The Tool Desk
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What counts as structured financial model generation?
A useful test asks whether a tool can create or update a coherent workbook whose inputs, calculations, and outputs are connected—not merely suggest formulas or explain finance concepts. Examples include an integrated three-statement operating model, a discounted cash flow (DCF) valuation, a budget or forecast, or a scenario update to an existing template.
These are different workflows. Building a workbook from a blank file tests more than editing an established template, where the sheet layout and conventions already exist. Decide which workflow matters to your team before comparing tools, and define what a successful deliverable must contain.
- Inputs: Source data, assumptions, units, periods, and any explicit treatment of missing or conflicting information.
- Calculations: Correct financial relationships implemented with inspectable formulas and sensible links between sheets.
- Outputs: Clearly labeled results that reconcile to the underlying assumptions and calculations.
- Revision behavior: Correct recalculation when a driver or scenario changes.
A workbook can get a headline value right while still being unsuitable: it may contain hard-coded outputs, broken links, confusing labels, or no usable path from a result back to its assumptions.
#1 Best Overall
How should you design a fair test?
1. Fix the task and test conditions
Write a short test specification for each workflow. Name the artifact, the spreadsheet application, the starting point (blank workbook or existing template), the source files, the prompt, the available time, and the completion criteria. Record each tool and model version, along with relevant settings. Keep these conditions constant when comparing candidates.
Do not let one tool use extra source material, more time, or manual help that another does not receive. If assistance is permitted, define it in advance and record it. Product availability, access controls, data-handling terms, and compatibility may depend on current vendor terms and the organization’s environment; verify those separately rather than assuming the options are equivalent.
2. Use representative cases, not a showcase prompt
Build a set of ordinary and difficult cases from the work your team actually performs. Include multi-sheet dependencies, multiple periods, realistic source documents, nonstandard line items, and at least one scenario change. Include cases with incomplete instructions or conflicting inputs if those occur in practice, and define how a good response should surface uncertainty instead of silently inventing an assumption.
Have qualified finance practitioners author or review the reference workbook and answer key. The reference should identify expected key values and formulas, assumptions, units, period conventions, and acceptable treatment of ambiguous inputs. A single expected number cannot show whether a model reaches it for the right reasons.
3. Repeat each run and preserve the evidence
Run each case more than once to see whether results vary. Keep the original workbooks, prompts, source files, tool settings, completion times, and reviewer notes. If practical, have reviewers score files without knowing which tool produced them. Record incomplete tasks and repairs needed; do not quietly omit failed runs from the comparison.
For a useful report, disclose the test cases, scoring rules, spreadsheet environment, repetitions, and whether results were independently assessed or published by a vendor. A small internal test is evidence about that test set, not a universal product ranking.
What should the scoring rubric measure?
Set the scoring scale and error-severity rules before running the test. Score the dimensions separately so a correct answer cannot conceal a fragile workbook. One practical scale is 0–4 for each dimension, where 0 means unusable or absent, 2 means materially incomplete and needing repair, and 4 means meets the predefined requirement. Define the intermediate scores for your team and attach examples to each case.
| Dimension | What to inspect | Example failure to record |
|---|---|---|
| Output accuracy | Compare key outputs with the reviewed reference, including units, signs, dates, periods, and rounding conventions. | A value appears plausible but uses the wrong period or unit. |
| Formula integrity | Inspect whether formulas are used where appropriate, references point to the intended cells, and patterns remain consistent across periods. | A forecast year is hard-coded while neighboring years use formulas. |
| Financial logic | Check whether statements and schedules link coherently and whether assumptions flow through the model as intended. | A changed operating assumption updates the income statement but not the linked cash flow. |
| Structure and readability | Check whether inputs, calculations, and outputs are discoverable, labeled, and organized for another analyst. | Key assumptions are mixed into calculations without clear labels. |
| Traceability and auditability | Check whether reviewers can trace source data and assumptions, inspect formulas, identify edits, and reproduce results. | A reported figure has no clear link to a source or calculation. |
| Robustness | Change drivers and scenarios, test incomplete instructions, and assess whether recalculations remain coherent. | A formula breaks or an output becomes inconsistent after a driver changes. |
| Presentation and usability | Assess whether an analyst can understand and use the workbook without extensive repair. | Important outputs are difficult to locate or interpret. |
| Operational fit | Assess compatibility with the organization’s spreadsheet environment, access requirements, data-handling rules, governance, and review process. | The workflow cannot be used under the organization’s applicable controls. |
Alongside scores, log the error, its location, severity, reviewer comment, and repair time. A wrong key output, a broken dependency, and an untidy heading are not equivalent failures. Agree in advance which errors make a case an automatic failure, especially if the workbook would otherwise be used for a material decision.
Rank #3
How do you test whether a model really works?
