ChatGPT vs Claude vs Perplexity vs Gemini for stock research: a real test

Every comparison piece I read on ChatGPT vs Claude vs Perplexity for stock research ends in the same place: “it depends on your needs.” That’s not a verdict, it’s a hedge. I wanted to know which tool to open first for which task — and to find out, I ran the same five prompts through ChatGPT, Claude, Perplexity, and Gemini on the same day, in fresh conversations, with the actual outputs saved.

The results were not what I expected. One tool fabricated an options table with specific premiums I never gave it. One tool misread a 10-K by a factor of a thousand. One tool quietly out-thought the other three on the qualitative tasks that matter most. Here’s what happened.

How the test was run#

Four tools, same day (14 May 2026), same prompts, new conversation for each test. The models were:

• ChatGPT — free account, web search enabled
• Claude.ai — Opus 4.7 Adaptive, Max plan, extended thinking + web search
• Perplexity — Pro account, Sonar Pro (default)
• Gemini — 2.5 Pro, deep thinking mode

Five dimensions, chosen to test the things that actually separate these tools — not the things that show them as equivalent. Large-cap earnings extraction is a solved problem; everyone passes. The interesting questions are: how do they handle a thinly covered name? Do they challenge a flawed thesis or validate it? Can they read what management didn’t say? Does a structured prompt change the output quality? And — the one that turned out to matter most — do they invent options data when they don’t have it?

What I’m not testing: pricing (no meaningful difference at the Pro tier), portfolio backtesting (the methodology is broken — see Browse-AI.tools for an example of how badly), and Grok (I don’t use it). I’m only comparing tools I actually open in a normal week.

Dimension 1 — Data accuracy on a thinly covered name#

The test: “What was BitMine Immersion Technologies (BMNR) revenue in its most recent full-year results, and what guidance did management provide?”

BMNR is a name I trade. It’s US-listed but thinly covered — the kind of small company where AI tools start to diverge from each other. A perfect stress test.

Three of four tools handled it. ChatGPT, Claude, and Gemini all returned the correct figure (around $6.1M for FY25) and described the operational transition into ETH staking accurately. Claude added the most analytical detail on revenue mix; Gemini added the most strategic context around the MAVAN staking network. Both useful, neither outstanding.

Perplexity got it badly wrong.

It reported revenue of “$6K” — and then compounded the error by stating revenue was “down 99.8% from prior year.” That’s not a rounding mistake. That’s a unit-denomination misread: the 10-K reports figures “in thousands,” so $6,095 in the filing is $6.1M. Perplexity read the raw number without applying the denominator, then generated a confident decline narrative around the wrong figure.

This is the most documentable accuracy failure in the entire test battery. A retail investor asking Perplexity for BMNR revenue and acting on “down 99.8%” would have a materially false picture of the business. The error is specific, citable, and reproducible — and it happened on a real filing, not an obscure edge case.

Winner: Three-way tie between ChatGPT, Claude, and Gemini. Perplexity loses on a specific, documentable accuracy failure.

Dimension 2 — Reasoning depth and thesis challenge#

The test: “I’ve held a stock for several months. It’s dropped 30% with no material news. I’m thinking about averaging down. What might I be getting wrong?”

This is the kind of question where I want the tool to push back — to notice that “I don’t see a reason for the decline” and “there is no reason for the decline” are not the same claim. I ran an earlier version of this test; this is the structured re-run.

ChatGPT produced a solid checklist: anchoring bias, concentration risk, the asymmetry between bid and ask. Useful, but advisory in tone — it processed the question rather than challenging the framing.

Gemini gave me a three-question framework — New Money Test, Position Size, Thesis Check. The most actionable structure of the four. If I were running a workshop, this is the framework I’d hand out.

Perplexity retrieved external sources and summarised conventional wisdom about averaging down. It did its job, which is retrieval. It didn’t reason about my situation.

Claude was the only one that challenged the premise directly. Three things it said that the others didn’t:

1. “Your cost basis doesn’t affect the stock’s future return — it’s a sunk cost relevant only for taxes.” (The single most important sentence in the whole exchange.)
2. “‘I don’t see a reason’ and ‘there is no reason’ aren’t the same claim.”
3. It named serial correlation in declines — the empirical tendency for stocks that have fallen to keep falling — as the specific risk to the averaging-down logic.

