Introducing the Fusion API, the smartest compound model in the market. Fusion achieves Fable-level intelligence at half the price. How it works 👇

Introducing the Fusion API, the smartest compound model in the market. Fusion achieves Fable-level intelligence at half the price. How it works 👇

Fable 5 is state-of-the-art on nearly all tested benchmarks, with exceptional performance in software engineering, knowledge work, scientific research, and vision. The longer and more complex the task, the larger Fable 5’s lead over our other models.

PR 1000 landing today. Code Factory is fully functioning. Agents write/review/land 100% of our code now. Don't get me wrong - there's a *lot* of work that I do crafting PRDs, steering conversations, etc, but the majority of the work is done by agents now.

SWE-Bench style grading has been the standard for years now - you ask the agent to solve an issue and then run its code on a pre-constructed unit test. The problem is that passing a unit test is only one part of writing production-ready code. You also want to evaluate agents on a number of other axes, including scope, coding style, and unintended side effects. The result is our new benchmark FrontierCode - which has ~80% fewer false positives and for which the best model (Opus 4.8) only scores 13%! "Where others grade like a CI, FrontierCode grades like a tech lead."

The belief at the time was that model costs were halving every 6 months meaning tokens would get cheap and so application layer companies would need to find a way to charge for the value of tokens by selling the work / services. What actually happened is AI got much more expensive than people realized at the time. The shift from chat to agents led to an explosion in cost. One user could trigger hundreds of agents and each of those agents could trigger more agents. Agents started running longer and more autonomously. On top of that frontier models like Mythos are getting more expensive not less. If you look at what is happening in engineering, the coding companies are doing incredibly well selling tokens because engineers are consuming so many rather than needing to sell the value of the work. In fact, there is starting to be a massive demand for enterprise infrastructure to help large organizations track, manage and optimize their agents / tokens. Now the problem for application layer companies is how do you take that large token cost and convert it into something useful for your customers. A rough analogy is every company is about to get the ability to hire infinite employees. The main challenge is going to be figuring out how to manage those employees and make your business model work the same way it did with human employees. You can think about the previous generation of enterprise SaaS as building tools so that organizations could manage a large number of humans and make them productive. All of this is going to get rebuilt to support hybrid human / agent organizations. Some will be built by model / cloud, some by existing enterprise SaaS and some by new cos. For Harvey this means we don’t have to become a services company. The infrastructure for every law firm to deploy, train and manage a large number of agents is going to be so complex that model / cloud providers and law firms likely won’t build all of it. And AI is going to be expensive enough that we can capture something that looks like labor spend which is much closer to services without actually having to sell services.