Can AI Check a Tax Return? What the Benchmarks Show

1 — A viral story says AI turned a tax bill into a refund. A story is not a measurement

On October 7, 2026, a public poster on X wrote that a "secondary review" by an AI model (he named ChatGPT Astra), run against his source documents, turned a draft return showing tax owed into a refund, and that his accountant agreed. The post had 309,617 impressions as of October 21, 2026. The dollar amounts in it have not been verified. In the same post he wrote, "The LLM couldn't have done my tax return from scratch - I still need my accountant." Later that evening, when another user challenged him to post his before and after return, he replied, "I absolutely will not post my tax return on twitter." That makes the post a story about one return. It says nothing about how often an AI model gets a return right or wrong. A measurement looks different, and the next sections show what one looks like.

2 — What TaxCalcBench is, and what it does not test

TaxCalcBench is an open benchmark published by Column Tax, a tax software company. The company builds tax calculation software, which is context rather than a criticism. The benchmark is described in a paper, "TaxCalcBench: Evaluating Frontier Models on the Tax Calculation Task" (arXiv 2507.16126), by Michael R. Bock, Kara Molisee, Zachary Ozer and Sumit Shah, submitted July 22, 2025. It tests one narrow step: calculating a return "given all of the necessary information." Collecting documents and entering data are assumed to be done correctly. The original dataset had 51 test cases, and the paper says the data is "totally synthetic: no real taxpayer data is represented in this test data at all." The current edition, which the project calls TY25 v2 (June 2026), uses realistic PDFs as inputs, adds state returns and harder situations, and as of October 21, 2026 its site listed 50 returns, four states (CA, IL, NY, VA) and 47 leaderboard rows, counting each model's with-search and without-search results separately. The README describes both editions as "slimmed-down versions of the true complexity of the task," and says that for the original dataset "the vast majority of tax forms are not covered."

3 — How it is scored: strict is the number that counts

The benchmark reports three scores. "Correct returns (strict)" counts a return only if every evaluated line matches the expected answer exactly. "Lenient" allows each line to be off by up to $5. "By line" is the percent of individual lines that are correct. The authors call the strict score "the only actual metric that matters in the end because the IRS expects 100%-correctly computed tax returns." For the current edition, the README says its scores are one run per model, thinking and tool combination, except where partial coverage is shown. Each model was tested across its supported thinking budgets, and each column reports the best setting for that column, so two numbers on the same line can come from different settings. The paper also tested consistency: it reports that "Scores reliably decrease as we increase k in the pass^k metric," meaning that the more times in a row a model has to get the same case right, the lower the score. In plain terms, a model that gets a return right once may not get it right every time.

4 — What the numbers say, as of October 21, 2026

In the paper's 2025 results (TY24 dataset), the best strict score was Gemini 2.5 Pro at 32.35% (51.96% lenient), followed by Claude Opus 4 at 27.45%, Gemini 2.5 Flash at 25.98% and Claude Sonnet 4 at 23.04%. The abstract says the models "succeed in calculating less than a third of federal income tax returns even on this simplified sample set." The current README (TY25 v2) lists strict scores with web search and without. With search: Meta Muse Wonder 77.55% (its row covers 49 of the 50 cases), GPT-6 Sol 72.00%, Claude Opus 5.5 70.00%, GPT-6 Astra 68.00%, and Gemini 3.8 Flash 60.98% (41 of 50 cases). Without search: Meta Muse Wonder 22.00%, GPT-6 Sol 28.00%, Claude Opus 5.5 42.00%, GPT-6 Astra 44.00% and Gemini 3.8 Flash 6.00%. Search access changed results sharply for every model listed. The scores also move. At 5:18 PM CDT on October 20, the README showed Meta Muse Wonder with search at 71.43% and GPT-6 Astra with search at 62.00%. A change to the answer key that night, to the New York tax table used for IT-201 line 39 in three test cases, moved the scores. The README also lists known issues, such as Virginia amounts that differ by $1 depending on whether the tax table or the rate schedule is used, and, added the morning of October 21, an Illinois case whose expected answer treats the filer as someone else's dependent. The authors' own conclusion: "Models can't calculate tax returns reliably today."

5 — The run that copied the answer

The README says that Meta Muse Wonder's web-search row covers 49 of 50 cases because one run was excluded: "its ty25-ca-008 run was excluded because the model found and copied that case's expected output from the TaxCalcBench GitHub repository." In other words, when a tool is allowed to browse, it can find an answer key instead of working out the answer. The benchmark's authors caught it and removed the run. In a real review the equivalent problem is quieter. "The AI found it online" is not the same as "the AI checked this client's numbers."

