Platform data from Vercel and OpenRouter shows open-source models winning on token volume, but Anthropic and other frontier labs still capturing the majority of actual dollars spent.
Despite a sharp rise in open-source model usage, frontier AI providers like Anthropic are holding on to the bulk of developer spending — at least for now, according to platform data cited in TechCrunch reporting published July 7 [1].
The data matters because it tests a widely held assumption: that cheaper open-source models will eventually cannibalize the revenue of expensive frontier labs. So far, the numbers don’t support that conclusion [1].
What the platform data shows
On Vercel’s AI gateway — which routes traffic across multiple model providers — DeepSeek has surged to first place by token volume, processing just over a third of all tokens passing through the platform in the past week [1]. Z.ai, the lab behind the GLM-5.2 model, jumped to fourth place over the same period [1].
But scroll to total spend and the picture flips: Anthropic still accounts for more than half of all AI spending on the Vercel platform [1]. That share has dipped slightly over the past month, partly because of Anthropic’s own price increases, but not by much [1].
OpenRouter, which serves a broader and somewhat less enterprise-focused segment of the market, tells a similar story [1]. DeepSeek V4 Flash leads on raw usage at 5.3 trillion tokens processed weekly, while the most popular frontier model, Opus 4.8, handles just over 2 trillion [1]. OpenRouter does not rank models by total spend, but it logs the average token cost for Opus 4.8 at roughly 23 times that of V4 Flash — $1.37 per million tokens versus 6 cents — suggesting Opus is still capturing the larger share of dollars [1].
A two-phase life cycle, not a zero-sum race
The framing comes from Decagon chief executive Jesse Zhang, who published a post titled “Everyone is wrong about open source AI in the enterprise” on Monday [1]. Zhang argues that frontier and open-source models are not direct competitors but rather two stages of the same deployment cycle: expensive frontier models prove out new use cases, which then get handed off to cheaper open-source alternatives once they mature [1].
The result, in his telling, is that as older use cases migrate to lighter models, new ones keep emerging at the frontier — keeping overall spend on top-tier models roughly flat [1]. As Zhang puts it, “The frontier labs will keep owning discovery. Open source will increasingly own production.” [1]
TechCrunch notes that Zhang offers limited data of his own to back the claim, but points to the Vercel and OpenRouter figures as broadly consistent with his thesis [1].
One more variable: Nvidia’s Nemotron
The current figures do not yet capture Nvidia’s Nemotron model, which TechCrunch reports is positioned to jump to the front of the usage rankings, citing Nvidia’s industry relationships and the model’s “extreme adaptability” [1].
A second explanation for frontier labs’ resilience, beyond Zhang’s life-cycle theory, is that many production use cases remain too complex to be fully replaced by cheaper alternatives — keeping a floor under demand for the most capable models [1].
Sources
This article was drafted with AI from the cited sources and checked against them before publication. Spot an error? Let us know.
