I used 3 reasoning models to debug 40 Shopify checkout errors: which one per bug type

The forty bugs that made me test three models

I run technical support for a Shopify agency in Bristol. We manage checkout flows for about thirty UK and Irish stores—fashion boutiques, supplement brands, a couple of artisan food shops. When a checkout breaks, the client notices immediately: abandoned carts spike, payment failures double, someone rings at 7 p.m. in a panic because they've just lost twelve orders in an hour.

Over three months this winter I logged every checkout bug we fixed. Forty in total. Payment gateway timeouts, VAT calculation errors that charged Irish customers the wrong rate, Royal Mail shipping logic that offered next-day delivery to the Outer Hebrides when the client only ships there on Tuesdays. The usual chaos.

I'd been using Claude Sonnet 4.5 for all of them—paste the error log, describe the symptom, get a diagnosis, fix it. Worked fine, but it's one of the pricier models on Kryotta, and I kept hearing that Gemini and DeepSeek were faster for structured debugging. So I re-ran a sample of those forty bugs through all three reasoning models to see which one actually found the problem fastest, and whether the cheaper options held up.

This isn't a rigorous benchmark. It's one developer, forty real bugs, three models, and a lot of notes in a spreadsheet. But the patterns were clear enough that I changed which model I reach for first, depending on the bug type.

Payment gateway timeouts: Claude won by a mile

Eight of the forty bugs were payment gateway timeouts—Stripe or PayPal, customer clicks "Pay now", nothing happens for twelve seconds, then a generic error. The logs usually show a 504 or a vague "request timed out" message, and the actual cause is buried three layers down: a webhook that's hitting a rate limit, a currency conversion API that's slow during peak hours, or a custom app that's waiting for a response from a third-party service that's gone to sleep.

I gave each model the same input: the Stripe error log, the Shopify checkout script, and a two-sentence description of the symptom. Claude Sonnet 4.5 found the root cause in every case, usually within the first three paragraphs of its response. It would say something like, "The timeout is happening because your webhook is calling the VAT validation API synchronously, and that API is taking nine seconds to respond during peak hours. Move the call to a background job or switch to async."

Gemini Pro found six out of eight, but it took longer to get there—more exploratory reasoning, more "let's check if it's this… no, probably that" before landing on the answer. DeepSeek R1 found five out of eight and kept suggesting I check the server logs, which is good advice in general but not helpful when the problem is in the Shopify script itself.

When the bug is a race condition or a timing issue, Claude's the one. It's better at holding the whole request flow in context and spotting where things go asynchronous when they shouldn't.

VAT calculation errors: Gemini was faster and cheaper

Twelve bugs were VAT-related. A customer in Dublin gets charged 20% UK VAT instead of 23% Irish VAT. A business buyer in Northern Ireland doesn't see the VAT exemption field. A German customer buying from a UK store during a Boxing Day sale gets charged VAT twice because the discount code applied before the VAT calculation instead of after.

These bugs are almost always logic errors in a Shopify Flow or a custom checkout script—someone wrote if country == "IE" when they meant if country == "IE" and customer_type == "consumer", or the VAT rate table hasn't been updated since Brexit.

Gemini Pro nailed eleven out of twelve. It's very good at spotting logic errors in conditional statements, and it formats its reasoning in a way that makes it easy to scan: "Here's the condition that's firing. Here's the condition that should be firing. Here's the one-line fix." Fast, clear, no extra commentary.

Claude found ten out of twelve, and the answers were more thorough—better explanations of why the logic was wrong—but I didn't need the explanation. I just needed to know which line to change. DeepSeek found nine out of twelve, and a couple of times it suggested rewriting the entire VAT calculation function when the fix was changing one operator.

For logic errors in pricing, tax, or discount code flows, Gemini's my first stop now. It's faster and costs about a third of what Claude does per query.