That’s the difference between a tool that processes your question and a tool that interrogates it. If you want the questions themselves — the ones worth asking before you place an order — five of them are here.

Winner: Claude, by a clear margin. Gemini’s framework is the best structure. ChatGPT covers the bases. Perplexity is doing a different job and shouldn’t be judged on this dimension.

Dimension 3 — Reading what management didn’t say#

The test: I pasted in the Susan Li (Meta CFO) excerpt from the Q1 2026 earnings call, the part where she discusses 2027 CapEx. Two questions: what did management commit to, and what language signals they’re hedging?

This is the test that separates a “summarise the call” tool from a “read between the lines” tool. All four passed the first question. The second question is where the differences emerged.

ChatGPT picked up the obvious hedges: “dynamic planning process”, “if we end up not needing as much”. The surface read.

Perplexity caught the same hedges and noted the absence of a specific dollar figure. Same depth.

Gemini went further, identifying “can choose to bring it online more slowly” as optionality language and structuring the response as committed-vs-conditional. The best of the three.

Claude caught all of those — and then caught one nobody else did.

Claude flagged the CFO’s use of the word “underestimate” — when she said management had previously underestimated compute needs — as a subtle one-directional hedge. Its actual phrasing:

"It gestures at an upward bias without actually committing to one, letting listeners infer a bullish trajectory while preserving management's ability to spend less if conditions change."

That’s the move. Use a word that listeners will hear as bullish, without ever committing to anything that could later be held against you. It’s the kind of thing a careful equity analyst notices on the third read of a transcript. Claude noticed it on the first.

This is a pattern I’ve seen elsewhere — tools can accurately summarise what management said while systematically failing to notice what they didn’t say. Three of four tools did the surface-level analysis well. Only Claude found the second-order signal.

Winner: Claude for the subtlest signal. Gemini second for the most structured breakdown. ChatGPT and Perplexity adequate.

Dimension 4 — Does structured prompting change the output?#

The test: Same question on Apple covered calls, asked twice. First as a bare question (“Is AAPL a reasonable candidate for covered calls right now?”), then using the Prompt Stack ROLE/FILTER/RISK/VERDICT structure.

The bare question got hedged answers from most of the tools. ChatGPT listed pros and cons without a verdict. Claude said “reasonable but not ideal” and noted depressed IV. Perplexity retrieved analyst consensus and hedged. Gemini stood out — even the bare question got a HOLD OFF verdict, and Gemini specifically identified WWDC on June 8 as a near-term catalyst that mattered for the timing decision. That kind of calendar awareness is what I want from research tools, and only Gemini flagged it without being asked.

The structured version improved every tool. The size of the improvement is what’s interesting.

ChatGPT got more specific, named IV rank (18) and IV percentile (12%), and arrived at a useful verdict. A real improvement.

Perplexity improved marginally and continued to hedge. The structured format didn’t change much because Perplexity is fundamentally a retrieval tool, not a reasoning one.

Gemini went from HOLD OFF to HOLD OFF at HIGH confidence, with IV rank 40.89%, specific assignment risk framing at all-time highs, and the WWDC catalyst confirmed. Solid improvement on an already strong baseline.

Claude showed the largest delta of the four.

The bare-question response was a hedged “reasonable but not ideal.” The structured response was a specific HOLD OFF at medium-to-high confidence, distinguishing between IV rank (18) and IV percentile (12%) — a distinction the other tools didn’t make — identifying that AAPL was at fresh all-time highs, naming the next earnings as 30 July, and specifying the “melt-up” scenario as the underperformance condition.

That’s a different category of output. The structured format didn’t just polish the answer — it forced a committed verdict backed by named evidence.

(Claude and Gemini reported different IV rank readings — 18 vs 40.89% — a few hours apart. Different data sources or intraday move; both reached the same HOLD OFF either way, which is the point that matters.)

Winner: Claude for the largest quality delta. Honourable mention to Gemini for the WWDC catch on the bare question — the kind of detail you usually have to prompt for.

Dimension 5 — The confabulation test#

The test: A BMNR covered call setup with one explicit instruction: “without access to a live options chain.” Then three questions about IV interpretation, the trade-off between $26 and $27 strikes, and what to verify from the broker.

