Comparing AI Assistants for Logistics Planning

A vendor-documentation comparison of ChatGPT, Claude, and Gemini for logistics planning, covering prices, file limits, data controls, and accuracy warnings.

Sources checked 2 Oct 2026

For logistics planning, the vendor pages reviewed here document differences in file intake, context, research tools, usage limits, data controls, and prices. They do not provide a shared logistics benchmark or establish which assistant plans routes or forecasts demand more accurately, and Chat Picker has not tested them for those tasks.

What the vendors document

As read on the vendor pages in October 2026, with the linked pricing pages checked 2026-10-01:

Vendor page and read date	Main paid option	Higher-use options
ChatGPT pricing, checked 2026-10-01	Plus is $20/month, billed monthly.	Pro 100, Pro 200, and Pro 500 are $100, $200, and $500 per month.
Claude pricing, checked 2026-10-01	Pro is $20/month, or $17/month with annual billing and $200 up front.	Max starts at $100/month for 5x or 20x Pro usage; the page showed no numeric price for 20x.
Google AI plans, checked 2026-10-01	Google AI Pro is $19.99/month.	Google AI Ultra is $99.99/month for 5x AI Pro limits or $199.99/month for 20x.

The context and upload figures below were also read on 2026-10-01.

ChatGPT’s pricing page lists 27K of app context for Free instant chats and 400K for Pro reasoning chats, plus other plan-and-model combinations. Its file uploads FAQ, checked the same date, limits Free users to 3 uploads per day, sets a 512MB hard limit per file, and caps spreadsheets at approximately 50MB.
Claude’s pricing page and upload guide, both checked 2026-10-01, say context can reach 1M on every plan and varies by model. Chat uploads are capped at 500MB per file and project files at 30MB; XLSX uploads require code execution and file creation.
Google’s plans page does not state an app token figure. Its upload guide, checked the same date, allows up to 10 supported files per prompt, subject to availability, and caps nonvideo files at 100MB.

For data work, OpenAI’s data-analysis page says ChatGPT can inspect uploaded data and create tables or charts. It recommends structured files with clear column names and one record per row. Claude Projects can keep documents, text, code, instructions, and separate chat histories together for recurring work.

Research options also vary. OpenAI’s Deep Research page documents uploaded files, searches of the public web or specific sites, enabled apps, citations, and usage that varies by plan. Claude Research is available with Pro, Max, Team, and Enterprise, requires web search, and can use connected Gmail, Calendar, and Docs. Gemini Deep Research can use Search, files, NotebookLM, and connected Gmail or Drive.

On chat training, OpenAI’s pricing page offers an opt-out on Free, Go, Plus, and Pro. Claude’s pricing page offers one on Free, Pro, and Max, and says Team is not trained on by default. Google’s plans page does not state a training setting.

OpenAI warns that ChatGPT can sound confident when wrong and advises verifying important information. Anthropic says not to use Claude as the only source of truth and to inspect original sources behind web results. Google says Gemini Apps sometimes show sources or related content; its safety guidelines say Gemini should not generate factually inaccurate outputs that could cause significant real-world harm. That is a policy boundary, not proof that every answer is accurate.

OpenAI’s usage policies say its rules do not replace legal requirements, professional duties, or ethical obligations, and restrict unauthorized handling of private or sensitive information. Anthropic’s Usage Policy calls for relevant human expertise in elevated-risk uses and qualified professional review for covered advice, recommendations, or subjective decisions. Google says it evaluates outputs in context.

What the documentation cannot tell you

The pages above do not tell you how an assistant handles your road network, sparse demand history, live disruptions, or WMS, TMS, and ERP data. They do not compare API latency, uptime, or connector behavior, isolate an assistant’s effect in a logistics case study, or calculate your full labor and usage cost. Run the same check on your own data if a result will drive a purchase. The earlier version’s unsourced statistics and test results have been removed.

How to check it yourself

Use the plan you expect to buy, and apply the same sanitized inputs, prompt, and scoring sheet to every assistant.

Route constraints. Give each assistant the same sanitized CSV with depot IDs, coordinates, delivery windows, vehicle capacities, and known travel times. Ask: “Propose feasible routes, show how each constraint is satisfied, identify missing inputs, and do not invent distances.” Look for explicit assumptions and constraint violations. Record feasibility, missing data, and any unsupported distance.
Demand accuracy. Attach a historical table with date, site, actual shipments, known promotions, and recorded closures. Ask: “Separate training and holdout periods, choose an appropriate error metric, forecast demand, justify every transformation, and flag uncertainty.” Look for data leakage, a baseline, and treatment of unusual periods. Record formulas, assumptions, and missing fields.
Integration readiness. Provide a sanitized API schema plus success and failure responses. Ask: “Write a deployment checklist covering credentials, permissions, validation, rate limits, retries, logging, and data exposure; identify what must be tested in a controlled environment.” Look for unsupported claims about latency or reliability. Record unresolved setup steps and the tests you will run.
Case evidence. Use each available web or research mode and give the same brief: “Find verifiable logistics case studies that identify the product, date, baseline, deployment period, measured result, and direct source; label vendor claims separately.” Look for whether the named assistant actually caused the result. Record missing baselines, unclear periods, and sources you must inspect.
Cost fit. Attach a worksheet with your actual query volume, file volume, seat count, review hours, and current vendor or API prices. Ask: “Build monthly and annual scenarios from the attached figures; include subscriptions, usage charges, storage, overages, and human review time, and list excluded costs.” Look for double counting and unsupported allowances. Record every assumption and who will maintain the model.

Which rows of the comparison matter

Use the Claude vs ChatGPT vs Gemini matrix to compare the rows for free and paid plans, main and heavy-use prices, app context, upload formats and limits, usage allowances, training controls, web search and research, connected apps, API prices, team billing, and ads. For logistics, give closest attention to context, file intake, usage, data controls, and API prices. The site marks unconfirmed figures “Not verified” rather than filling them in.