Royal Mail shipping logic: DeepSeek surprised me

Ten bugs were shipping-related. A customer in the Scottish Highlands selects next-day delivery, pays £8.50, then gets an email two days later saying the item will arrive in five days. A store offers free shipping on orders over £50, but the threshold doesn't account for VAT, so a £48 order with VAT becomes £57.60 and the customer still pays for shipping. An Post tracking numbers don't validate correctly for Irish customers, so the "track your order" link goes to a 404 page.

These bugs are usually in the shipping rate logic—Shopify's built-in rules mixed with a custom app or a third-party service like Shippo or Easyship. The error logs are messy: half the information is in Shopify, half is in the shipping app's dashboard, and you're trying to figure out which rule fired when.

DeepSeek R1 found eight out of ten. It's surprisingly good at tracing through nested conditional logic when the rules are spread across multiple systems. It would say, "Shopify is passing the pre-VAT total to the shipping app, the app is applying the free shipping rule, then Shopify is adding VAT after the fact, so the customer sees the wrong total." Exactly right, and it got there faster than the other two.

Claude found seven out of ten, Gemini found six. Both of them kept suggesting I simplify the shipping logic, which is good advice but not always possible when the client's using a third-party app they can't replace.

If the bug involves data passing between Shopify and another service, DeepSeek's worth trying first. It's free on Kryotta, and it's better at this specific type of problem than I expected.

The other ten bugs: no clear winner

The remaining ten bugs were a mix—JavaScript errors in the checkout UI, a conflict between two apps, a customer's browser caching an old version of the checkout page, one very weird issue where a discount code only worked on Tuesdays because someone had copy-pasted a cron expression into the wrong field.

No model consistently outperformed the others on this grab bag. Claude was better at the UI bugs, Gemini was better at the app conflicts, DeepSeek found the cron expression issue (possibly because it's the kind of absurd edge case that reasoning models are weirdly good at).

Here's the table I actually use now

I don't use Auto routing for debugging because I want to control which model I'm using based on the bug type. But if you're handling a high volume of support queries and you don't want to think about it, Auto routing will usually pick Gemini for structured problems and Claude for anything that needs more context.

What I'd do differently next time

I should've tracked time-to-fix, not just whether the model found the bug. A couple of times Gemini gave me the right answer but phrased it in a way that made me second-guess myself, so I spent an extra ten minutes testing. Claude's answers are more confident, which is useful when you're under pressure and you just need to ship the fix.

I also didn't test these models on bugs that required reading Shopify's API documentation or cross-referencing a changelog. That's a different skill—retrieval and synthesis, not reasoning—and I'd probably reach for Gemini Flash with a long context window for that.

Questions people ask

Do I need to paste the entire error log, or just the relevant lines?
Just the relevant lines, plus enough context to explain what the customer was trying to do. If you paste a 300-line log, the model will spend half its response summarising the log instead of diagnosing the problem.

Which model is cheapest for this kind of work?
DeepSeek R1 is free on Kryotta. Gemini Pro is about a third the cost of Claude Sonnet 4.5 per query. If you're debugging ten issues a day, Gemini will save you £40–60 a month compared to using Claude for everything.

Can I use these models to write the fix, or just diagnose the problem?
All three can write the fix. Claude's code is usually more polished, Gemini's is faster to generate, DeepSeek's is fine but sometimes needs a bit of cleanup. I usually ask for the diagnosis first, check it makes sense, then ask for the code.

What if the model gets it wrong?
It happens. If the first answer doesn't fix the bug, I paste the new error message and ask it to try again. If it's still wrong after two attempts, I switch models or go read the Shopify docs myself.

I've been using Kryotta's model switching for this workflow because I can keep the same conversation open and just change the model in the dropdown if the first one doesn't work. Saves a lot of copy-pasting. If you're debugging Shopify checkouts and you're tired of guessing which model to use, try it for a week and see which one you reach for most often. You'll know by the third bug.