How

AI Tools for Investment Analysis and Risk Assessment

Vendor documentation sets out prices, limits, data controls, and accuracy cautions for investment analysis, but cannot establish whether a given assistant answer is correct.

Sources checked 2 Oct 2026

For AI tools for investment analysis and risk assessment, vendor pages settle what each vendor offers for document analysis, web research, usage limits, and source checking, but not whether an assistant will give a correct investment conclusion. Chat Picker has not tested ChatGPT, Claude, or Gemini for this use, and this page is not financial advice.

What the vendors document

The vendor figures below were read on October 1, 2026.

OpenAI’s ChatGPT Plus help page lists Plus at $20 per month. Anthropic’s Claude Pro help page lists Pro at $20 per month in the US, while Google’s US AI plans page lists AI Pro at $19.99 per month with 4x Free usage. Those are listed subscription prices, not performance claims. Consumer subscriptions also are not API quotes: Plus excludes API usage, while Claude Platform pricing and Gemini API pricing charge separately for API use.

For document-heavy work, OpenAI’s pricing page lists app context windows of 27K on Free Instant, 54K on Go and Plus Instant, 128K on Pro Instant, and 256K on Go and Plus or 400K on Pro for reasoning. Claude’s pricing page says context can reach 1M on every plan and varies by model. Google’s Gemini Apps limits page lists 32K without a paid AI plan, 128K on AI Plus, and 1 million on AI Pro and Ultra. OpenAI’s page gives no exact message counts; Claude describes five-hour and weekly limits, while Google says limits refresh every five hours until a weekly limit is reached.

For source work, OpenAI’s Plus page lists file uploads, analysis, and Deep Research where available. Claude’s pricing page includes web search, file creation, and code execution on Free. Gemini’s source-display help page says a Sources button can connect a response to public sites, uploaded files, or connected Google Workspace documents. These are routes to evidence, not proof that every claim is supported.

On model training, OpenAI’s pricing page lists an opt-out for Free, Go, Plus, and Pro; Anthropic’s pricing page lists one for Free, Pro, and Max; the Gemini plans page read did not state a chat-training setting. For teams, OpenAI says Business workspace data is not used for training, while Claude’s pricing page says Team is not trained on by default.

The accuracy notes are equally important. OpenAI says ChatGPT can be incorrect or misleading, may sound confident when wrong, and should be treated as a first draft with important information checked against reliable sources. Anthropic says incorrect or misleading output is hallucination, advises against relying on Claude as the only source of truth, and says to inspect cited sources and original context, especially for high-stakes advice. Gemini’s guidelines say training-data limits can contribute to limited viewpoints or overgeneralizations, and that Gemini should not produce factually inaccurate outputs that could cause significant financial harm. Anthropic says elevated-risk uses should include relevant human expertise; OpenAI’s usage policies say its rules do not replace professional duties and prohibit automated high-stakes decisions in sensitive areas without human review. These warnings do not supply an investment-risk score.

What the documentation cannot tell you

Vendor documentation cannot show how an assistant handles your actual filing set, transcript language, ratio definitions, or conflicting evidence. It also cannot establish completeness, stable conclusions, acceptable review time, or the full cost of your workflow. A citation panel shows that links are available; it does not show that each link supports the sentence beside it. Long context is not a reliability guarantee: Anthropic’s context documentation says accuracy and recall can degrade as token count grows.

How to check it yourself

Run the same packet through each product. Use identical source files and prompts; record the product, plan, region, date, and available tools; start fresh chats; and retain both answers and source panels. Record corrections rather than treating a fluent answer as a pass.

Filing signals. Upload identical 10-K and 10-Q pages. Prompt: “Extract revenue, operating income, operating cash flow, total debt, and risk-factor headings. Pair each item with filing, page, period, unit, and supporting quotation; write NOT FOUND if absent.” Look for traceable quotations and correct units. Record omissions and corrections.
Earnings-call tone. Upload the same transcript excerpt. Prompt: “Classify statements about demand, pricing, margins, and guidance as positive, neutral, negative, or mixed; quote exact words, keep speaker labels, and separate fact from interpretation.” Look for unsupported labels. Record every classification change and why.
Ratio checks. Upload a consistent statement table. Prompt: “Calculate current ratio, operating margin, debt-to-equity, and free-cash-flow conversion. Show each formula, numerator, denominator, period, and missing input; never replace missing data with zero.” Compare arithmetic, units, and aligned periods. Record numerical differences.
Macro and industry context. Attach dated source excerpts. Prompt: “Separate sourced facts from interpretation, link each macro or industry point to the business driver, and state uncertainty or conflicting evidence without inventing a forecast.” Look for date and source matches. Record unsupported links.
Model and cost choice. Attach a spreadsheet with measured task volume, input and output token counts, subscription cost, API charges, and review time. Prompt: “Compare app and API workflows under these actual constraints; identify assumptions, plan limits, and review steps before selecting a design.” Record total spend, interruptions, review time, and reasons.
Hallucination control. Use the same packet with a source list and a deliberately inconsistent copy. Prompt: “Create a claim table; attach support, flag contradictions and missing evidence, and separate quoted fact from inference.” Look for citations that genuinely support claims. Record unsupported claims and mismatches.

Which rows of the comparison matter

In the Claude vs ChatGPT vs Gemini matrix, start with Free plan, Low-cost tier, Main paid plan, Heavy-use plans, and Team plan. Then check Context window in the app, How usage limits are described, Training on your chats, and Ads. Add API prices if you are building a workflow.

Price rows reveal billing, not value; context rows reveal capacity, not extraction accuracy; training rows matter for confidential material; and usage rows matter when you process batches. Each matrix figure carries its vendor source and read date. Confirm the vendor page before subscribing because plans and local prices change. Leave “Not verified” cells blank.