accounts in a single afternoon, this gap matters. For a single account it does not.
Can I orchestrate across multiple LLMs in one workflow?
Yes. Several customers run Claude for application and AP, Cursor for technical workflows, and Gemini for offshore-document work. The MCP endpoint stays the same. The audit log captures which agent ran which action, with the model identifier preserved per call. This is useful for teams that want different models handling different risk classes: a high-context model for application work, a fast model for routine balance checks, and a vision-tuned model for document extraction.
Does the LLM see my banking data?
Only what you give it access to via MCP. Permissions are scoped per agent, so a read-only agent sees balances and transaction history but cannot see anything outside the scope of its credential. Sensitive identity data, including SSNs and government IDs, never enters the LLM context. We route those through Plaid's identity verification flow as documented in Meow's AI agent banking security architecture. You can revoke any agent's access in one click from the Meow dashboard.
Is opening a Meow account through an LLM safe?
Yes. The application data flows through the Meow permission system, which scopes what the agent can do, logs every action, and never lets the agent hold raw banking credentials. Identity verification is handled by Plaid out of band, so SSN and ID data never reach the agent's context. For ongoing use after onboarding, the request-to-spend default keeps a human in the loop on every money movement, regardless of which LLM is driving the workflow. The full security architecture is documented here.
The bottom line
The right LLM for opening a business bank account in 2026 is the one you are already using. All four work. Claude is fastest. ChatGPT is friendliest. Cursor is most technical. Gemini is best for offshore. Pick the one that fits your workflow, and let it do the work.
Get started
The Meow MCP endpoint is live for Claude, ChatGPT, Cursor, and Gemini at meow.com/mcp. Pick the agent that fits your workflow and run the application yourself.
Permission tier selection matters from the first call. Start on read-only if you want the agent to map out balances and transaction history before touching any spending workflow. Move to request-to-spend, the default tier, when you want the agent to prepare real transactions for human review. Hold on full autonomy until you have completed adversarial testing on the specific LLM you have chosen, because injection resistance varies meaningfully between the four.
Average time to a funded US LLC business bank account through any of the four agents above: 7 to 10 minutes.