What is the fastest way to compare AI models side by side?

Send one prompt to two or more models at the same time and read the answers next to each other. You can do this with browser tabs, with a provider playground, or with a multi-model workspace that fans one prompt out automatically. The tool matters much less than using the same prompt and a fixed scoring rubric.

How many prompts do I need to get a reliable answer?

Ten prompts drawn from your real work, run once each, is enough to separate models on a job. Three prompts is noise. Fifty is a project you will abandon halfway. If two models tie after ten, they are genuinely interchangeable for that job and you should pick on price or speed.

Should I trust public benchmarks instead?

Use them to decide which two or three models are worth testing, then ignore them. Benchmarks measure a distribution of problems that is not yours, and leaderboard position does not predict which model writes an email you would actually send.

How often should I re-run the comparison?

Once a quarter, and again whenever a lab ships a frontier release. Keep the test set in a file so a re-run costs twenty minutes instead of a fresh afternoon of design.

How to Compare AI Models Side by Side (And What to Actually Test)

Comparing AI models side by side sounds like it means opening two tabs and eyeballing the answers. That version gives you a result in five minutes and the result is usually wrong, because the thing you end up measuring is which answer was longer or which model happened to open with a confident sentence.

A comparison that survives contact with real work needs four things: prompts from your actual job, a fixed rubric, blind scoring, and a way to re-run it in three months when the models change. Here is the whole method. It takes about an hour the first time and twenty minutes on every repeat.

Step 1: build the test set from last week, not from your imagination

Open whatever you worked in last week and pull out ten things you asked a model to do, or ten things you should have. Real ones, with the real messy input attached.

A good test set for a marketing team might look like: rewrite this landing page section, turn these five customer calls into themes, draft a reply to this annoyed customer, summarise this competitor’s pricing page, write three subject lines for this email, check this paragraph for claims we cannot support, turn this brief into an outline, explain this analytics drop, condense this 2,000 word post into a LinkedIn version, and draft a FAQ from this support thread.

Two rules about the prompts themselves. Write each one the way you would write it for a competent new hire, with the context and the constraints included. And write it without any model in mind. A prompt that has been tuned against one model for months contains small compensations for that model’s habits, and testing with it measures prompt fit rather than model ability. This is the most common way people accidentally confirm the model they already use is best.

Step 2: run the same prompt against every model at once

Three ways to do this, in ascending order of how much you will actually keep doing it.

Browser tabs. Free, works today, and fails the moment you have more than two models or more than three prompts, because you will start paraphrasing the prompt slightly differently in each tab without noticing. If you do it this way, paste from a text file every single time.

Provider playgrounds or the API. Precise, lets you pin temperature and system prompt, and costs cents rather than a subscription. The downside is that you are testing a raw model, not the product most people will use, and the polished apps add retrieval and tool use that change the answer.

A multi-model workspace. One prompt box, several models, answers rendered next to each other. This is what AIHub does: send the prompt once, get ChatGPT, Claude, Gemini, Llama, Mistral and Perplexity answering in parallel, and keep the one you want. The advantage for a comparison is mechanical rather than magical, which is that the prompt is provably identical across models because you only typed it once.

Step 3: score on a fixed rubric

Scoring “which is better” produces a vibe. Scoring five named dimensions produces a decision you can defend a week later.

Dimension	What you are asking	What a fail looks like
Instruction fidelity	Did it do the thing asked, including the boring constraints	Ignored the word limit, changed the format, answered a nearby question
Factual grip	Are the specifics correct and traceable	Invented a statistic, a citation, a function name or a feature
Usable as written	Would you ship this with light edits	Reads like a first draft that needs restructuring
Voice	Does it sound like you or like a press release	Slides into generic marketing register by paragraph three
Effort to fix	How long to get it to shippable	Faster to rewrite from scratch

Score each dimension 0 to 2. Zero is fail, one is acceptable, two is good. Ten prompts across five dimensions gives each model a score out of 100, which is enough resolution to separate real differences and not so much that you agonise over a point.

The dimension that decides most real comparisons is the last one. A model that produces a beautiful answer that needs restructuring loses to a plainer one you can ship in two edits.

Strip the model names before you score. Paste the answers into a document as A, B and C, shuffle the order between prompts, and score them there.

This step feels like overkill and it is the one that changes results most often. Brand expectation is strong: people rate an answer higher when they believe it came from the model they already pay for. If you are running this to justify a purchase to someone else, blind scoring is the difference between evidence and an opinion with a table attached.

Step 5: write down the answer, with a date

Record the winner per job, the score gap, and the date. A gap of ten points or less means the models are interchangeable for that job, so pick on price or latency and move on. A gap of thirty means you have found something real and should route that job to that model by default.

The date matters because this expires. Every lab ships a frontier release every few months and the ordering reshuffles. Keep the test set in a file, re-run it quarterly, and the re-run costs twenty minutes. For what the current ordering tends to look like by job, see ChatGPT vs Claude vs Gemini.

Four mistakes that produce a fake winner

Testing on toy prompts. “Write a poem about a robot” separates nothing. Every frontier model clears it, and clearing it predicts nothing about your quarterly report.

Judging on first impressions. The model that opens with a confident summary reads better and is not necessarily more correct. Read to the end before scoring.

Letting length win. Longer answers feel more thorough. Score against the brief, and if the brief said 200 words then 600 words is a fidelity failure, not generosity.

Running it once and never again. A comparison from six months ago is describing models that have all been replaced. The whole point of keeping a test set in a file is that the second run is cheap.

Once you have the answer, the practical problem becomes routing: getting the right job to the right model without three subscriptions and three tabs. That is the part AIHub handles, and it is the same problem we looked at from the workflow side in where AI actually lands in a small business. More on model choice on the AIHub blog.