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Claude vs Gemini: Which Is Better? Side by Side

Run claude-opus-5-5 and gemini-4-argon head-to-head on your own prompt, with your own API keys. Real responses, cost, and latency side by side. Free, no signup.

Open in Colab -> View source

Ask Claude and Gemini the same everyday question and our run surfaced a clean trade-off, not a winner. Claude was the faster of the two. Gemini was the cheaper. Neither was both. That is the kind of split where the “better” model depends entirely on whether you are optimizing for latency or for cost.

The run

The whole setup behind the numbers:

  • Prompt: one everyday question (below), with an “under 200 words” limit.
  • Models: Claude claude-opus-5-5 and Gemini gemini-4-argon.
  • Settings: each model’s default, temperature 0.7, no system prompt, no extra context.
  • Keys: our own, calling each provider directly. One real run.

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: $0.0094
  • Speed: 5.3 seconds (faster here)
  • Length: 449 tokens
  • Style: warm, numbered walk-through

Gemini (gemini-4-argon)

  • Cost: $0.0028 (cheaper here)
  • Speed: 7.0 seconds
  • Length: 259 tokens
  • Style: firm, concrete, script-led

The actual answers:

Claude (claude-opus-5-5)
Stay calm and empathetic, but don’t feel obligated to refund. Acknowledge the feedback without admitting fault, then explain that a half-eaten custom cake can’t be resold and “too sweet” is a preference, not a defect. Offer a goodwill gesture instead of a refund, such as 10 to 20 percent off next order or a sweetness consultation. Document everything and post a clear non-refundable policy customers initial at ordering.
Gemini (gemini-4-argon)
Once a custom cake leaves your shop, and especially once half is eaten, you are not obligated to refund based on taste. Require the remaining cake back to “test the batch,” differentiate error from preference, and if it was made to your standard recipe, do not refund. Use a short script that offers a small store credit toward a less-sweet option, and update your order contract: all custom sales are final after pickup.

Both reach the same core advice. The measurable difference is the trade: Claude got you the answer faster, Gemini got it to you cheaper, on the same question.

Insight: When two models split speed against cost like this, there is no single right pick. It depends on your job:

  • A user-facing feature where people wait on the answer leans toward the faster model.
  • A high-volume batch job where latency is invisible leans toward the cheaper one.

Measure both on your own task and decide against your own priority, not a leaderboard.

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 Gemini 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 prices 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
See plans ->
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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.