I tested Claude and ChatGPT on 50 pitch decks. Here's what they saw.

I uploaded the same 50 seed-stage pitch decks to Claude 3.5 Sonnet and ChatGPT-4o. Same decks, same scoring rubric, same 10-point scale. The average score difference was 14 points. On six decks, the delta was over 30 points.

This is not a "which model is better" piece. Both models are excellent at reading documents. But they see different things when they read the same deck, and if you are raising right now, those differences matter.

The setup

I took 50 pitch decks from founders who gave permission to use their materials. All were seed-stage companies raising between $500K and $3M. All decks were 10-15 slides in PDF format. Half had raised successfully. Half had not.

I fed each deck to both models using identical prompts. The prompt asked each model to score the deck on ten dimensions: problem clarity, market size evidence, competitive positioning, team credibility, traction specificity, ask clarity, use-of-funds breakdown, visual design, narrative flow, and investor fit signals. Each dimension scored 0-10. Total possible score: 100.

I did not tell either model which decks had raised and which had not. I wanted to see what each model flagged as strong or weak, independent of outcomes.

What Claude noticed

Claude scored decks an average of 8 points lower than ChatGPT. The median Claude score was 64. The median ChatGPT score was 72.

Claude was consistently harder on vague market sizing. When a deck said "$10B market" without a source or a build-up, Claude docked points. When a deck cited a Gartner report or showed a bottoms-up TAM calculation, Claude rewarded it. ChatGPT was more forgiving of unsourced claims.

Claude also flagged narrative gaps more aggressively. One deck opened with a problem slide, then jumped straight to product screenshots. No transition. No "here is why existing solutions fail" slide. ChatGPT gave the deck a 78. Claude gave it a 61. In Claude's feedback: "The deck does not explain why the status quo is insufficient. The product appears in slide 4 without setup."

Claude penalized decks that buried the ask. If the funding amount or use-of-funds breakdown appeared after slide 12, Claude marked it as a structural flaw. ChatGPT rarely mentioned slide order as a weakness.

Claude also cared more about team slides. If a team slide listed titles but not prior outcomes ("VP of Engineering" but not "built the data pipeline at Stripe that processed $40B"), Claude scored it lower. ChatGPT gave points for titles alone.

What ChatGPT noticed

ChatGPT scored decks higher on average, but it was not just grade inflation. ChatGPT rewarded different things.

ChatGPT cared more about visual polish. Decks with clean typography, consistent color schemes, and professional layouts scored 6-9 points higher with ChatGPT than with Claude. Claude mentioned design in its feedback but rarely weighted it heavily. ChatGPT often opened its feedback with design observations.

ChatGPT also gave more credit for aspirational language. One deck described its product as "the operating system for climate adaptation." Claude flagged this as vague. ChatGPT called it "a clear and compelling positioning statement." When founders used big, future-oriented framing ("we are building the X for Y"), ChatGPT scored it as strong positioning. Claude often asked for evidence that the comparison was earned.

ChatGPT was also more lenient on traction slides. If a deck showed a graph with an upward slope, ChatGPT scored it well even if the Y-axis was unlabeled or the time period was unclear. Claude consistently flagged unlabeled axes and missing denominators ("20% month-over-month growth" without clarifying the base number).

ChatGPT gave higher scores to decks that included quotes from customers or partners, even if the quotes were generic ("This product is a must-have for our team"). Claude wanted specificity in testimonials. One deck included a quote: "We saved 15 hours per week using this tool." Claude scored it higher than a deck with "This tool is amazing!" ChatGPT scored both similarly.

The decks where both models agreed

There were 12 decks where Claude and ChatGPT scores were within 5 points of each other. These decks shared three traits.

First, they had clear problem-solution-traction arcs. The problem was specific. The solution was tied directly to the problem. The traction slide showed that real people were using the solution to solve the problem. Both models rewarded narrative clarity.

Second, they included specific numbers on every slide that made a claim. Not "large market" but "$4.2B spent on X in 2024, growing at 18% annually per Forrester." Not "fast growth" but "340 users in Q1, 890 in Q2, $47K MRR as of last week." Both models liked specificity.

Third, they had clean, simple designs. No 8-point font. No clipart. No walls of text. But also no over-designed slides with complex graphics that obscured the point. Both models rewarded clarity over decoration.

The 12 high-agreement decks had an average score of 76 on Claude, 79 on ChatGPT. Ten of the twelve had raised successfully.

The decks where the models diverged most

Six decks had score deltas over 30 points. I looked at those six carefully.

