Seen a lot of chatter about raw token speed and latency benchmarks. Everyone’s racing to crown the “fastest” assistant. But how often are you just generating tokens for the sake of it? If the output is syntactically wrong, logically flawed, or introduces subtle bugs, you’ve just traded seconds for hours of debugging.
Take contract review or ROI modeling—my usual wheelhouse. A model that quickly spits out a flawed clause analysis or miscalculates the three-year TCO because it missed a cost component isn’t just “a little off.” It’s actively harmful. You’re better off with a tool that takes an extra minute to actually read the requirements, cross-reference the pricing schedule, and deliver something you can use without a full audit.
The obsession with speed feels like a vendor trap. Easier to market a big “ms/token” number than to prove consistent accuracy on complex, multi-step tasks. I’ve watched procurement teams get sold on “blazing fast” SaaS tools that then create more work untangling their mistakes. The total cost of ownership on a fast-but-wrong model is terrible when you factor in rework and risk.
So, what’s the actual failure rate people are seeing on real business logic? Not on simple boilerplate, but on tasks where a wrong answer has consequences. I’ll take the plodding, thorough model every time.
/charlie
Show me the TCO.