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ChatGPT alternatives

AI model comparison workflow.

By · tested and updated September 2026

Many users ask which AI is best, but the better question is which AI wins your task. A comparison workflow makes the answer visible instead of relying on vibes, leaderboards or brand loyalty.

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Short verdictMethod
Best forTeams, writers, developers, researchers and buyers deciding which AI subscription or API to use
Check before payingLimits, data and workflow

Quick answer

Use one prompt, one scoring sheet and at least two models. Compare correctness, specificity, sources, tone, formatting, speed, limits and how much human editing remains.

Do not compare models by reading comparisons, including this one. Twenty minutes with one of your own prompts will tell you more than any ranking, and it stays true when the models change.

Decision map

What to check before choosing.

Best fit

Best fit

Teams, writers, developers, researchers and buyers deciding which AI subscription or API to use.

Not ideal

Not ideal

People who only need a quick casual answer.

Test prompt

Test prompt

Answer this task. Then list assumptions, failure points and how another model might disagree.

Step by step

The method, and why each step is there.

AreaUseful forWatch out for
Prompt designUse the same prompt and same source material across models.Changing the prompt changes the test.
ScoringScore correctness, usefulness, specificity, tone, citations and format.Do not let a confident style hide wrong facts.
Second-pass critiqueAsk a different model to critique the best answer.Critique also needs human review.
Source verificationUse search-focused tools when facts matter.Open official or primary sources yourself.
RepeatabilityRun the test on multiple examples from your real work.One lucky answer does not prove a tool is best.

The workflow

Six steps, about twenty minutes.

This is the method behind every recommendation on this site, reduced to something you can run yourself this afternoon. It is deliberately small — a test you actually complete beats a benchmark you admire.

  1. Pick one real task you do weekly. Not a puzzle, not a riddle, not “write a poem about a robot”. A task whose output you would normally have to use.
  2. Write the prompt once and freeze it. Retyping between tools is the most common way these comparisons get quietly ruined — the prompt drifts, and you end up measuring your typing rather than the models.
  3. Run it across at least two providers, not two models. Models from one family fail in similar ways. OpenAI against Anthropic tells you more than two OpenAI models do.
  4. Score edit distance, not impressiveness. How many minutes until you would send it? That is the number that predicts whether you will still be using the tool in a month.
  5. Note where they disagree. Two strong models contradicting each other marks an uncertain claim. That is a finding about the question, not just about the models.
  6. Record the date and the model version. These systems change weekly. A result without a version string and a date is an anecdote, not a comparison.
Horizontal bar chart of context window sizes by model, from 1.05M tokens for GPT-6 Astra down to 200K tokens for Claude Haiku 4.5.
One specification worth checking before you test: context window, from official provider documentation, checked 2026-09-10. If your real task involves long documents, this decides which models can attempt it at all.
We publish our own version of this at larger scale — twenty fixed prompts, six models, a six-dimension rubric, blind scoring. The method is at the AI model test protocol, published before the results so it can be checked independently of them.

Keep the results

A comparison you cannot reproduce is an anecdote.

Record the date, the model version string and the exact prompt alongside your scores. These systems change often enough that an unlabelled result is worthless within a quarter, and you will not remember which version you tested.

Re-run the same frozen prompts in three months. The change over time is often more useful than the original ranking — it tells you which vendor is actually improving on the work you do.

Sources

Where these claims come from.

Every figure on this page was read from the official documentation below on 2026-09-10. Prices, limits and model names change without notice — the source is authoritative, this page is not.

OpenAI model documentation: current model names, context windows and output limits. Anthropic model documentation: Claude model IDs, context windows and max output tokens. Google Gemini model documentation: the current Gemini model family. Anthropic pricing: current Claude plan prices and usage limits. OpenAI pricing: what each ChatGPT tier includes.

FAQ

Questions about comparing models.

How long does a proper comparison take?

About twenty minutes for one task across two or three models. Anything much longer and you will not repeat it, which matters because these systems change often enough that a one-off comparison ages quickly.

How many models should I compare?

Two or three, from different providers. Beyond that the marginal information drops sharply and the scoring gets sloppy — and sloppy scoring is worse than no comparison, because it feels like evidence.

What should I actually measure?

Edit distance: minutes of your work between the answer and something you would send or merge. It correlates with real-world usefulness far better than any impression of quality formed while reading.

Why record the model version?

Because “ChatGPT” and “Gemini” are product names, not models, and what sits behind them changes. A result without a version string cannot be reproduced or fairly compared against a later run.