bpp wins on tokens and loses on being read correctly. Across 8 models and ~770K records, mean comprehension error was GCF 31% / JSON 40% / bpp 54%. The failure is mechanical: bpp interns repeated values into a pointer table and models return the pointer instead of the value. The token saving is erased the moment you price the errors.
23 comprehension runs across 10 models (Claude Opus/Sonnet/Haiku, GPT-5.5/5.4/5.4-mini, Gemini 2.5 Flash/Pro, Gemini 3.1 Pro, Gemini 3.5 Flash). Generation eval across 11 models and 3 providers (Anthropic, OpenAI, Google). GCF wins 22, ties 1, loses 0 on comprehension. GCF achieves 5/5 valid generation on every frontier model with zero prior training. TOON fails 0/5 on generation with Opus, GPT-5.4, GPT-5.4-mini, Gemini 3.1 Pro, and Gemini 3.1 Flash Lite. JSON breaks on input at 500 symbols.
We inserted GCF into TOON’s benchmark harness. Same datasets, same tokenizer, same methodology. GCF uses 34% fewer tokens on mixed-structure data, matches TOON on flat data, and achieves 100% LLM comprehension accuracy where JSON fails at 66.7%.