Inside China’s Machine · Tokenomics, Episode 3 of 10
The Answer Price = price per token × tokens per attempt ÷ success rate
For agent work, model upgrades at a mostly unchanged token price, not token price cuts, now move the cost of a solved task down or up with success and tokens per attempt, and the maker that controls success is positioned to price that saving back in.
On Google’s price page for its Gemini models, read October 26, 2026, two Flash models sit one above the other. Gemini 3.8 Flash is “Our most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows.” Gemini 3.6 Flash is “Our previous generation Flash model, balancing speed and multimodal capabilities across general agentic and everyday tasks.” Both carry the same three rates: $0.75 per million input tokens, $0.075 per million cached input tokens, $3.75 per million output tokens. Under each rate on both models there is a second sentence. For input it reads “$1.50 starting January 1, 2027.” Every rate doubles on that date.
DeepSWE v1.1 is a public coding leaderboard with 113 real software tasks, run under one agent harness, mini-swe-agent, and last updated September 22, 2026. It shows what the newer model does with those identical rates. At the vendor’s “high” effort setting, 3.8 Flash burns 72 percent more tokens per attempt than 3.6 Flash. Its attempt costs about the same, $2.36 against $2.21. It solves 74 percent of attempts against 47 percent. So a solved task costs $3.20 on the newer model and $4.73 on the older one, about a third less. The leaderboard’s cost figures for these rows reproduce Google’s list prices to the cent, so this is the price a buyer would actually have paid, not a model of it.
The token price did not move. The answer price fell by a third. And the maker has already written down the date the rates double.
That is the pattern. For agent work, the price per token has stopped doing the work. Model makers mostly hold the token price where it was and ship a better model. The buyer’s bill for a solved task then moves with three things: how much more often the new model succeeds, how many more tokens it burns getting there, and what those tokens cost on average. When success outruns the extra cost of appetite, the bill falls, often by a third or more. When the cost of appetite wins, a better model produces a dearer answer. The saving usually comes from success, and success is something the maker builds and the buyer can buy more of by paying for more effort. So the maker, not the buyer, is in the position to decide how much of that saving to keep.
One bill, three numbers
The Answer Price, the same measure as the cost per solved task in the table below, is what a buyer pays per solved task: price per token × tokens per attempt ÷ success rate. Read the Flash upgrade through it, one lever at a time.
The first lever is token price, and on the price page it is unchanged. The second is tokens per attempt, which went from 12.69 million to 21.88 million. The agent took 166 steps instead of 117. A buyer reading only those two numbers would expect the bill to rise by about 72 percent.
It did not, and the reason is that agents mostly re-read their own transcripts. On 3.6 Flash, 89.4 percent of input tokens per attempt were cache hits. On 3.8 Flash the figure was 98.7 percent. Google bills a cache hit at a tenth of the input rate, so almost all of the extra appetite was billed at the cheapest rate on the page. Averaged over every token the attempt used, the price fell from $0.174 per million to $0.108, a drop of 38 percent, while the price page stayed exactly the same. The token count rose while the average price per token fell. The effective price per token fell because the extra tokens were mostly cache hits, the cheapest kind of token. The cost of an attempt ended up 7 percent higher.
The third lever, success, then did all the work. The success rate rose from 0.467 to 0.738. $2.36 divided by 0.738 is $3.20.
There is a quiet point in that sequence. The usual story about agents says that cheaper tokens invite more thinking: the buyer gets a discount and spends it on longer runs. Here there was no discount to spend. The longer runs came with the model, by the maker’s design, at an unchanged list price. Effort rose on its own, and success decided whether it was worth paying for.