A new theory suggests that frontier and open-source models aren’t competitors, but rather two phases of the same life cycle. Expensive frontier models are used to prove out use cases that can be passed along to cheaper open source alternatives as they mature.
Data from Vercel’s AI gateway dashboard supports this point. In just the past week, DeepSeek has surged into the lead for token volumes, now processing just over a third of the tokens passing through the company’s infrastructure. Z.ai — the lab behind the popular GLM-5.2 model — jumped into a respectable fourth place over the same period.
However, if you scroll down to overall token spend, you’ll see Anthropic still accounts for more than half of the overall AI spend on the platform. Given that much of the recent change comes from Anthropic’s own rising prices, the share has dropped slightly over the past month, but not significantly.
OpenRouter tells a similar story, capturing a much larger (but slightly less enterprise-y) segment of the market. DeepSeek V4 Flash is the main winner on overall usage, processing 5.3 trillion tokens weekly. The most popular frontier model, Opus 4.8, handles just over 2 trillion.
The figures don’t fully prove this theory, but they do show that frontier labs like Anthropic aren’t suffering too much from the rise of open source — at least not yet. One explanation is that the market of AI-addressable tasks is growing so fast that the top models are able to maintain their position just by dominating early-stage deployments.
As one expert puts it, “The frontier labs will keep owning discovery. Open source will increasingly own production.” Another explanation might be that, even as clients move to open source, many use cases are so difficult that they can’t be entirely replaced with cheaper alternatives. Either way, this two-tiered economy of models may become a relatively stable feature of the AI economy.
**The Rise of Open Source AI: A New Era for the Industry?**
The growth of open-source AI has been one of the most significant trends in the industry over the past year. With more companies adopting open-source models, it’s natural to wonder whether frontier labs like Anthropic will suffer as a result.
However, data from Vercel’s AI gateway dashboard suggests that this isn’t the case — at least not yet. Despite the growth of open source models, frontier labs are still maintaining their market share. But what does this mean for the industry?
**A Two-Tiered Economy?**
One possible explanation is that the market of AI-addressable tasks is growing so fast that the top models are able to maintain their position just by dominating early-stage deployments. This would suggest that open source and frontier models aren’t competitors, but rather two phases of the same life cycle.
Expensive frontier models are used to prove out use cases that can be passed along to cheaper open source alternatives as they mature. As more mature use cases switch to lighter models, new use cases keep arising — and the overall spend on frontier models barely goes down.
**The Data Speaks for Itself**
Data from Vercel’s AI gateway dashboard supports this point. In just the past week, DeepSeek has surged into the lead for token volumes, now processing just over a third of the tokens passing through the company’s infrastructure. Z.ai — the lab behind the popular GLM-5.2 model — jumped into a respectable fourth place over the same period.
However, if you scroll down to overall token spend, you’ll see Anthropic still accounts for more than half of the overall AI spend on the platform. Given that much of the recent change comes from Anthropic’s own rising prices, the share has dropped slightly over the past month, but not significantly.
**Conclusion**
The figures don’t fully prove this theory, but they do show that frontier labs like Anthropic aren’t suffering too much from the rise of open source — at least not yet. One explanation is that the market of AI-addressable tasks is growing so fast that the top models are able to maintain their position just by dominating early-stage deployments.
As one expert puts it, “The frontier labs will keep owning discovery. Open source will increasingly own production.” Another explanation might be that, even as clients move to open source, many use cases are so difficult that they can’t be entirely replaced with cheaper alternatives. Either way, this two-tiered economy of models may become a relatively stable feature of the AI economy.
Source: Original article