GPT-6.1 Terra vs GPT-6 Astra vs Claude Fable 5.1: Messy Sales Sheet Test

Posted on Oct 28 • Originally published at ranthis.com • AI-assisted

OpenAI released GPT-6.1 Terra on 26 October as its cheaper, faster model for everyday work: $1 per million input tokens and $5 per million output, a tenth of GPT-6 Astra's list price. Cheap only helps if the answers hold up, so the day after launch we gave Terra, GPT-6 Astra and Claude Fable 5.1 the same messy sales sheet: 40 rows, six planted problems, three branch totals that have to be right to the dollar. On "high" effort Terra matched Astra's perfect score in all three runs at about a tenth of the cost. The more useful finding was elsewhere: one ambiguous cell changed a branch total by $10,800 from one run to the next, on the same model with the same settings.

Measured on 27 Oct 2026 through the OpenAI and Anthropic APIs, from a Python script. The data is invented; the task, answer key and scoring code were committed before any model was called, and the raw answers are kept with this article's source files.

Row 18 sold 9 filing cabinets at "1.2k", that is $1,200. Every other filing cabinet in the sheet sold at $3,200. Our answer key treats "1.2k" as a price written differently, so it should be normalized and counted. But a price that low can also look like a typo, and the sheet invites that reading: the shorthand and the odd price sit in the same cell.

Astra and Terra on high and max counted it in every run. Terra on default counted it once and dropped it twice; Fable 5.1 dropped it once and counted it twice. Same prompt, same data, same settings.

What the models wrote about the row is the part to watch. When Terra (default) counted it, its note said: "The unit price was normalized from 1.2k to 1200." When it dropped it, the note said only: "The price of 1.2k means $1,200, which differs from the $3,200 price of the other filing cabinets." That note never says the row was left out of the total; you find out only by checking the Chicago number. Fable's note in the run where it dropped the row was explicit: the price "does not match the Filing cabinet price of 3200 used everywhere else, so the row was excluded as bad data."

Earlier that day we had run the same 40 rows and six traps as a Vietnamese-language sheet (Vietnamese labels, prices and shorthand); the English sheet above was derived from it. The pattern flipped: there, Terra on default counted row 18 in all three runs and Terra on high dropped it in all three. So the effort setting is not what decides this row; it varies run to run. Only GPT-6 Astra and Terra on max counted it in all six runs across the two sheets.

For structured office work like checking and totalling a sales sheet, GPT-6.1 Terra on "high" did everything GPT-6 Astra did in our test, a little faster and at about a tenth of the price. The default setting is cheaper again but less steady on judgment calls. Whichever model you use, make it say whether each flagged row was counted or excluded, and spell out how to treat shorthand like "1.2k": on our sheet, that one cell was the difference between two Chicago totals $10,800 apart.

How this was made: an AI (Claude) helped write this article; the RanThis editor ran the test script, checked every number against the raw results and checked facts against the linked sources.