Compare ChatGPT, Claude, Gemini, and Other AI Models

Comparing ChatGPT, Claude, Gemini, and other AI models with the same prompt helps you see how they differ in accuracy, usefulness, tone, and speed for a particular task. You’ll need access to the models you want to test and a simple way to record their responses; the steps below help keep the comparison fair and repeatable.

What You’ll Need

Access to the AI models you want to compare. Free and paid plans may offer different models or limits, so note which model and plan you use.
A single prompt and, when needed, the same reference material or instructions for each model.
A document or spreadsheet for saving responses, model names, settings, and scores.
A small set of criteria that match your task, such as correctness, completeness, clarity, or how well the response follows instructions.

Step-by-Step

Choose a task that matters to you. Decide what you want the comparison to establish. For example, you might need a model to summarize a meeting, explain a concept, draft a customer email, or generate code. A test based on your real use is more informative than asking which model is “best” overall. Avoid using confidential or sensitive information unless you understand and accept the service’s data-handling terms.
Write one clear, self-contained prompt. State the task, audience, context, required format, and any constraints. If you want a 150-word summary for a nontechnical reader, say so. Specify what the model should do when information is missing, such as asking a question or marking an assumption. Clear instructions reduce the chance that differences in your prompt—not the models—explain differences in the answers.
Prepare any shared material. If the task relies on a document, image, dataset, or source text, provide the same material to each model in a supported format. Keep the order and wording of the instructions identical. If one service cannot accept the same file type or amount of material, either choose a simpler test all services can handle or record that limitation as part of the comparison.
Check which models and settings you are using. Select the specific model where the service lets you choose one, and record its displayed name. Also note the date, plan, and relevant options, such as web search, file analysis, or extended reasoning. Turn optional tools off for all models if you want to compare text generation alone. If tools are part of your real workflow, enable equivalent capabilities where possible and clearly note where they differ.
Start a fresh chat for every test. Open a new conversation with each model so earlier messages do not influence the answer. Paste the exact same prompt and shared material. Do not add extra explanation to one chat unless you add it to every chat. If a model asks a follow-up question, record it; either answer consistently across the test or treat the need for clarification as an outcome.
Save the first responses before editing anything. Copy each response into your comparison document and label it with a neutral identifier, such as A, B, or C. Keep the original wording, including caveats and formatting. Record the date, model name, prompt version, and any tool settings. This makes it easier to review the results later and prevents a polished rewrite from being mistaken for the model’s initial answer.
Score answers against criteria you chose in advance. A simple 1–5 scale can work, but define what each score means. For factual work, check claims against reliable sources rather than rewarding confident language. For writing, assess whether the response follows the requested audience, format, and tone. For coding, run the same tests in the same environment. Consider separate scores for accuracy, instruction-following, completeness, readability, and usefulness.
Review limitations and repeat important tests. Note errors, unsupported claims, missing details, and any unwanted tool use. A single response can vary because of randomness, changing services, or differences in model updates. For an important decision, repeat the same test in new chats, or try several representative prompts. Keep the prompt unchanged within each comparison, and report results as observations for that task—not a universal ranking.

Common Mistakes

Changing the prompt between models: Even a small wording change can affect the result. Save a master copy and paste it unchanged.

Letting chat history carry over: A model may use context from earlier turns. Use a new chat for each run, and don’t give one model corrections that others do not receive.

Comparing different capabilities without noting them: Web access, file handling, and model selection can change responses. Record what was enabled, and avoid treating a tool advantage as a pure language-model difference.

Judging by fluency alone: A polished answer can still be wrong or incomplete. Verify facts and evaluate against task-specific criteria.

Declaring a universal winner from one prompt: Performance depends on the task, instructions, available tools, and response variability. Limit your conclusion to what you tested.

Tips

Use prompts that include clear success conditions, such as “give three options, explain the trade-offs, and end with a recommendation.”
For a less biased review, hide model names while you score the responses. Reveal the labels afterward.
Use a table with one row per model and one column per criterion, plus a notes column for errors or useful details.
Test a mix of ordinary and challenging examples if you want to understand reliability, not just best-case output.
Include the time it took to get a useful result and how much editing you needed if those factors matter to your workflow.
Re-run comparisons when model names, tools, or service settings change. Record the test date so older results are not mistaken for current performance.

Troubleshooting

The answers look very different in length or format: Check that the prompt specifies the same output structure and approximate length. If you add a correction, start a new run for every model rather than correcting only one.

A model cannot access the file or feature: Check its supported input types and limits. Use a common format or a shorter excerpt that every model can process. If equivalent access is not possible, mark the comparison as unequal instead of treating the result as a direct match.

Responses change when you repeat the prompt: Save each run and note any available generation settings. Repeat enough times to see whether the difference is consistent; do not select only the most favorable answer from each model.

You can’t verify a factual answer: Ask for sources in a separate, clearly labeled test, then check those sources yourself. A citation or confident explanation is not proof that a claim is correct. If the fact matters, consult dependable primary or authoritative references.

One response appears to use current information and another does not: Check whether browsing or search was enabled. Either compare without those tools or enable comparable access and record the difference.

Frequently Asked Questions

Can I compare models using a free account?
Usually, if the services provide access to the models you want to test. Free plans may have different model choices, usage limits, or tools, so record those conditions.

Should I use one prompt or several?
Use one prompt for a quick, narrow comparison. For a broader view, test several representative prompts, keeping each prompt identical across models.

Is it fair to compare different model versions?
It can be useful if those are the versions available to you, but identify each version and date. Don’t present the result as a controlled comparison of equivalent models.

Should I turn on web search?
Only if search is part of the task. For a fair test, either leave it off for all models or use comparable search access and note any differences.

How do I decide which model performed best?
Choose the model that scores best on the criteria that matter for your use. Accuracy, speed, cost, privacy terms, and editing effort may matter differently for different tasks.

Can I paste work or personal information into every model?
Not automatically. Review each service’s privacy and data-use settings first, and use anonymized or non-sensitive material when possible.

General steps for current mainstream versions; menus can differ by device and version. See the disclaimer or report an error.

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