Did the gross profit of Palantir for the quarter ended September 30, 2026 come to at or above 84.0% of its revenue?
Divide the gross profit by the revenue that Palantir reports for the quarter ended September 30, 2026 in its 10-Q or 10-K filing on SEC EDGAR. The answer is yes if the ratio is at or above 84.0%; no if it is below 84.0%.
If the source is late or silent: If Palantir has not filed by November 16, 2026, or a filing restates a period the rule reads, the test is inconclusive, and the Result says what was read and what was missing.
If no
The gap holds open
No: a mix shift or cost increase pulls the ratio below 84.0%, breaking the recent trend.
If yes
Open-weight models close the gap
Yes: margins stay close to the prior quarter's 84.7% reading, clearing this bar.
50%
NoYes
No position, no money. The crowd, the Call and the machines appear after you lock your number.
— You
— CallOct 22
— Machines2 models
— Readersshows from 5
The trail · since Oct 22
the Call 76, recorded Oct 22machines 2 runsreaders show from 5
The case · 10 people · 2 minutes
The gap holds open4 people argue noOpen-weight models close the gap6 people argue yes
When Mistral realizes it cannot build globally leading open-weight models despite significant resources, it pivots toward compute and infrastructure services. This move abandons its stated sovereign AI positioning and places it in direct…
The full model needs roughly 256 GB of RAM, which blocks local use on ordinary machines, but pointing Claude Code's environment variables at an OpenRouter-hosted GLM 5.2, or OpenCode at Cloudflare Workers AI, keeps the workflow identical while…
What would change the Call
Palantir's actual gross profit and revenue figures in its 10-Q or 10-K for the quarter ended September 30, 2026 decide this.
2 models ran this one. Their numbers and reasons appear after you lock yours.
The thesis
Will an open-weight model match or be within 2 points of the top closed model on a major coding benchmark before 2027-12-31?
Open-weight releases from Chinese and Western labs (DeepSeek, Qwen, GLM, Llama-class) will reach within ~2 points of the best closed frontier model on a headline public coding benchmark, demonstrating effective capability parity.