According to CoinMeta, Laminar has released a model flow-1 that specifically checks the execution trajectories of AI and agent. This model is capable of reading model calls, tool calls, and return results to identify errors that occur during the execution of agent. Based on 523 challenging trace tests conducted by Laminar, the error detection accuracy of flow-1 was 0.835, which is better than GPT-6-SOL's 0.816. For datasets with less than 100,000 LLM and tokens records each, the average analysis cost for flow-1 is about 0.0011 US dollars, whereas for GPT-6-SOL it is 0.026 US dollars, representing a cost difference of approximately 23 times. Although these results are currently based on Laminar's own internal testing and have not been verified by third parties, there are still doubts within the community regarding its performance in real production environments.