What does model agnostic mean?

Model agnostic means the same AI worker runs on any supported model, so you pick at deploy time and can switch later without rewriting the worker.

Published by WorkerKit. Updated 2026-07-23.

A kit brings the instructions, not the model. That one sentence is the whole idea, and its consequences are worth spelling out because most of them are commercial rather than technical.

Tied to a model vs model agnostic

	Tied to one provider	Model agnostic
Switching model	A rewrite	A dropdown
Provider outage	Your worker is down	Redeploy on another model
A cheaper model ships	Migration project	Change and measure
Price rise	You absorb it	You move
Comparing two models	Two implementations	One run, sent to each

What it means in practice

Claude, ChatGPT, Gemini and Grok all drive the same kits. You choose the model when you deploy an AI worker, switch whenever you like, and can send a single run to a different model to compare the two on the same job with the same inputs.

That last one is the useful trick. Model comparisons in the abstract are marketing. A comparison on your job, with your instruction and your data, is evidence. Meeting Prep Assistant reaching calendar, email, meeting notes and CRM is a genuinely hard test: four sources, one synthesised document. Run it twice on two models and read both prep sheets. You will know within a morning.

Why it matters more than it sounds

Model choice is the fastest-moving variable in this category. A model that is best for a job in March may be second by June and cheaper by September.

If a worker is welded to one provider, every one of those changes is a rewrite. If it is not, they are a dropdown. Being model agnostic is a bet that the frontier keeps moving, which so far has been safe.

It also removes a quiet dependency. A worker tied to one provider inherits that provider's outages, rate limits and pricing decisions with no recourse. When a fleet runs unattended, "the provider is having an incident" turns into "nothing ran this morning and nobody noticed", which is a much worse sentence.

Per-model grades, and their provenance

Model agnostic does not mean every model is equally good at every job. Some jobs need careful long-context reasoning; some need speed and low cost.

Per-model grades are not currently displayed on kit pages. Compare models on your own examples. When a publisher describes performance, distinguish between measured results and declared expectations:

Provenance	What it means	How to weigh it
Measured	Taken from real runs of this kit	Evidence
Declared	Stated by the kit's publisher	A claim, useful until runs measure it

That mark is not decoration. A measured grade and a declared one deserve different confidence before you put a worker on a job that touches customers. You can also filter the directory by model to see only the kits that fit the one you already use.

The pricing consequence

Model tokens bill at the provider list price with no markup, from the wallet or your own key.

This is what makes model agnosticism real rather than nominal. If a platform took a cut of tokens, it would earn more when you chose the bigger model, and "switch any time" would sit in tension with its own revenue. With no markup there is nothing to steer: switching to a cheaper model saves you money and costs WorkerKit nothing.

Watch for this when comparing platforms. A vendor that marks up tokens and also offers model choice has an interest in which one you pick, whatever the docs say.

The reasonable objection

"Model agnostic means lowest-common-denominator. You cannot use any provider's best features."

There is something in it. A kit written to run anywhere cannot depend on one provider's proprietary extension, and a prompt tuned for one model is rarely optimal on another.

The answer is that the constraint sits at the right level for this kind of work. A kit's value is in the instruction, the access rules and the wiring, none of which are provider-specific. The provider-specific tuning that does matter is mostly about phrasing. Test the same inputs on different models to find where each one is weaker; model choice does not promise identical performance.

When to pin a model instead

Being able to switch is not a reason to switch constantly.

A job you have measured. If a model has proven itself on a worker touching customers, leave it. Novelty is not a reason to re-qualify.
Compliance constraints. If a provider is approved and others are not, that decision is not yours to optimise.
Cost stability. Switching to a cheaper model mid-quarter changes your token spend profile, which someone may be forecasting.

The value is in being able to move, cheaply, when there is a reason. That is different from moving often.

FAQ

Which AI models can run WorkerKit kits?

Claude, ChatGPT, Gemini and Grok all drive the same kits, because a kit brings the instructions rather than the model. You pick the model when you deploy a worker and can change it at any time.

Can I switch a worker to a different model later?

Yes, at any time, with no rewrite. You can also send a single run to a different model to compare the two on the same job with the same inputs, which is a much better test than a benchmark on somebody else's task.

What does a per-model grade on a kit page mean?

Per-model grades are not currently displayed on kit pages. The term describes a score for one kit on one model. Weigh a measured result from real runs as evidence and a declared score from a publisher as a claim.

Does WorkerKit charge more for a more expensive model?

No. Model tokens bill at the provider's list price to the cent with no markup, whether paid from the wallet or your own key. WorkerKit earns the plan price and the wallet top-up fee, so nothing in the pricing steers your model choice. See /pricing.

Published WorkerKit plans and prices apply to shared cloud.

Can I use my own provider account?

Yes. Add an Anthropic, OpenAI, Google or xAI key and those tokens bill straight to your provider account, with the first 1M tokens each month carrying no platform fee. See BYOK.