我运行时间最长的agent测试台正在建造Interconnects Dashboard——一个汇集所有全网开放模型采用和性能最佳数据的汇总器。 这个仪表板是免费提供的，并变得相当有用。 然而，我们对于开放模型的采用所做的所有测量都非常不准确。 我们有吵闹的平台度量衡，来自初创企业的营销相关度量衡，以及不完善的公共评价。 它需要一个知情的眼睛找出事情的去向。 这些研究指出是目前我最接近我们的立场
和大多数互联互通的事情一样，我们的目标是建立比我们捕捉更多的价值，因此仪表板上的数据在可预见的将来仍将是免费的。 指出将会比我的许多文章更原始，他们将主要领取报酬，使我能够更自由地发言。 如果你在人工智能政策或政策制定研究中工作，请联系我们，我们有可能得到折扣或免费访问。
我今天要讨论的主要问题是：公开模型推论的市场是什么？它在生长吗？相对于OpenAI/Anthropic，它是否是一个四舍五入的错误？
以开放模型推论Baseten、Fireworks和Together AI作为这里的贝勒韦瑟(以及OpenRouter的代言量的开放模型份额，超标器可能也在这里有一大足迹，例如Amazon Bedrock和Azure的模型花园，但我还没有看到它们按模型类别细分使用。 初创企业的命运与开放式模型推论作为一个类别的成功有着更直接的联系。
关于最新数据，我们可以从我关于开放模式经济的演讲(在这里录制)中看一个幻灯片开始。 这些是开放模型初创企业公开披露的随时间推移记录尺度
这种显示近乎负责的增长，在GLM 5.2的步法变化之后的上升前，开放的agents. 例如，烟火宣布在本周的第一次公司会议上，他们每天为客户的定制型号提供超过40T的标志。 这仅仅是他们推论的一个部分，一个垂直的，他们称从这个夏天的2x增长，所以我会期望他们这个秋季的总数远高于100T的符号当我们得到下一次更新。
我对这些数字的第一直觉是把他们和任何封闭的模型或超规模公司都打成平地，至少就物量而言值得庆幸的是，Google 发布整个平台的符号卷和收入报告中的API使用量。 我们可以在开放模型推论平台上覆盖这两个数字，由此得出的图案如下：
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My longest running agent testbed has been building the Interconnects Dashboard — an aggregator of all of the best data on open model adoption and performance from across the web. This dashboard is freely available and becoming quite useful. Still, all the measurements we have on open model adoption are incredibly imprecise. We have noisy platform metrics, marketing-related metrics from startups, and imperfect public evaluations. It takes an informed eye to figure out where things are going. These research notes are my current best approximations of where we stand.
As with most things on Interconnects, our goal is to build well more value than we capture, so the data on the dashboard will remain free for the foreseeable future. These notes will be more raw than many of my articles, a place for sharing what I’m mulling over but don’t have complete evidence for (yet). They will be primarily paid, enabling me to speak more freely. If you work in AI policy or policy-facing research, please reach out to us and we can likely get discounted or free access.
The primary question I wanted to address today is: What is the market for open model inference? Is it growing? Is it a rounding error relative to OpenAI/Anthropic?
I’ve been building the best measurements I can here, primarily using the open model inference startups Baseten, Fireworks, and Together AI as the bellwethers here (along with the open model share of OpenRouter’s token volume, which they thankfully report in their API). The hyperscalers also likely have a major footprint here, e.g. Amazon Bedrock and Azure’s model garden, but I haven’t seen them break down usage by model category. The startups’ fates are much more directly correlated with the success of open model inference as a category.
For the latest data, we can start with a slide from my talk on the open model economy (recording here). These are the public disclosures from the open model startups on their token volume, over time, log-scale.
This shows near-exponential growth, before the uptick that would follow GLM 5.2’s step change in open agents. For example, Fireworks announced at their first company conference this week that they’re serving more than 40T tokens per day to customers’ customized models. This is just one slice of their inference, a vertical that they said grew over 2x from this summer, so I would expect their total number this fall to be well above 100T tokens when we get the next update.
My first instinct with these numbers has been to ground them versus any closed model or hyperscale company we can, at least in terms of token volume. Thankfully, Google releases token volumes for the entire platform and API usage in their earnings reports. We can overlay those two numbers on top of the open model inference platforms, with the resulting plot below:
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