Reconcile important outputs
Compare the workbook with the reviewed reference at the values that matter for the task. Check that periods, signs, units, and assumptions match—not just the final result. Where a value differs, trace the discrepancy through the relevant formulas and inputs before deciding whether it is a harmless presentation difference or a substantive error.
Inspect formulas and dependencies
Review key formulas directly in the spreadsheet. Trace important outputs back to their drivers, and check links between sheets and across periods. In a three-statement model, for example, test whether a changed operating assumption flows to dependent statements and schedules as expected. In a DCF, test whether changing a valuation driver updates the relevant cash flows or valuation outputs rather than leaving stale values behind.
Change assumptions deliberately
Apply a predetermined set of driver changes, such as a different growth assumption or scenario, and compare the resulting workbook with expected behavior. The exact direction and size of a change depend on the model; judge it against the reference logic rather than an intuition that may not fit the task. Also test a boundary or incomplete-input case where relevant, and see whether the workbook or tool makes the limitation visible.
A fluent explanation of a model is not evidence that its workbook logic is correct. The formulas, dependencies, and recalculated outputs need to withstand inspection.
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How should public benchmark results be interpreted?
Benchmarks can help identify what researchers or vendors have tested, but scores are not directly comparable unless the tasks, software environment, scoring method, and version are aligned. End-to-end spreadsheet benchmarks are more relevant to complete workbook workflows than tests of isolated formula questions, yet they still cannot substitute for a test of your own model types.
| Published evidence | What was reported | How to read it |
|---|---|---|
| SpreadsheetBench 2 paper authors (2026) | The paper abstract reports 321 tasks averaging 11.8 worksheets and 593.5 cell modifications per instance. It reports best overall task accuracy of 34.89% and debugging accuracy as low as 12.00% in its results. | These are study-specific results for a business-spreadsheet benchmark that includes financial reports and filings. They do not predict the result for a particular product, organization, or finance workflow. |
| Meridian’s BlueFin benchmark description (2026) | Meridian describes 131 expert-authored tasks and 3,225 rubric criteria, covering integration, auditability, professional structure and formatting, and robustness under changing scenarios and assumptions. | This is the benchmark publisher’s description of its design; use it as context, not as an independent product ranking. |
| OpenAI’s Model ML Composite case study (2026) | OpenAI reports 36% fewer tokens per workbook and 83.3% headline accuracy for a specified Excel workflow and comparison. | This is a vendor-published case study with a defined workflow, not a general-purpose independent comparison. |
| Anthropic’s internal Real-World Finance evaluation (2026) | Anthropic describes roughly 50 investment and financial-analysis use cases across spreadsheets, slides, and documents, assessed with rubrics or preferences for finance knowledge, completeness, accuracy, and presentation. | This is an internal vendor evaluation, not a controlled public head-to-head comparison. |
| FinSheet-Bench authors (2026) | The authors report a highest result of 82.4% across 24 files and say no standalone model configuration in their tested set reached an error level they considered low enough for unsupervised professional finance use. | This is a spreadsheet-reasoning study, not a benchmark of complete workbook generation. Its result should not be read as a universal error rate for AI financial models. |
Financial Models Lab described a comparison design but did not publish comparable scored results because the controlled test could not be executed. That article therefore does not establish a winner. Taken together, these examples show why a benchmark’s task mix and ownership belong beside any score you cite.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which tools should you compare?
Candidate categories include spreadsheet assistants such as Microsoft Copilot in Excel, ChatGPT for Excel, and Claude for Excel, as well as specialist finance-workflow products. The available evidence does not establish comparable current features, eligibility, regional availability, prices, privacy terms, or feature parity across these options, so do not assume that the product names alone make their capabilities equivalent.
Use a common workbook exercise to compare task completion, accuracy, formula behavior, traceability, consistency, and usability. Then verify current commercial and operational terms directly with each vendor: the test can show how a candidate performed on your work, but it cannot establish its current access rules or suitability for your data policies.
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What governance is needed before using generated models?
Keep a qualified person accountable for reviewing material assumptions, formulas, and outputs. Reviewers should challenge unusual results, document accepted changes, and retain enough information to reproduce the result. The depth of control should reflect how the workbook will be used and the organization’s risk profile.
For regulated institutions, apply the rules and controls relevant to the institution and jurisdiction. The U.S. Office of the Comptroller of the Currency’s revised guidance dated April 17, 2026 describes a risk-based approach tailored to an institution’s model-risk profile, size, and operational complexity. Federal Reserve guidance emphasizes technical expertise, critique, documentation, and ongoing monitoring, and notes that generative and agentic AI are rapidly evolving. The Central Bank of the UAE rulebook is jurisdiction-specific; its inclusion of spreadsheet-tool review in independent validation scope should not be treated as a global requirement.
Do not treat an AI-generated workbook as self-validating or use it for a material decision merely because its narrative sounds confident. The required review and controls depend on the task and applicable organizational and regulatory obligations.
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