An algorithm is a finite set of explicit steps or rules written by a person to solve a problem, while an AI model is the set of learned parameters that a training algorithm produces from data — so the algorithm is the recipe, and the model is the cake that comes out of it.
If you only remember one distinction, make it this: algorithms are hand-written instructions, models are statistical patterns extracted from examples. People mix the two up constantly, which is why a sentence like "the algorithm decided your loan application" is technically backwards — an algorithm trained the model, and the model made the call.
Here is the mechanism in plain terms. When someone builds an AI system, they write a training algorithm — gradient descent is the classic example — which is a loop of explicit mathematical steps: measure how wrong the model is, nudge the internal numbers to reduce that wrongness, repeat.
Those internal numbers are the parameters. A large language model can hold billions of them. After training finishes, the algorithm steps away and the model remains, frozen, as a file you can run.
At inference time — the moment you type a prompt — no learning happens. The model just does arithmetic on your input and produces output. That is why a model can be copied onto a laptop and behave identically to the same model on a server, and why two people using the same model get the same answers. The algorithm is the process; the model is the artifact.
A concrete comparison makes the difference actionable. Imagine you need to sort a list of 10,000 customer names alphabetically. A classic sorting algorithm handles this perfectly: it is deterministic, you can prove it is correct, and it will never surprise you.
Now imagine you need to decide which of those customers is most likely to cancel their subscription next month. No hand-written rule set can capture that reliably — the signals are tangled and nobody knows the exact weights in advance. So you use a training algorithm on historical data, and it produces a model that outputs a probability per customer.
Same word, "algorithm," but completely different jobs: one is a fixed procedure you can audit line by line, the other is a learned pattern you can only evaluate by testing its predictions. The practical rule of thumb: if you can write the rules yourself, use an algorithm; if you cannot articulate the rules but you have examples, train a model.
Now the part people actually trip over — versions. When you hear "the new version" of a model, that usually means a fresh training run with more data, more parameters, or a different training algorithm, producing a different artifact with different behavior. According to our internal AI tool database, which tracks 360 AI tools with pricing and capability snapshots verified as recently as 2026-09-18, tools that look identical on the surface often sit on different model versions underneath, which is why the same prompt can give noticeably different results across two products.
The database snapshot is a point-in-time record, not a live feed, so treat any capability claim as something to re-check at the vendor's page rather than a permanent fact.
Where this framing breaks down: the algorithm-versus-model line gets blurry in systems that keep learning after deployment, such as recommendation engines that update on live click data. There, the training algorithm never really stops, so "the model" is a moving target. It also breaks down in cost terms — a bigger model is not automatically better for your task, and running a large model when a small one would do wastes money and adds latency.
The honest limit is that neither term tells you whether a system is any good. A model is just a compressed summary of its training data, and if that data was thin, biased, or stale, the model inherits those flaws no matter how elegant the training algorithm was. If you want to go deeper on why models confidently produce wrong answers, that is a separate mechanism worth understanding on its own.
A useful tip most beginners miss: when a vendor says "our AI," ask two separate questions. First, what model is it — which version, trained when, on roughly what? Second, what algorithm or rules wrap around that model — because most real products are a model plus a pile of ordinary deterministic code for formatting, filtering, and safety checks. The wrapper is often where the actual product value lives, and it is the part that does not change when the underlying model gets swapped out.