How to Compare AI Models

Don't pick benchmark tasks. Pick the actual work you do:

• Drafting a customer email.
• Reviewing a piece of code you'd actually run.
• Summarising a document you'd actually read.
• Brainstorming names for an actual product.

If you don't use AI for that task in real life, don't include it in the test.

Common mistake: testing with puzzles and trick questions ("how many r's in strawberry") because they're fun. They measure tokenisation quirks, not usefulness. Second common mistake: tasks where you can't judge quality - If you can't tell a good answer from a great one, the task can't rank models. Pick work where you're the expert and the AI is the assistant.

Before touching any model, write the exact prompt for each task and freeze it. Same wording, same context, same attachments for every model - The moment you tweak a prompt for one model mid-test, the comparison is dead.

Make the prompts realistic, not minimal. If you'd normally give context ("I'm a B2B SaaS founder, audience is churned customers..."), include it - You're comparing models on the work you'll actually give them, and well-specified prompts are what you'll actually use (see how to write better prompts).

Keep a scratch file with the frozen prompts. You'll reuse it every time you re-test, which turns a one-off experiment into a personal benchmark suite.

Use AskAI.free to run the same prompt across ChatGPT 4o, Claude Sonnet 4, and Gemini 2.5 Pro in the same session. Same prompt, same context - Only the model changes.

For coding-heavy work, also include DeepSeek R1 (a reasoning model). For research-heavy work, include Perplexity.

One subtlety: models are not deterministic - The same prompt produces different answers run to run thanks to temperature. For tasks you care about, run each prompt twice per model. A model that's brilliant once and mediocre once is a worse daily driver than one that's solidly good twice; consistency is a feature you can only see with repeat runs.

Copy each answer to a doc with model names hidden. Read all of them. Rank them on:

• Was it correct?
• Did it actually do what I asked, or what it thought I meant?
• Did the tone match the audience?
• How much editing would I need before sending/using it?

Reveal the model names after ranking. You'll often be surprised - The model with the loudest marketing isn't usually the best for your task.

Blinding matters more than it seems: in informal tests, people rate identical text higher when told it came from their preferred model. Shuffle the order too - Whatever you read first anchors your expectations. The "editing distance" criterion is the most predictive of real-world value; an answer that's 90% right but needs restructuring costs more time than one that's 85% right in the right shape.

Average performance is not what you care about. You care about worst-case performance - how badly does the model fail on the trickiest 10% of tasks?

Include in your test: a task with subtle ambiguity, a task that requires saying "I don't know," a task with a politically sensitive angle. The model that gracefully handles edge cases is the right daily driver.

The "I don't know" probe deserves detail: ask something that sounds answerable but isn't ("what did the CEO say in the Q3 earnings call?" for a private company that holds none). Models that confabulate here will hallucinate on your real work too - This single probe disqualifies more models than any quality test.

For tasks where you need 50 outputs a day, speed and cost matter as much as quality. Reasoning models are slower; ChatGPT 4o is the fastest among the flagships.

On AskAI.free, all models share your monthly token allowance - So picking a more expensive model means fewer total questions. Match the model to the task: don't waste Claude Sonnet 4 tokens on "summarise this email."

A practical way to think about it: classify your AI use into "volume work" (dozens of small tasks daily) and "depth work" (a few tasks where quality compounds). Pick a fast cheap default for volume and a flagship for depth. Most people need exactly two models, not five.

Models update. The leader in January is often dethroned by July. Set a recurring reminder to re-run your comparison and update your defaults - Your frozen prompt file from step 2 makes the re-test a 20-minute job.

Also re-test when a provider ships a major version, because regressions are real: a model update can improve coding while making its prose more robotic. Your personal suite catches that; the launch blog post won't mention it. For a head start between tests, our comparison hub stays current with verdicts across the most common tasks - ChatGPT vs Gemini and the rest.

Lena, a freelance content strategist, tests four models on five frozen tasks: a 600-word blog draft in a client's voice, a content-calendar brainstorm, a competitor-page teardown, an email to a difficult client, and a "summarise this 40-page brand guide" job. Two runs each, scored blind in a spreadsheet.

Results surprise her: Claude wins the blog draft and the difficult email decisively, Gemini wins the brand-guide summary (the only task near context limits), and ChatGPT wins brainstorming on speed and variety while losing on depth. Perplexity, which she expected to ignore, wins the competitor teardown outright because its claims came with sources.

Her resulting stack: Claude as default, Perplexity for anything research-shaped, ChatGPT for rapid-fire ideation. Total cost on a multi-model plan: $9.99/mo. The leaderboard she'd been reading would have told her to use one model for everything - And it would have been the wrong one for three of her five tasks.