Comparing AI Assistants for Marine Research

What OpenAI, Anthropic, and Google document about ChatGPT, Claude, and Gemini for marine research, including file limits, research access, data handling, and accuracy caveats.

Sources checked 2 Oct 2026

Comparing AI Assistants for Marine Research comes down to what each vendor documents about files, research access, context, limits, and data handling; it does not establish which assistant is best. According to Chat Picker’s method, Chat Picker has not tested ChatGPT, Claude, or Gemini for these tasks and makes no quality or reliability ranking. This rewrite removes older statistics and test results that lacked sources.

What the vendors document

Plans and research. Start with plan fit. As read on October 1, 2026, OpenAI’s Plus page lists ChatGPT Plus at $20 per month with monthly billing; Anthropic’s pricing page lists Claude Pro at $20 per month with monthly billing; and Google’s US plans page lists Google AI Pro at $19.99 per month. On free plans, OpenAI’s pricing page describes limited uploads, Anthropic’s page lists web search, file creation, code execution and memory, and Google’s page requires a Google Account and says Pro access varies. These prices do not establish equal usage allowances.

Research modes differ. OpenAI’s Deep Research page says the mode can use uploads, the public web, selected sites and enabled apps, creates a proposed plan and cited report, and varies by plan. Anthropic’s Research page limits Research to Pro, Max, Team and Enterprise, requires web search, and can use connected Gmail, Calendar and Docs plus the web. Google’s Deep Research page includes Google Search by default and allows files, NotebookLM notebooks, Gmail or Drive. This documents availability, not synthesis quality.

Files, context and data. Upload limits can decide whether a dataset reaches the assistant. In the same October 1, 2026 reading, OpenAI’s File Uploads FAQ sets a 512 MB hard limit per file and approximately 50 MB for CSV files or spreadsheets; Anthropic’s upload page gives 500 MB for a chat file and 30 MB for a project file and requires code execution and file creation for XLSX; Google’s file-upload page allows up to 10 supported files in one prompt, with nonvideo files up to 100 MB. A permitted file is not proof that every row or page was parsed correctly.

Context figures are not quality scores. In the same reading, Anthropic’s pricing page says Claude can use up to 1M tokens on every plan, with the amount varying by model; OpenAI’s pricing page lists separate app context figures for Instant and reasoning categories by plan; and Google’s plans page gives no app token figure. For training on chats, OpenAI’s page lists an opt-out on Free, Go, Plus and Pro, while Anthropic’s page lists opt-out on Free, Pro and Max and says Team is not trained on by default. Google’s plans page states no comparable training setting. These rows do not provide a complete institutional retention, access or deletion schedule.

Accuracy and policy. OpenAI’s accuracy note says ChatGPT can produce incorrect or misleading output and that confidence is not reliability. Anthropic’s incorrect-response note says not to use Claude as a sole source of truth and to check cited sources and original context. Google’s related-content page says links may cover public sites, uploaded files and connected Workspace documents; Google’s policy guidelines say context matters, including scientific applications, and that Gemini should not generate factually inaccurate outputs that could cause significant real-world harm. OpenAI’s usage policies say its rules do not replace professional duties; Anthropic’s Usage Policy calls for qualified human review of covered advice directly affecting people. These statements support source, code and domain checks, not validation of a marine method.

What the documentation cannot tell you

The documentation cannot tell you whether an assistant will misread quality-control flags, choose an unsuitable statistical test, violate physical constraints, run code correctly, or calibrate uncertainty on your files. A citation link shows that a source is offered; it does not show that the source supports the claim. A controlled trial can answer those questions for your workflow, but its result does not establish a general ranking or transfer automatically to another dataset, plan or task.

How to check it yourself

Use the same de-identified or synthetic input and exact prompt in fresh chats. Record each plan, enabled tool, date, answer and code result; do not infer a general winner.

Oceanographic data wrangling. Give each assistant the same CSV with documented units, missing values and quality flags. Ask: “Inspect the attached daily sea-surface temperature CSV. Report schema, units, missing values, duplicate timestamps and quality flags; do not impute. Show validation code and stop if units conflict.” Look for invented records, silent coercion and hidden assumptions; record errors, omissions and edits.
Statistical analysis. Attach temperature and salinity observations with basin labels. Ask: “Using the attached observations, test whether salinity differs among the labeled basins. State assumptions, treat missing data explicitly, report effect size and uncertainty, address multiple comparisons and provide Python.” Check the test rationale, independence, distribution and leakage; record mismatches between prose, code and caveats.
Circulation model construction. Attach regular-grid current and temperature fields. Ask: “Using these fields, propose a neural-network parameterization for a circulation model. Define tensor shapes, coordinates, physical constraints, losses, data splits and failure checks; do not invent observations.” Look for grid, units, boundary and conservation handling; record unsupported assumptions and reproducibility gaps.
Ensemble evaluation and uncertainty. Attach forecasts and held-out observations. Ask: “Evaluate the attached circulation forecasts against the held-out observations using a simple baseline and an ensemble. Keep the holdout untouched, report metrics and calibrated uncertainty, and separate measured results from proposed checks.” Look for leakage, unsuitable metrics and unsupported calibration claims; record mismatches.
Literature evidence. Attach a marine-biology paper set. Ask: “Using only the attached literature, summarize evidence on coastal heatwaves. Tie each substantive claim to a specific source passage and list contradictions or missing evidence.” Open every cited passage; record claims that outrun the source or omit contrary evidence.
Reproducibility and code. Attach a quality-control file named salinity_qc.csv. Ask: “Generate a standalone Python script that reads salinity_qc.csv, validates required columns, flags rows with a missing station ID, duplicate timestamp or nonnumeric salinity without filling them, writes salinity_anomalies.csv, and prints a summary. State dependencies and random seeds.” Run it in a clean environment; record edits, execution errors and whether the output matches the answer.

Which rows of the comparison matter

Open the Claude vs ChatGPT comparison matrix and read the rows for plan and billing terms, free-versus-paid features, file types and upload limits, app context, usage caps, web search and deep research, source display, training controls, projects, connected apps and API prices. Use the three-way matrix to place Gemini in the same rows. Treat “Not verified” as unresolved, confirm current terms on the linked vendor page and remember that an app subscription is not an API price.