ChatGPT

Claude vs ChatGPT for Scientific Computing

Compare documented ChatGPT and Claude limits for scientific computing, including context, uploads, data controls, and accuracy warnings, without claiming a test-backed winner.

Sources checked 2 Oct 2026

If you are weighing Claude vs ChatGPT for scientific computing, the vendors' own pages document practical constraints—plan access, context, uploads, and data controls—but do not establish which assistant is more correct for equations, experiments, uncertainty analysis, or citations. Chat Picker has not tested either assistant for this use and does not declare a winner.

What the vendors document: Claude vs ChatGPT

As read on October 1, 2026, OpenAI's ChatGPT pricing page lists app context windows of 27K for Free instant chats, 54K for Go and Plus, and 128K for Pro; its reasoning-model row lists 256K for Go and Plus and 400K for Pro. Anthropic's Claude pricing page says the Claude app offers up to 1M context on every plan, varying by model. Neither page publishes exact message counts. A larger documented window does not establish that an assistant will interpret a long research bundle correctly.

The file pages, also read on October 1, 2026, add narrower boundaries. OpenAI's File Uploads FAQ says uploads are available on Free and paid plans, subject to limits: every chat file has a 512MB hard limit; text and document files are capped at 2M tokens; CSV files and spreadsheets are capped at approximately 50MB depending on row size; and Free users have 3 uploads per day. Anthropic's upload-files page lists 500MB per chat file and 30MB per project file, plus additional token limits, and requires code execution and file creation for XLSX uploads. It says PDFs of 100 pages or fewer receive text and visual analysis, while PDFs from 101 to 1000 pages receive text-only processing.

In that same October 2026 reading, OpenAI's File Uploads FAQ says chats are saved until deletion and associated files are generally deleted within 30 days after the relevant chat, account, or custom GPT is deleted, subject to the FAQ's exceptions. Its ChatGPT pricing page says training opt-out is available on Free, Go, Plus, and Pro. Anthropic's Claude pricing page says opt-out is available on Free, Pro, and Max, while Team is not trained on by default.

The accuracy notes are equally direct. OpenAI's accuracy guidance says ChatGPT can be incorrect or misleading and may sound confident; it recommends using it as a first draft and verifying quotations, data, technical information, and references. Anthropic's incorrect-response guidance says Claude can produce convincing quotations not grounded in fact; it advises against relying on Claude as a sole source of truth and recommends checking cited sources for context omitted from the synthesis.

What the documentation cannot tell you

The vendor pages read for this comparison do not provide a controlled, task-by-task evaluation of symbolic derivation, integration and simplification, experiment design, uncertainty propagation, citation accuracy, or research workflow integration. They do not tell you whether an answer contains a sign error, an unbalanced design, a mishandled covariance term, a broken code edge case, or a plausible-looking citation that does not support the claim.

A controlled trial on your own workload is needed to answer those questions. Use the same prompt, files, tool access, and intended plans for both assistants; compare against an analytical solution, independently checked code, or a primary source; and preserve each raw response before editing it. Set acceptance rules before reviewing the outputs. The older page's unsourced statistics and test results have been removed rather than carried forward.

How to check it yourself

Use the plans you intend to buy, keep attached files and tool access consistent, and run each task in a fresh chat. Save raw outputs before correcting them.

Symbolic derivation. Give both: “Derive the general solution for the linear equation y'(t)+k y(t)=exp(-a t), treating k and a as nonzero constants. Show every integration step, handle the case where k equals a, simplify the result, and verify it by differentiation and substitution.” Look for correct signs, constants, algebra, and a valid check. Record each error and any assumption left unstated.
Experiment design. Give both: “Design a temperature-by-catalyst-loading experiment with yield as the response. Propose factor levels and ranges, include replicated center points, randomized run order, controls, blocking over production batch, and a prespecified analysis. State assumptions and justify each design choice.” Look for a coherent run structure, defined controls, and an analysis that matches the design. Record missing constraints, unclear replication, and required edits.
Error propagation. Give both: “For uncertain inputs A and B, derive first-order propagated standard uncertainty for f(A,B)=A×B under independence. Then include the covariance term for correlated inputs and explain when linear propagation is inadequate.” Look for the derivative terms, correct uncertainty units, a clear independence assumption, and a valid discussion of nonlinearity. Record omitted terms or mismatched units.
Reproducibility and citations. Give both: “Identify the primary paper that introduced the Metropolis-Hastings algorithm. Give the authors, title, venue, year, DOI or stable URL, and a supporting quotation. Explicitly say if you cannot verify any field.” Look for a source that exists, metadata that matches, and a quotation that supports the attribution. Record dead links, incorrect details, or context that does not support the claim.
Workflow integration. Give both: “Write a complete Python script named analyze_results.py that reads results.csv with columns run_id, temperature_c, and yield_percent; validates the headers and numeric values; computes count, mean, and sample standard deviation of yield_percent grouped by temperature_c; writes summary.csv; and exits with a clear message for malformed input. Use pandas and state the dependency version.” Look for a clean-environment run, correct grouping, defined missing-value handling, and the requested output schema. Record dependency issues, code changes, failures, and manual steps.

Which rows of the comparison matter

In the Claude vs ChatGPT comparison matrix, prioritize these labeled rows:

Context window in the app and How usage limits are described for document fit and capacity.
Training on your chats for unpublished or sensitive material.
Free plan, Main paid plan, Heavy-use plans, and Team plan for expected volume and administration.
Ads if interface policy makes it relevant.
API prices if you plan automation rather than app-only work.

A blank cell or Not verified is unresolved, not zero. Chat Picker's method records the vendor source and read date for published figures, but you should confirm current pricing and limits before subscribing. If Gemini is also on your shortlist, use the three-way matrix.