How

AI Tools for Portfolio Optimization and Risk Analysis

Vendor documentation establishes plan limits and general assistant features, but it does not establish portfolio-method accuracy or results for a particular portfolio.

Sources checked 2 Oct 2026

For portfolio optimization and risk analysis, the vendors document general plan features, upload limits, and accuracy cautions—not validated performance on methods such as mean-variance optimization, Monte Carlo simulation, or risk decomposition. As read on October 1, 2026, OpenAI’s ChatGPT pricing page, Claude’s pricing page, and Google’s US Google AI plans page provide the documented details below. Chat Picker has not tested ChatGPT, Claude, or Gemini for this use, so this page makes no result claim and is not financial advice.

What the vendors document

As read on October 1, 2026, OpenAI’s ChatGPT pricing page lists unlimited text chats on Free, with limited uploads, images, voice, and deep research. It gives app context windows of 27K for Free instant models, 54K for Go and Plus instant models, 128K for Pro instant models, 256K for Go and Plus reasoning models, and 400K for Pro reasoning models. Exact message counts are not published there. The page says a training opt-out is available on Free, Go, Plus, and Pro.

As read on October 1, 2026, Claude’s pricing page lists web search, file creation, code execution, and memory on Free, with no listed tier between Free and Pro. Pro is $20/month when billed monthly or $17/month with annual billing. The page gives an app context of up to 1M on every plan, varying by model. It describes more usage on Pro, while Max offers 5x or 20x Pro usage; message counts are not published. A training opt-out is available on Free, Pro, and Max, and Team is not trained on by default.

As read on October 1, 2026, Google’s US Google AI plans page lists 15 GB of storage on Free. Google AI Plus is $4.99/month with 2x the Free usage limits and 400 GB of storage; Google AI Pro is $19.99/month with 4x the Free limits and 5 TB. It describes compute-based limits that refresh every 5 hours up to a weekly limit. The page gives file uploads of up to 1,500 pages but no app token figure. It does not state a setting for training on chats, so Chat Picker leaves that comparison unverified.

The vendors’ accuracy guidance matters just as much as their feature lists:

OpenAI’s accuracy guidance says ChatGPT can produce incorrect or misleading outputs and may sound confident when wrong. OpenAI recommends treating it as a first draft and checking important information against reliable sources.
Anthropic’s Claude accuracy guidance says Claude can make convincing statements that are not grounded in fact. It advises scrutinizing high-stakes advice, reviewing cited sources, and checking original pages for missing context rather than relying on Claude as the only source of truth.
Google’s Gemini safety guidelines describe large language models as probabilistic and note that Gemini may produce overgeneralizations or outputs that violate its guidelines. The policy says Gemini should not generate factually inaccurate outputs that could cause significant financial harm, but a policy instruction is not evidence that a particular answer is correct.

What the documentation cannot tell you

The vendor pages we read do not say how any assistant performs on mean-variance optimization, reinforcement learning, Monte Carlo simulation, sparse factor regression, variational-autoencoder scenario generation, or Shapley risk decomposition.

Your own trial is where you check whether an assistant preserves units and dates, respects portfolio constraints, includes transaction costs, avoids look-ahead bias, produces stable factor estimates, and reconciles risk contributions. Documentation alone cannot establish those properties for your data. The previous version of this page included unsourced statistics and test results; they are not carried forward.

How to check it yourself

Use the same anonymized workbook, named portfolio.xlsx, for every run. Include the holdings, return history, transaction costs, factor exposures, scenario fields, and constraints you actually impose. Keep the file and prompt unchanged when comparing plans, and require the assistant to identify missing inputs rather than invent them.

Mean-variance optimization. Give it: “Analyze portfolio.xlsx. Show the expected-return and covariance inputs, objective, constraints, solver assumptions, and validation checks. If an input is missing, stop and list it rather than inventing a value.” Look for explicit units, aligned dates, and feasible constraints. Write down every assumption, missing field, and warning.
Reinforcement learning for dynamic rebalancing. Give it: “Design a dynamic rebalancing policy using the return and transaction-cost fields in portfolio.xlsx. Define the state, action, reward, constraints, training and validation separation, and a benchmark; do not claim backtest performance that the file does not establish.” Look for look-ahead leakage, turnover, costs, and reproducibility. Record each leakage control and baseline.
Monte Carlo simulation with neural acceleration. Give it: “Specify a Monte Carlo simulation for portfolio.xlsx and a neural-network surrogate. Define dependence, inputs, approximation error, tail checks, and validation; do not generate performance figures.” Look for how dependence and extreme scenarios are preserved. Write down the proposed error measures and validation method.
Sparse regression for factor estimation. Give it: “Use the factor exposures and returns in portfolio.xlsx to specify a sparse regression. Define the outcome, predictors, regularization, selection rule, validation split, and stability checks; do not invent coefficients.” Look for look-ahead leakage, unstable selections, and multicollinearity handling. Record the assumptions behind each modeling choice.
Variational-autoencoder scenario generation. Give it: “Design a variational-autoencoder scenario generator using only the scenario fields in portfolio.xlsx. Define preprocessing, training objectives, validation, constraints, and an unconditional baseline; label every generated scenario as synthetic.” Look for dependence, tail behavior, leakage, and invalid financial values. Write down the acceptance checks before inspecting any output.
Shapley risk decomposition. Give it: “Define a Shapley risk decomposition for the positions and risk factors in portfolio.xlsx. Define the risk measure, players, characteristic function, computation method, units, and error checks; stop if required risk inputs are absent.” Look for additivity, sign conventions, sampling error, and unsupported causal claims. Record whether the method reconciles to the stated total risk.

Which rows of the comparison matter

In the Claude vs ChatGPT vs Gemini comparison matrix, start with Free plan, Main paid plan, Context window in the app, How usage limits are described, and Training on your chats. Add Low-cost tier, Heavy-use plans, or Team plan when budget, usage volume, or collaboration makes them relevant.

Each matrix figure carries its vendor source and read date. When a figure cannot be confirmed, the cell is marked “Not verified” and left blank rather than estimated. Confirm prices on the vendor page before subscribing.