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.
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.
So every kit page grades itself per model, and each grade is marked with where it came from:
| 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 but untested |
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, which is why per-model grades exist rather than a claim that every model performs identically. You are told where each one is weaker instead of being promised they are the same.
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?
It scores how that kit performs on that model, and it is marked with where it came from: measured from real runs, or declared by the publisher. Weigh a measured grade as evidence and a declared one 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.
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.