The gap between closed and open models might be much smaller than commonly assumed, because we don’t know what closed model providers do *in addition to* model inference

When Claude dominates GLM-5.2 in benchmarks, it’s usually assumed that Anthropic has superior model architectures, superior training pipelines, and other advanced machine learning techniques that make their models better than the competition. But actually, this doesn’t follow. Because the benchmarks compare model inference on GLM with the whole Claude product, and we don’t know what that product does behind the scenes. Anthropic already redacts reasoning traces and doesn’t give you access to the full conversation. They could easily be using RAG/knowledge injection, e.g. for software documentation Prompt preprocessing Context-dependent system prompts Hidden internal tool calls “Clown-car MoE“/shelling out to specialized expert models all of which can dramatically improve model performance, and serve the entire thing as “Claude” over their API. You wouldn’t know about it and when benchmarking Claude against an open model, you’d effectively be comparing apples to oranges. It’s perfectly possible that they don’t have a single model whose inference output beats open models.