Over the past few weeks, we’ve been running open weight Large Language Models through Deep Agents harness evaluations, and the initial results show they are a viable option to use instead of, and alongside, closed frontier models. GLM-5 (z.ai) and MiniMax M2.7 each score similarly to closed frontier models on core agent tasks such as file operations, tool use, and instruction following.
This isn’t...
The narrative presents open-weight LLMs as a pragmatic, cost-effective alternative to closed frontier models, emphasizing their performance parity in agentic tasks. This is a strong argument: the data shows open models like GLM-5 and MiniMax M2.7 achieving correctness scores within 5-10% of closed models, while offering dramatic cost savings (e.g., $87k annually for high-throughput workloads) and lower latency. The piece avoids hyperbole, grounding claims in specific benchmarks and real-world pr...