Which AI Model Should I Use?
About this resource
Teams lose a surprising amount of time to this question, usually by asking it in the wrong form. "Which is best" has no answer. "Which is better for this kind of task, and how much does the difference matter" does. This one-page guide compares the major AI assistants by task type rather than by benchmark score.
It covers where the differences are real enough to affect your results, where they are marginal enough that switching is not worth the disruption, and which considerations matter more than model quality: what your organization already licenses, what the data handling terms say, and whether an enterprise account is available. For most organizations those three settle the question before capability enters into it.
This is the fastest-ageing document in the library. Model capabilities shift every few months, so treat any specific comparison as accurate at the review date printed on the handout rather than indefinitely.
How to use it
Settle the question once at the organizational level rather than letting each person answer it individually. The cost of three teams on three tools is not the subscriptions; it is that no prompt, procedure, or piece of learning transfers between them. Start with what you already license, confirm the data terms, and only then compare capability.
Inside the download
Which is better for this task, not which is best overall.
The task categories where the difference is large enough to change your output.
The larger set of everyday tasks where any of them will do.
Existing licensing, data terms, and account type usually decide this before capability does.
Common questions
There is no single answer, and the differences between the leading assistants on everyday business tasks are smaller than the marketing suggests. What matters more is what your organization already licenses, whether an organizational account with appropriate data terms is available, and whether your people can get consistent results from it.
For long-document analysis, code, and highly structured output the differences are real. For drafting, summarizing, rewriting, and everyday communication they are marginal. Most organizations get more value from standardizing on one and building practice around it than from choosing the theoretically best one.
Usually not at first. Multiple tools fragment practice, prevent prompts and procedures from transferring, and multiply the governance surface. A second tool is worth adding once there is a specific capability gap that the first genuinely cannot cover.
Free. No email list, no follow-up sequence.
More from the resource library, chosen because they pair well with this one.
What these tools do genuinely well and where they reliably fail, so you can assign work correctly the first time.
Tokens, context windows, training cutoffs, and why these systems produce confident wrong answers. One page, kept beside a monitor.
Ten questions to answer before the purchase order, eight questions for the vendor, and four honest options including doing nothing.
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