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Is Claude Better Than ChatGPT? Claude vs ChatGPT Compared

Run claude-opus-5-5 and gpt-5 head-to-head on your own prompt, with your own API keys. Real responses, cost, and latency side by side. Free, no signup.

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Ask Claude and ChatGPT the same everyday question with a clear length limit, and the biggest difference in our run was not the advice. It was who followed the instruction. Both gave sound guidance, but ChatGPT ran about three times longer than the limit we set, while Claude stayed close to it, faster and cheaper.

The run

Here is exactly what we sent, so you can see the whole pipeline behind the numbers:

  • Prompt: one everyday question (below), with a hard “under 200 words” limit.
  • Models: Claude claude-opus-5-5 and ChatGPT gpt-5.
  • Settings: each model’s default, temperature 0.7, no system prompt, no extra context.
  • Keys: our own, calling each provider directly. One real run, not an average.

Prompt

A small bakery owner asks: “A customer wants to return a custom birthday cake they already picked up, saying it was ’too sweet.’ They’ve eaten half of it. How should I handle this professionally while protecting my small business?” Give practical, specific advice in under 200 words.

The result, side by side

Claude (claude-opus-5-5)

  • Cost: $0.0095
  • Speed: 5.8 seconds
  • Length: 457 tokens
  • Length limit: close to the 200 words asked for

ChatGPT (gpt-5)

  • Cost: $0.0135
  • Speed: 16.2 seconds
  • Length: 1,338 tokens
  • Length limit: well over it, roughly 3x longer

The actual answers:

Claude (claude-opus-5-5)

Hold your position kindly. Half-eaten custom food can’t be resold, and “too sweet” is a taste preference, not a defect. You’re not obligated to refund.

Offer a small goodwill gesture (optional): 10 to 15 percent off their next order. Document everything, and post a clear written policy customers initial at pickup.

ChatGPT (gpt-5)
Triage first: if it is a true defect, remake or refund; if it is preference, handle as goodwill. Then a full script offering store credit, plus a longer checklist: a printed final-sale policy, an order form with a sweetness checkbox, a pickup sign-off, tastings, batch photos to fight chargebacks, and a one-time polite reply to any public review.

Both reach the same core advice. The measurable difference is that ChatGPT gave you more to work with, and Claude gave you exactly what you asked for, faster and cheaper on this run.

Insight: On a task with a hard length limit, the real test is instruction-following, not advice quality. Here the cheaper, faster answer was also the one that respected the limit. On your own prompts, watch whether a model honors your constraints, not just whether it sounds good.

These results come from a specific run on 2026-11-02 on one defined task, using real models through a real pipeline. They show how each model performed on that task, not a general ranking of model quality, which depends entirely on your task and data. Run your own to see your numbers.

Run it on your own question

The free tool below opens with Claude and ChatGPT ready to go:

  1. Click Launch to open the notebook.
  2. Paste your own question and add your own API keys.
  3. Read both answers side by side with their cost and speed, in about a minute.

Nothing is sent to our servers, and your keys stay on your machine.

Want to go further? At llmprover.pysolvr.com you can score which answer is actually best for your task, compare up to 20 models at once, and see which part of your setup caused a weak answer.

Run as of 2026-11-02. Models and their behavior change often, so run a fresh comparison to see today’s result on your own question.

This free tool is a slice of the full product. With a paid plan you also get:

  • compare up to 20 models at once
  • score which answer is best
  • see which part of your setup caused a weak answer
  • save benchmarks and track changes over time
  • let your AI agent run it for you
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Free tool, provided as-is, no warranty. Uses your own API key. You are responsible for your usage and costs. Keys never leave your machine and are never sent to pysolvr. Outputs are informational, not advice. pysolvr is not liable for decisions made from these results.