Jiyuan Ludong, a startup founded by former Huawei Pangu large model lead Wang Yunhe, has closed a new funding round that values the company at several hundred million dollars, with total funding now in the tens of millions of dollars. The round was led by Honghui Fund. The company’s core product is Routing Harness, a system designed to let agents automatically choose and switch between different models while carrying out tasks. Its OpenSquilla project has around 6,400 stars on GitHub, while the company’s multi-model API platform has reached 54,000 users and more than 500 billion daily token calls. In evaluation results published by the company, a domestic multi-model combination outperformed Fable 5 on the DRACO research task at roughly one-third of the cost. Wang said the next step is to train a proprietary model using real task data, model-selection records, and user feedback gathered through the Harness system, extending the product path from helping agents choose models to building an Agent-Native Model.
Jiyuan Ludong, a startup founded by former Huawei Pangu large model lead Wang Yunhe, has completed a new funding round that values the company at several hundred million dollars. Its cumulative funding has reached the tens of millions of dollars, and the latest round was led by Honghui Fund.
Company focus remains on multi-model routing
The company’s main product is Routing Harness, which is designed to let agents automatically select and switch between different models while performing tasks.
Its OpenSquilla project has about 6,400 stars on GitHub. Jiyuan Ludong also said its multi-model API platform has 54,000 users, with daily token calls surpassing 500 billion.
Evaluation results and model training plan
According to evaluation results released by the company, a domestic multi-model combination outperformed Fable 5 on the DRACO research task, while costing about one-third as much.
Wang’s next step is to train the company’s own model using real tasks, model-selection data, and user feedback collected through the Harness system. The goal is to move from helping agents choose models to building an Agent-Native Model.
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