How does a harness affect LLM Behaviour? Frontier models perform really well on benchmarks. So we measured how a harness can affect their behaviour: - We run GLM-5.3, Inkling and Kimi-K3 on our release - We run the eval through mini, Pi and OpenCode and measure a few things - First-models generalise really well and the differences exist at the token level - Kimi is the largest model but most token efficient when paired with Pi - Pick your combination carefully for any model - Failure on a task counterintuitively happens not because a model is lazy. Infact the models spend twice as much compute and steps, they try harder - Each harness gives different tools, how the models pick them varies. GLM prefers testing the most, Inkling spends most of its time reading files. It was a huge honor to be part of @reflection_ai x @scale_AI release. I learned a lot 🙏 labs.scale.com/blog/swe-benc…