Using tokens to evaluate models is an outdated approach. Cost per task is what matters. Not all tokens are created equal

Yes, but… more thinking tokens also means longer solution generation time. That said, v4 Flash is a fast model. I use it all the time because it’s very smart for the price. But it is verbose sometimes.

The thinking trace was (preview) frustrating to read, I think I'd prefer a summary view of it at this point.

It’s not outdated at all to use tokens to estimate performance, it’s directly related.

But why should I care? If my metrics are speed and cost? How many tokens it takes as a user is arbitrary to some extent.

If speed is a metric for you, tokens required to solve a problem affects that metric.

All else being equal passing triple the amount of tokens through a model to solve the same problem makes it slower.

Doesn’t mean this model is bad, and it has to be considered how cheap it is, but it’s a factor.

Still, seems useless to measure tokens when you can measure speed.

> inefficient

That depends. Is it also more reliable?

If two books, one big one slim, prove the same thesis, what I would be interested in is the quality of the content, not the size. There can be a measure of efficiency in "have you really thought it through", but it is clearly complex - it requires measuring how solid the reasoning is.

you are correct. but in my experience -- not benchmarks -- g flash 3.6 is SO BAD for coding. I'm using all vendors all day and gemini is the worst by far. I built my own semi-deterministic orchestrator for coding agents.

Roughly equivalent to Gemini 3.6 Flash in capabilities at 1/20th the price...

Mind you, until the recent price cuts to Luna - Gemini 3.6 Flash wasn't even egregiously priced (but oh how things change in just 1 week).