On May 7, 2026, I gave the same retirement-planning prompt to Claude, ChatGPT, and Gemini. The setup: 35 years old, $50,000 saved, $80,000 salary, target retirement at 65, all the standard real-return assumptions baked in. Three different AIs. Three different responses. One got the math right, walked through every step, and flagged the single risk that most retirees underestimate. One returned a partial answer drowned in citation badges and stopped halfway. One built a clean fiduciary-style plan with the same end answer but less depth on risk. This article is the head-to-head, with the actual outputs.
Choosing the right AI for retirement planning is like choosing a financial planner. They will all give you an answer. The right one for you depends on what you actually need: a precise calculation, a plain-English plan, a list of action steps, or a risk audit. After running the same prompt on all three, the answer is not which one is best overall. It is which one is best for which step in the planning process. The smart move is to know which to reach for when.
This guide shows the test prompt, the three responses, the scoring across math, completeness, citations, and risk awareness, and a workflow for using all three together. Real outputs from this morning. No hype.
The Test Prompt
Identical wording sent to all three within a 10-minute window on May 7, 2026:
Act as a fee-only fiduciary financial planner. I am 35 years old with $50,000 saved across all accounts, earning $80,000 gross annually with a 5% annual raise expected. I want to retire at 65 with the same lifestyle. Assume: inflation 2.5%, pre-retirement portfolio return 7% real, post-retirement return 4% real, safe withdrawal rate 4%, current annual spending 80% of gross income. Calculate target portfolio, monthly savings required, asset allocation, generic tax-advantaged strategy, and one overlooked risk. Show your math. Cite sources. Under 350 words.
The prompt forces five outputs. The math is testable. The asset allocation reveals the AI's risk philosophy. The tax-advantaged section shows whether the AI defaults to country-specific accounts (like 401k or ISA) or generic principles. The overlooked-risk question reveals depth. Same input, three personalities.
Why a Retirement Plan Is the Hardest AI Test
Three reasons retirement planning is harder for an AI than stock research.
First, the math compounds. A small assumption error in year one becomes a large dollar error by year 30. An AI that returns the wrong target portfolio number misleads everything downstream. Stock research can absorb a 10% error on a single quarter's revenue figure. A retirement plan that overstates the target by 20% sends a 35-year-old saving the wrong amount for three decades.
Second, the answer requires both calculation and judgment. The 4% safe withdrawal rule is a defensible default but assumes a US 60/40 portfolio over a 30-year horizon. A 35-year-old planning a 30-year retirement starting at 65 actually has a 30+ year planning horizon for the assets to last, so the standard 4% may need adjustment. An AI that doesn't surface that nuance is giving you a calculator output, not a plan.
Third, the answer is global by necessity. Retirement accounts vary by country: 401k and IRA in the US, ISA and SIPP in the UK, Superannuation in Australia, generic pension and individual savings in most of the EU. An AI that assumes US-only structure fails 80% of readers. The right answer is to abstract to the principles (employer-match, tax-deferred, tax-free, taxable) and let the reader translate to local accounts.
Target portfolio: $1,600,000 in today's dollars. All three AIs got this. The differences started right after.
Side-by-side scoring of Claude, ChatGPT, and Gemini on the identical retirement-planning prompt.
How Each AI Actually Responded
Claude (Anthropic)
Length: ~1,810 characters. The most complete response. Claude returned the full math chain: annual spending $64,000 (80% of $80K), target portfolio $1,600,000 ($64,000 / 4%), existing $50,000 grows to $380,613 over 30 years at 7% real, gap to fill $1,219,000. Asset allocation glide path from age 35 (90% equity, 10% bonds) to 65 (60% equity, 40% bonds). Tax strategy: max employer-match first, then tax-deferred up to limit, then tax-free, then taxable, with a 6-month cash emergency fund outside all of them. Most overlooked risk: sequence-of-returns risk, with three concrete mitigations (2-3 year cash buffer, flexible withdrawals, delay discretionary spend in down years). Sources cited: Bengen 1994, Trinity Study 1998, Shiller/Damodaran for return data. Best for: building the actual plan you can act on.
