Claude vs ChatGPT vs Gemini: Integrations

A vendor-document comparison of integration routes and limits, from ChatGPT's listed Drive, OneDrive, and SharePoint connectors to Gemini's Workspace requirement for Drive files.

Sources checked 2 Oct 2026

For Claude vs ChatGPT vs Gemini integrations, treat the consumer app and developer API as separate layers: vendors document different routes, limits, plan controls, and policy conditions, but not which setup fits your workflow. ChatGPT lists connected sources, Claude documents cloud deployment and project knowledge, and Gemini documents Drive and Workspace-linked files (OpenAI’s data analysis guide, Anthropic’s Claude pricing documentation, and Google’s Gemini file-upload guide). Chat Picker has not tested the assistants for integration quality, speed, accuracy, or reliability, as its sourcing method states.

What the vendors document: Claude vs ChatGPT vs Gemini

The figures here are as read on the linked vendor pages in October 2026. Consumer and API bills are separate: ChatGPT Plus is $20 per month with monthly billing; Claude Pro is $20 monthly or $17 per month with annual billing and $200 up front; Google AI Pro is $19.99 per month (OpenAI, Anthropic, and Google).

OpenAI separates API input, cached-input, and output prices and applies a 10% regional-endpoint uplift to models released on or after March 5, 2026. Anthropic says regional and multi-region endpoints carry a 10% premium, while US-only inference for Claude 4.6 and later uses a 1.1x multiplier. Google says each Search query triggered by a submitted request is charged, although the cited passage provides no numeric charge (OpenAI API pricing, Claude Platform pricing, and Gemini API pricing). The cited Gemini pages do not state a data-residency rule.

The clearest connection differences are concrete. OpenAI says files from connected Google Drive, OneDrive, and SharePoint sources can be used for data analysis when connectors are available for the account or workspace. Gemini requires Keep Activity to be on and Google Workspace to be connected for Drive access; a work or school administrator must enable access. Anthropic says MCP servers in its Connector Directory must follow its Directory Policy. The pages read provide no comparable plugin count or ecosystem score (OpenAI, Google, and Anthropic).

For developer routes, OpenAI says its latest models support text and image input, text output, vision, the Responses API, and client SDKs. Anthropic says all current Claude models support text and image input, text output, vision, and tool use; Claude is also available through Amazon Bedrock and Google Cloud. Google’s API pages list models and intended uses, but the cited material does not provide one common modality matrix or SDK comparison (OpenAI models, Claude models, and Gemini models).

File boundaries differ too. OpenAI documents a 512 MB hard limit per ChatGPT or GPT file and three uploads per day for free users. Claude lists a 500 MB chat-file limit and a 30 MB project-file limit. Gemini permits up to 10 supported files in one prompt; one code folder or GitHub repository can contain up to 5,000 files within 100 MB (OpenAI, Claude, and Gemini).

Consumer-plan controls do not settle API retention. ChatGPT lists an opt-out on Free, Go, Plus, and Pro; Claude lists one on Free, Pro, and Max and says Team is not trained on by default. The Gemini plans page states no corresponding setting (OpenAI, Anthropic, and Google).

OpenAI warns that ChatGPT can be incorrect or misleading and may sound confident. Anthropic says not to rely on Claude as the only source of truth and to inspect cited sources. Gemini Apps may show sources and related content, but displaying a source does not certify the answer (OpenAI, Anthropic, and Google).

Usage policies add further constraints. OpenAI prohibits tailored licensed advice without appropriate professional involvement. Anthropic requires qualified review for covered advice and decisions, requires AI disclosure for consumer-facing outputs, and treats legal interpretation as high-risk. Google says Gemini should not produce factually inaccurate content that could significantly harm health, safety, or finances. These are vendor rules, not legal advice (OpenAI, Anthropic, and Google).

What the documentation cannot tell you

The pages cannot show whether a connector can reach your permissioned source, whether an SDK handles your authentication and error paths cleanly, or whether citations survive your workflow. They also provide no workload-specific cost, latency, accuracy, or reliability result.

Anthropic separately warns that accuracy and recall can degrade as token count grows. A larger context limit therefore does not prove better retrieval. The pages describe projects, instructions, retrieval, and model choice, but do not provide a comparable fine-tuning matrix. Treat those as different controls: a project instruction is not fine-tuning.

How to check it yourself

Test connections. Put a non-sensitive file named support-summary.pdf in each connected source supported by your selected plan. Ask: “Find support-summary.pdf, identify its support categories, and quote the heading for each.” Record connector availability, permission behavior, citations, and any failure message. Mark an unavailable connector separately.
Test files and visual data. Give each assistant a one-page PDF with a labeled bar chart and a CSV containing the same values. Ask: “Compare total revenue by region between the PDF chart and CSV. Return the values, units, and page or row supporting each figure.” Record extraction errors, unit changes, unsupported claims, and missing citations.
Test API economics. Send this through each documented API: “The checkout service returned errors for a short period, some customers were charged twice, and the incident manager needs a concise summary plus the affected systems. Return JSON with exactly the keys summary and affected_systems.” Record input, cached-input, output, tool, search, and regional charges where exposed.
Test the developer path. Use the same checkout prompt through the vendor’s documented starter model. Record authentication steps, SDK errors, structured-output handling, tool-call behavior, and whether usage appears in logs. Do not turn setup difficulty into an unsupported quality claim.
Test customization. Create a project or equivalent workspace with the instruction: “Treat the uploaded policy as fictional, answer in US English, and cite the sentence that controls each conclusion.” Attach a short policy document and ask about its notice period. Record instruction following, retrieval, source traceability, plan requirements, and whether the feature is merely customization rather than fine-tuning.
Test source fidelity. Give each assistant a controlled document whose claims can be checked against an original source. Ask: “List each claim supported by the document, quote the supporting sentence, and label any statement the document does not support.” Check every citation yourself and record unsupported statements or misleading confidence.
Check governance. Use only synthetic data. Give each assistant a fictional support ticket containing an email address, delivery address, and order description. Ask: “List the personal-data fields and the access, retention, location, and deletion questions this conversation cannot resolve.” Then verify the categories against settings, endpoints, and contract terms. Record unresolved requirements for a qualified reviewer.

Which rows of the comparison matter

On the three-way comparison matrix, start with plan and API prices, context and usage limits, file boundaries, training controls, and source verification. Then check whether it includes rows for connected sources, projects or knowledge, cloud and API availability, regional endpoints, and policy review.

Treat a blank “Not verified” cell as unresolved, not as evidence that support exists. Chat Picker records vendor figures with their source and read date but supplies no test scores or rankings, as its method explains.