Vercel AI Gateway shows open models dominating token volume over closed ones

FoxTPNL / Wikimedia Commons (CC BY 4.0)

Vercel AI Gateway shows open models dominating token volume over closed ones

Open-weight models surged from 11% to 56% of token volume in just four months, but closed models still capture the lion's share of revenue

The AI model landscape just hit a tipping point. Open-weight models now account for 56% of all token volume flowing through Vercel’s AI Gateway as of August 2026, up from a mere 11% in April. That’s a fivefold jump in four months, and it marks the first time open models have overtaken their closed counterparts on this metric.

On a single day, August 22, the share spiked to roughly 62%. More recent leaderboard data has shown it touching 78.4%.

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The volume-revenue paradox

Before anyone declares total victory for the open-source crowd, there’s a wrinkle worth examining. Anthropic, maker of the Claude family of closed models, still commands 61-65% of total gateway spend despite representing only about 30% of tokens processed. Its models carry premiums of up to 4.4 times the average token price.

That’s the “barbell” pattern emerging across enterprise AI usage. On one end: massive volumes of cheap inference running through open-weight models from labs like DeepSeek, Z.ai, and Moonshot. On the other: premium, high-stakes workloads where companies pay a significant markup for Anthropic’s quality guarantees.

DeepSeek has become particularly prominent in this shift, frequently ranking first or second in volume contribution with roughly 25% or more of total token share. Google, by comparison, sits at around 11%.

A race to the bottom on price

The economics driving this shift are stark. Average price per token fell 23.2% in August alone, the third consecutive monthly decline. Over the past five months, prices have dropped more than 50%.

Open-weight models are running high-volume workloads at approximately one-seventh the cost of frontier closed models.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.
Vercel AI Gateway shows open models dominating token volume over closed ones
Vercel AI Gateway shows open models dominating token volume over closed ones

Open-weight models surged from 11% to 56% of token volume in just four months, but closed models still capture the lion's share of revenue

FoxTPNL / Wikimedia Commons (CC BY 4.0)

The AI model landscape just hit a tipping point. Open-weight models now account for 56% of all token volume flowing through Vercel’s AI Gateway as of August 2026, up from a mere 11% in April. That’s a fivefold jump in four months, and it marks the first time open models have overtaken their closed counterparts on this metric.

On a single day, August 22, the share spiked to roughly 62%. More recent leaderboard data has shown it touching 78.4%.

Advertisement

The volume-revenue paradox

Before anyone declares total victory for the open-source crowd, there’s a wrinkle worth examining. Anthropic, maker of the Claude family of closed models, still commands 61-65% of total gateway spend despite representing only about 30% of tokens processed. Its models carry premiums of up to 4.4 times the average token price.

That’s the “barbell” pattern emerging across enterprise AI usage. On one end: massive volumes of cheap inference running through open-weight models from labs like DeepSeek, Z.ai, and Moonshot. On the other: premium, high-stakes workloads where companies pay a significant markup for Anthropic’s quality guarantees.

DeepSeek has become particularly prominent in this shift, frequently ranking first or second in volume contribution with roughly 25% or more of total token share. Google, by comparison, sits at around 11%.

A race to the bottom on price

The economics driving this shift are stark. Average price per token fell 23.2% in August alone, the third consecutive monthly decline. Over the past five months, prices have dropped more than 50%.

Open-weight models are running high-volume workloads at approximately one-seventh the cost of frontier closed models.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.