ChatGPT (OpenAI)
Length: ~240 characters of usable text before the citation panel took over. ChatGPT started a clean answer ("Target retirement portfolio: $64,000/year using 4% safe withdrawal rule") then surfaced four citation badges (Trinity Study, Morningstar, Vanguard, Federal Reserve) inline and effectively stopped writing. The answer never reached the savings calculation, allocation, tax strategy, or risk section. This is a known failure mode: when ChatGPT activates web search on math-heavy multi-section prompts, the citation insertion can interrupt the response. The workaround is to disable web search and re-prompt, but that defeats the source-citing requirement. Best for: prompts where you want sources but can tolerate retries.
Gemini (Google)
Length: ~1,820 characters. Solid fiduciary-tone response. Math correct: $64,000 spending, $1,600,000 target. Walked through portfolio calculation with the safe withdrawal rule. The plan structure was cleaner than Claude's, with section headers and a numbered steps format. Gemini's tax-advantaged section listed accounts more explicitly (employer plan, individual retirement account, tax-free options, brokerage). Slightly thinner on risk discussion than Claude. Did not surface sequence-of-returns risk by name, instead flagging "market volatility in early retirement years" which is the same idea expressed less precisely. Best for: clean readable plans you would email to a spouse.
Scoring Across Five Dimensions
- Math accuracy: Claude correct, Gemini correct, ChatGPT correct on what it produced (truncated).
- Completeness: Claude covered all 5 sections, Gemini covered all 5, ChatGPT covered ~1 of 5.
- Citation quality: Claude cited Bengen + Trinity + Shiller + Damodaran with years, Gemini cited fewer specific sources, ChatGPT bombed citations inline at the cost of completing the answer.
- Risk awareness: Claude named sequence-of-returns risk and three mitigations, Gemini implied it, ChatGPT did not reach this section.
- Action-readiness: Gemini's structured headers were the most copy-pasteable, Claude's was the most defensible, ChatGPT's was unusable as standalone.
Claude won on completeness and depth. Gemini won on readability. ChatGPT failed on this prompt - try re-prompting with web search off.
Decision matrix: which AI to reach for given the actual retirement-planning question you are trying to answer.
How to Use All Three Together: The Retirement Planning Stack
The smart workflow for retirement planning is not picking one AI. It is mapping each question to the model that handles it best.
Step 1: Establish the target with Claude
Use Claude for the foundational calculation. Target portfolio, required savings rate, asset allocation by age, sequence-of-returns risk awareness. Claude is the most thorough on the math chain and the risk discussion. Save the response as the baseline plan.
Step 2: Reformat for shareability with Gemini
Paste Claude's plan into Gemini and ask: "Reformat this as a 5-section retirement plan I can email to my spouse, with a one-page summary at the top." Gemini's structural editing is the strongest of the three. The output is a polished document.
Step 3: Stress-test specific assumptions with ChatGPT
Ask ChatGPT (with web search disabled) to stress-test individual assumptions: "Run this same plan with 5% real return instead of 7%, then with 3% inflation instead of 2.5%, then with retirement at 60 instead of 65. Show how the target and savings rate change." ChatGPT excels at quantitative scenario analysis when the math is bounded.
Step 4: Pull country-specific tax accounts with Perplexity
Optional fourth model. Ask Perplexity: "For [your country], what specific tax-advantaged retirement accounts apply, with current contribution limits, citing the official tax authority." Perplexity's citation discipline is the strongest, and tax-account specifics need verifiable sources.
Common Mistakes That Cost You
Mistake 1: Asking for a Single Number
"How much do I need to retire?" returns a vague answer. Specify: target spending, retirement age, return assumptions, withdrawal rate. The prompt structure determines the answer quality. Vague prompts make even the best AI return useless plans.