Deck 1: A consumer social app. ChatGPT gave it an 81. Claude gave it a 48. The deck had beautiful design, aspirational language ("the future of how Gen Z connects"), and a competitive slide that positioned the product against Instagram and Snapchat. ChatGPT loved the positioning and the design. Claude flagged the lack of evidence for the competitive claims, the absence of a go-to-market strategy, and the vague traction slide ("10K downloads in the first month" with no context on retention, engagement, or monetization). This deck did not raise.

Deck 2: A B2B SaaS infrastructure tool. Claude gave it an 82. ChatGPT gave it a 51. The deck was text-heavy, visually plain, and full of technical jargon. But it had a bottoms-up market size calculation, a detailed competitive matrix with specific feature comparisons, a traction slide with logos, MRR, net revenue retention, and payback period, and a use-of-funds table that broke down hiring, infrastructure costs, and sales spend by quarter. Claude rewarded the specificity. ChatGPT docked points for poor visual design and dense slides. This deck raised $2M.

Deck 3: A climate tech hardware company. ChatGPT gave it a 77. Claude gave it a 44. The deck had sleek renderings of the product, a bold mission statement, and endorsements from two recognizable advisors. ChatGPT scored it highly on vision and design. Claude flagged the absence of a technical risk discussion, the lack of unit economics, and the vague timeline ("pilot in 2025" with no month or customer named). This deck did not raise.

The pattern: ChatGPT rewarded decks that looked and sounded like venture-backable companies. Claude rewarded decks that provided evidence that they were venture-backable companies.

What this means if you are raising now

If you are using AI to score your deck or draft your pitch, the model you choose will shape the feedback you get.

If you run your deck through ChatGPT and get a high score, check whether that score is driven by design and framing, or by substance. ChatGPT will tell you if your deck looks good. It will not always tell you if your deck answers the questions an investor will ask in the first meeting.

If you run your deck through Claude and get a low score, read the feedback carefully. Claude often flags gaps that will come up in diligence. If Claude says your market size is unsourced, an investor will notice. If Claude says your traction slide is missing context, an investor will ask for it.

Both models are useful. But they are useful for different stages of the process.

Use ChatGPT to refine positioning, improve design, and make sure your deck is readable. Use Claude to stress-test your narrative, find logical gaps, and identify the questions you have not answered yet.

Or use both. Run your deck through both models, compare the feedback, and fix the issues that both models flag. The areas where Claude and ChatGPT agree are almost always the areas where your deck is weakest.

The model does not matter as much as the rubric

The bigger takeaway: neither model is "seeing" your deck the way a human investor sees it. Both models are pattern-matching against the rubric you give them.

When I changed the rubric to weight design more heavily, ChatGPT scores stayed roughly the same, but Claude scores shifted up by an average of 7 points. When I changed the rubric to penalize unsourced claims more heavily, Claude scores stayed the same, but ChatGPT scores dropped by an average of 9 points.

The model is less important than the instructions you give it. If you are using an AI tool to score your deck, ask what rubric it is using. If the rubric rewards things that do not matter to investors (visual polish, aspirational language, brand-name comps), the score will be misleading.

If the rubric rewards things that do matter (specificity, narrative clarity, evidence of traction, realistic market sizing), the score will be useful.

What I would do differently next time

I would add a third test: show the deck to five investors and ask them to score it on the same rubric. Then compare human scores to model scores.

I would also test decks at different stages. All 50 decks in this test were seed-stage. I expect the delta between Claude and ChatGPT would be smaller for Series A decks (more data, less vision) and larger for pre-seed decks (more vision, less data).

I would also track which specific feedback led to changes that improved outcomes. Scoring is useful, but the feedback is what matters. If a founder changes three things based on Claude's feedback and then raises, those three things are worth documenting.

I am running a follow-up test with 100 decks, evenly split between pre-seed, seed, and Series A. If you want the data when it is ready, email me at hello@claudefundraiser.com and I will send it.

Why this matters beyond pitch decks

This test was about pitch decks, but the lesson applies to any task where you are using AI for feedback.

Different models notice different things. If you are using AI to draft cold emails, review financial models, or write investor updates, the model you choose will shape the output. Claude tends to prioritize logical rigor and specificity. ChatGPT tends to prioritize fluency and polish.

Neither is better in every case. The right model depends on what you are optimizing for.

If you need something that sounds good, use ChatGPT. If you need something that holds up under scrutiny, use Claude. If you need both, use both.

If you are raising right now and want to see how your deck scores with both models, you can upload it here and get feedback in 30 seconds. The tool runs your deck through Claude's scoring rubric (the one that flagged the gaps in the decks that did not raise) and surfaces the specific slides that need work. It also shows you which investors match your stage, sector, and geography, so you are not pitching funds that will never write the check.