GLM-5.1: Towards Long-Horizon Tasks
GLM-5.1: Towards Long-Horizon Tasks
I am working on a large scale dataset for producing agent traces for Python <> cython conversion with tooling, and it is second only to gemini pro 3.1 in acceptance rates (16% vs 26%).
Mid-sized models like gpt-oss minimax and qwen3.5 122b are around 6%, and gemma4 31b around 7% (but much slower).
I haven’t tried Opus or ChatGPT due to high costs on openrouter for this application.
Same thing I noticed.
My use cases are not code editing or authoring related, but when it comes to understanding a codebase and it's docs to help stakeholders write tasks or understand systems it has always outperformed american models at roughly half the price.