Mistake 2: Trusting the AI's Default Assumptions
Each AI has a slightly different default for inflation, real return, and life expectancy. If you do not specify them, the AI fills them in. Three different AIs with three different defaults will produce three different target numbers from the same person. Always specify your assumptions explicitly.
Mistake 3: Ignoring Sequence-of-Returns Risk
This is the single most underestimated risk in retirement planning. A bear market in the first 5 years of retirement can permanently damage a 4% withdrawal plan even if long-term average returns are fine. Mitigations: keep 2 to 3 years of expenses in cash and bonds, use flexible withdrawal rates, delay big discretionary spending in down years.
Mistake 4: Treating the AI's Plan as Investment Advice
None of these AIs are licensed fiduciaries. The output is a framework, not a personalized recommendation. Take the framework to a fee-only human planner once you are within 10 years of retirement, when the stakes shift from accumulation to distribution. The AI is the first draft. The human is the final review.
Mistake 5: Forgetting the Country-Specific Tax Layer
All three AIs default to generic principles in this test because the prompt asked for global framing. In reality, your local tax-advantaged accounts (employer plan match, individual retirement, tax-free) determine 20% to 40% of your final outcome. Always run a second prompt for your country specifically.
Frequently Asked Questions
Why did ChatGPT fail on this prompt?
ChatGPT activates web search by default for prompts that mention specific assumptions or rules. The web search inserts citations inline, which on long math-heavy prompts can interrupt the response generation. Disable web search and re-prompt, or use the GPT-5 model variant which handles longer multi-section answers better.
Should I trust the 4% safe withdrawal rule?
It is a defensible starting point based on the Trinity Study (1998) and Bengen (1994), both cited by Claude in the test. It assumes a 30-year retirement horizon and a 60/40 portfolio. For longer retirements (40+ years for early retirees) or different portfolios, the rate adjusts down to 3% to 3.5%. Always stress-test with at least one alternative withdrawal rate.
How often should I re-run the plan?
Annually after each tax year, plus once after any major life event (marriage, child, job change, inheritance, large debt payoff). The math compounds across decades, so small assumption changes have outsized impact when caught early.
Can I share my actual portfolio with the AI?
Yes, but limit to ticker symbols and dollar amounts. Avoid sharing account numbers, personal identifiers, or institution names. The AI will give you concentration analysis, sector tilt, and rebalancing suggestions from the holdings list alone.
What to Watch Next
- v Does ChatGPT ship a fix for the citation-interruption failure mode within the next 6 months?
- v Does Gemini's Google Sheets integration mature into a real retirement-planning workbook by year-end?
- v Does any AI vendor launch a finance-specific model variant trained on planner workflows?
- v Does your own retirement plan re-run produce a target within 5% of your original each year (a sign of stable assumptions)?
- v Does sequence-of-returns risk show up in financial press more often as Boomers retire en masse?
Key Takeaways
- Same retirement prompt, three AIs: Claude best on completeness and risk depth, Gemini best on readability, ChatGPT failed on this prompt due to citation interruption.
- All three converged on the same target ($1,600,000) when the math reached the answer.
- Claude surfaced sequence-of-returns risk by name with three concrete mitigations. Gemini implied it. ChatGPT did not reach that section.
- Use Claude for the foundational plan, Gemini for shareable formatting, ChatGPT for scenario stress-tests with web search off, and Perplexity for country-specific tax accounts.
- Always specify your assumptions explicitly. Vague prompts produce vague plans across all three AIs.
- The AI's plan is the first draft. A fee-only human planner reviews the final draft within 10 years of retirement.
References
Trinity Study (Cooley, Hubbard, Walz, 1998): aaii.com Trinity Study
Bengen 4% Rule original paper (1994): retailinvestor.org Bengen
Damodaran historical equity returns: pages.stern.nyu.edu/~adamodar
Claude AI: claude.ai
ChatGPT: chatgpt.com
Google Gemini: gemini.google.com