Tag: model-comparison

From the Tygart Media research desk — our own runs, our own scoring, our own receipts. Part of the GLM Desk.

Every AI pricing page on the internet prints the same table: dollars per million tokens, in and out. It’s accurate and it’s useless, because nobody buys tokens. You buy finished tasks — a census completed, a draft delivered, an audit verified — and the cost of a finished task depends on how the model behaves while doing it: how much it re-reads, how often it retries, whether it plans before it fetches. Per-token tables hide all of that.

We can print the other number because we meter every agent run we seat and keep the receipts. What follows is cost per finished task from our own fleet records — dated, scoped, and with the task shapes named, because a cost without a task shape is just another token table.

The receipts: what finished GLM tasks actually cost us

All runs below: the same seat (details in the settings box), production or scored-battery work, platform charges as recorded.

The shape to notice: the most expensive finished task on this list — a 250-query, five-site analysis with a 21,600-character input — cost four cents. The entire 15-run study behind this desk’s limit map and dial test cost about six cents. At these numbers, the cost of testing a seat properly is a rounding error; the cost of not testing it is paid in wrong answers shipped.

The same brief, 44 models: behavior is the price

Cost per task is where model choice stops being abstract. In our 44-model census — one identical brief, exact-match scoring — the runs that scored a perfect 20/20 ranged from 0.83¢ to 86.3¢. A hundred-to-one spread for the same correct answers. The cheapest perfect run was this seat. Three early finishers make the point in miniature: three models, three perfect scores — 13¢ (31 seconds), 6¢ (58 seconds), and 30¢ (52 seconds) respectively.

And per-token price doesn’t predict where a model lands in that spread, because behavior intervenes. The slowest perfect run in the census took four minutes: it fought through four visible tool failures and re-fetched pages three times before landing perfect. Stubbornness finished the task — and the meter ran the whole time. A model that plans its fetch before fetching (the recorder shows this seat doing exactly that: weighing the fetch method against the task’s exactness requirement, then executing once) costs less per finished task than a model with cheaper tokens and sloppy process. The token table can’t tell you which is which. The receipt can.

What we don’t claim

These are one shop’s charges, on one platform, on one date, for no-repository agent tasks of the shapes named above. Direct API usage prices by token — the maker’s official card for this model lists $0.15 per million input tokens and $0.50 per million output at the time of writing — and your tasks will meter differently than ours. Treat every figure here as a receipt, not a rate card: evidence of what a finished task cost when a specific seat did specific work on a specific day. That’s still more than the token tables give you, which is the point of publishing them.

We update this page when the desk’s records add a materially different task shape — or when the watch on the maker’s catalog fires and the pricing ground moves. If nothing changes, nothing is posted; that’s the desk’s standing promise.