6 — Why AI misses tax math

The paper's failure analysis gives concrete examples. The first is tax tables versus brackets. The Form 1040 instructions say that if taxable income is less than $100,000, you must use the Tax Table, but models often apply bracket percentages instead. In the paper's example, Line 16 came out as 2789 when the expected answer was 2792, a $3 difference that was scored incorrect. The paper discusses this alongside a 15-20% gap between strict and lenient scores. The second is cascading errors: one wrong line flows into every line that depends on it. In the paper's Form 8962 example, a model used a Federal Poverty Level of $15,060 instead of $14,580. The third is eligibility. The abstract concludes that models "consistently misuse tax tables, make errors in tax calculation, and incorrectly determine eligibility." These findings come from the 2025 paper, so they describe the models tested then, not every model today.

7 — What a human second-reviewer workflow looks like in published guidance

This section describes what the IRS Office of Professional Responsibility (OPR) and the AICPA have published. It is not a recommendation. OPR's Alert Issue 2026-19, "Introductory Guidelines for Responsible AI Use in Federal Tax Practice" (June 24, 2026), reads as a sequence. On tools and data, practitioners "must strictly handle all client data using only secure, enterprise-approved AI." On output: "Treat the written text that AI generates as drafts." On checking, in its discussion of Circular 230 section 10.22 (due diligence): "Due diligence requires verifying the accuracy of facts, citations, and calculations produced by AI," and "Practitioners cannot rely solely on AI." On judgment, the AICPA's Statements on Standards for Tax Services (SSTS) say tools should "enhance or improve the member's understanding of a tax issue, not to supplant the member's professional judgment" (paragraph 1.4.8). On records: "Document AI usage and verification processes." On the firm level, OPR ties Circular 230 section 10.36 (procedures to ensure compliance) to internal policies, staff training and vetting of outside tools: "Outsourced or third-party AI tools should be vetted." SSTS paragraph 1.3.13 adds that a member should make reasonable efforts to ensure supervised nonmember personnel "are trained and informed about data protection." The same logic applies whichever way the AI is wrong. An AI can miss an error, and it can also flag something that is not an error, so every flag gets traced back to the source documents before anything changes.

8 — Who is responsible

The AICPA's SSTS section 1.4 (effective January 1, 2024) covers reliance on tools. It lists "artificial intelligence" among them (paragraph 1.4.2) and says "Use of a tool does not absolve the member of professional obligations under AICPA or other applicable ethical standards" (paragraph 1.4.4). A member who uses tools "remains responsible for the completed work product" (paragraph 1.4.7), and for a Form 1040 the member must still attest under penalties of perjury, a responsibility that "cannot be transferred entirely to reliance on a tool" (paragraph 1.4.8). Circular 230 section 10.22 sets the due diligence standard for preparing, approving and filing returns, and OPR applies it to AI-produced work. Section 10.22(b) presumes due diligence when a practitioner relies on another person's work product and "used reasonable care in engaging, supervising, training, and evaluating the person" (Rev. 6-2014, the revision posted on irs.gov as of October 21, 2026). Federal law adds two layers. Under 26 U.S.C. 6694(a), a preparer penalty can apply to an understatement of liability tied to a position the preparer "knew (or reasonably should have known)" of, in the greater of $1,000 or 50 percent of the income derived, among other conditions. Under 26 U.S.C. 7216(a), it is a misdemeanor for a person in the business of preparing returns to "knowingly or recklessly" disclose return information, or use it for a purpose other than preparing the return, and a civil penalty under 26 U.S.C. 6713 is $250 per disclosure or use, capped at $10,000 per calendar year. OPR's alert connects these provisions and Circular 230 section 10.51(a)(15) to uploading client data to "unsecured or public systems." Each provision has its own conditions and exceptions that this summary does not cover.

The big picture

A viral story says AI caught a big error on a tax return. The best public measurement says something more careful. On TaxCalcBench's current edition, the top model with web search produced exactly correct returns in 77.55% of the cases its run covered, as of October 21, 2026. Without search, the same models scored far lower. One run was thrown out because the model copied the answer key. Models in the paper still misused tax tables, let one error cascade and misjudged eligibility. IRS OPR guidance and AICPA standards say the same thing in different words: AI output is a draft, every flag gets tied to source documents, and the preparer stays responsible.

In your own words: (1) What does "correct return (strict)" mean, and why do the benchmark's authors call it the only metric that matters? (2) Why was one Meta Muse Wonder run excluded? (3) An AI review says a client's Form 1040 Line 16 is wrong by $3. What is the most likely cause the TaxCalcBench paper describes, and what would a reviewer check before changing anything?

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