These two models show up in the same shortlists and get treated as alternatives. They are alternatives in the way a delivery van and a pickup are alternatives: the same job title, different economics, and the wrong one costs you either money or rework.
Where the difference is real
The reasoning-heavy work is where a top model earns its price: multi-step code changes, long documents with cross-references, and anything where a plausible wrong answer is worse than no answer. If your workload is mostly that, the cheaper model's occasional confident error is the expensive part.
The high-volume work is where the cheaper model wins without argument: classification, extraction, summarisation of short passages, and drafting that a human edits anyway. You will process more of it than you think, and the price difference compounds at that volume.
Where the difference is not
Context window alone. Both numbers look generous, and neither tells you how evenly a model attends across a very long input. A detail buried in a hundred thousand tokens is not reliably recovered by either, and pinning the relevant section is cheaper than relying on headroom.
Benchmark gaps of a point or two on a published test set are also not a decision. The test set is fixed, so it leaks; and the row you are reading is one column of a table you did not design.
The split worth building
Route by consequence, not by category. A call whose wrong answer is caught by a test or a schema can go to the cheap model. A call whose wrong answer reaches a customer goes to the expensive one. Write that rule down, measure the routing rate for a week, and adjust once rather than continuously.
Numbers in the table are the fields on each listing, and the verdict underneath is generated from your selected rows. Neither replaces running your own prompt through both with the names stripped, which is the step that actually settles it.