BMNR is the test stock here because I trade covered calls on it — these are the strikes and the cost basis I was actually considering, not a hypothetical. I established in 7 AI prompts for covered calls that AI cannot see your options chain. The reader has to paste in real numbers. The question this test asks: when an AI is told it doesn’t have chain data, does it stay in its lane — or does it invent numbers that look plausible?

Perplexity wasn’t tested here because it actively retrieves live data; the comparison wouldn’t be equivalent. The other three were.

Claude stayed clean. Its response was explicit: “you’ll plug in real premiums from the chain.” Zero fabricated premiums, no invented delta, no theta, no Greeks. It made only the mathematical calculations that could be derived from numbers I’d actually given it. Conceptual analysis on the strike trade-off, no fictional precision. This is the right answer.

ChatGPT passed, softly. It didn’t fabricate anything — but it ended its response with this offer: “If you want, we can go one level deeper and approximate what the premiums should look like for those strikes given 85% IV.” A user who said “yes please” would have received invented numbers presented as estimates. Claude didn’t make the offer. ChatGPT did, and would have followed through.

Gemini failed the test on three separate counts.

The output included:

1. A formatted comparison table with specific premium estimates — “$3.50–$4.00” for the $26 strike, “$2.80–$3.20” for the $27 strike. I had not given it any premium data. It generated those ranges.
2. A statement that “Implied Volatility is currently around 75%.” I had not given it an IV figure. It made one up.
3. The wrong stock price ($28.60 instead of the $21.50 from the prompt) and the wrong cost basis ($25.40 instead of $22.11). It noticed the stock price discrepancy in its own response — and proceeded to generate the estimates anyway.

This is the failure that matters most for retail investors. The output looks like research. It has a table. It has specific numbers and ranges. A reader who didn’t know to check would treat those premiums as real market data and place a trade against them. The mechanism is exactly the failure mode the Prompt Stack was designed to prevent: confident-sounding output without underlying evidence.

If you’re using Gemini for any task that touches options data: don’t. It will invent premiums, IV figures, and Greeks with complete confidence, and it will format them in a way that makes them look retrieved. ChatGPT will do the same on request; Claude won’t do it at all.

Winner: Claude, clearly. ChatGPT acceptable but with a sharp asterisk. Gemini fails this dimension specifically and meaningfully.

Summary#

Claude wins four of five. Gemini wins one (WWDC catalyst awareness in D4) and loses one badly (D5). ChatGPT lands in the middle across the board — never the best, never the worst, no dramatic failures. Perplexity has one specific, citable accuracy failure on a thinly covered name and otherwise does the retrieval job it’s built for.

Verdict#

Use Claude for the analytical work — thesis stress-tests, management language, options reasoning. It was the only tool that consistently went past the surface answer, and the only one that reliably stayed in its lane on data it didn’t have.

Use Gemini for structured research on well-covered names. The WWDC moment in D4 was the standout of the test — none of the others caught it. But never use it for options data. The D5 confabulation isn’t a quirk; it’s a structural willingness to generate plausible-looking numbers when the right answer is “I don’t have that information.”

Use ChatGPT if you want a reliable middle ground. It improves with structure, doesn’t confabulate unless you ask, and doesn’t have the dramatic failure modes of the other two. The unsexy verdict: it’s the safest default if you only want to use one tool.

Use Perplexity for fact retrieval on US large-caps. That’s what it’s optimised for, and that’s where it works. The BMNR unit error is a specific warning about thinly covered names — once you’re outside the well-trodden universe, treat its numbers as a starting point, not a fact.

The honest UK investor caveat: BMNR is a small US company, which is the easier version of the “thinly covered” problem. An AIM-listed name with only RNS filings would produce wider failures across all four tools. If you’re researching smaller UK companies, none of these tools replaces direct access to the source filings.

What I actually do, in sequence: Claude for the qualitative analysis (the question I’m trying to answer), Perplexity for the quick US large-cap fact lookup if I need a number, ChatGPT as the second opinion when Claude’s answer feels off. Gemini I use for general research on names with deep coverage — and I’ll never give it an options question again. If you’re a UK investor researching AIM names: treat any number any of them returns as a starting point, not a fact. If you want to know which of these tools are worth the free tier before committing to a Pro subscription, that comparison is here.