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When an LLM receives 500 records as JSON, it gets 53,341 tokens of repeated field names (`\"qualified_name\":`, `\"kind\":`, `\"score\":` on every record). At this scale, GPT-5.5 returns empty strings. Claude Opus spends 143 lines manually enumerating symbols and still gets the wrong answer. JSON averages 54.1% comprehension accuracy across 10 models.\n\n## What GCF does differently\n\nGCF declares field names once in a header. Rows are positional values. Section headers (`## targets`, `## related`) replace per-record metadata. The same 500-record payload uses 11,090 tokens instead of 53,341.\n\n- **79% fewer input tokens** than JSON\n- **63% fewer output tokens** than JSON\n- **91.2% comprehension accuracy** across 10 models and 3 providers (Anthropic, OpenAI, Google)\n- **Four models hit 100%**: Claude Sonnet, Gemini 2.5 Pro, Gemini 3.1 Pro, Gemini 3.5 Flash\n- **5/5 generation validity** on every frontier model with zero prior training\n- **Wins all 6 datasets** on TOON's own benchmark using their datasets and tokenizer\n\n## The benchmark\n\n1,300+ LLM evaluations. 23 comprehension runs. 28 generation runs. 10 models. 3 providers. Deterministic ground truth, no LLM judge, reproducible from one command.\n\n### Comprehension (can the model read it?)\n\n| Model | GCF | TOON | JSON |\n|:---|:---|:---|:---|\n| Claude Opus 4.6 | **96.2%** | 84.6% | 73.1% |\n| Claude Sonnet 4.6 | **100%** | 73.1% | 53.8% |\n| Claude Haiku 4.5 | **96.2%** | 69.2% | 57.7% |\n| GPT-5.5 | **84.1%** | 67.7% | 45.8% |\n| GPT-5.4 | **78.0%** | 56.0% | 44.1% |\n| GPT-5.4-mini | **71.8%** | 64.1% | 54.2% |\n| Gemini 2.5 Pro | **100%** | 76.9% | 58.3% |\n| Gemini 3.1 Pro | **100%** | 76.9% | 46.2% |\n| Gemini 3.5 Flash | **100%** | 61.5% | 46.2% |\n| Gemini 2.5 Flash | **80.6%** | 54.1% | 57.0% |\n\nGCF \u003e TOON \u003e JSON on every model from every provider. No exceptions.\n\n### Generation (can the model write it?)\n\n| Model | GCF | TOON | JSON |\n|:---|:---|:---|:---|\n| Claude Opus 4.6 | **5/5** | 0/5 | 5/5 |\n| Claude Sonnet 4.6 | **5/5** | 2-3/5 | 5/5 |\n| GPT-5.5 | **4-5/5** | 1-2/5 | 5/5 |\n| GPT-5.4 | **5/5** | 0/5 | 5/5 |\n| Gemini 2.5 Pro | **5/5** | 1/5 | 5/5 |\n| Gemini 3.1 Pro | **5/5** | 0/5 | 5/5 |\n\nTOON's official decoder rejects LLM-generated output on 7 of 9 models. GCF achieves 5/5 on every frontier model with zero training.\n\n### Token efficiency\n\n| Format | Tokens (500 symbols) | vs JSON |\n|:---|:---|:---|\n| **GCF** | **11,090** | **79% fewer** |\n| TOON | 16,378 | 69% fewer |\n| JSON | 53,341 | baseline |\n\n## How to try it\n\n```bash\npip install gcf-proxy\n```\n\nWrap any MCP server. Zero code changes. Your server keeps outputting JSON, the model receives GCF.\n\n## Links\n\n- [Live site](https://betterthanjson.com)\n- [Full benchmarks](https://gcformat.com/guide/benchmarks.html)\n- [Full eval data](https://gcformat.com/guide/eval-results.html)\n- [GCF Specification](https://github.com/blackwell-systems/gcf)\n- [GCF Proxy](https://github.com/blackwell-systems/gcf-proxy)\n- [Playground (live three-way comparison)](https://gcformat.com/playground.html)\n- [Whitepaper (DOI: 10.5281/zenodo.20579817)](https://doi.org/10.5281/zenodo.20579817)\n- Implementations: [Go](https://github.com/blackwell-systems/gcf-go) | [TypeScript](https://github.com/blackwell-systems/gcf-typescript) | [Python](https://github.com/blackwell-systems/gcf-python) | [Rust](https://github.com/blackwell-systems/gcf-rust) | [Swift](https://github.com/blackwell-systems/gcf-swift) | [Kotlin](https://github.com/blackwell-systems/gcf-kotlin)\n\n## License\n\nMIT - [Dayna Blackwell](https://github.com/blackwell-systems)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblackwell-systems%2Fbetterthanjson","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblackwell-systems%2Fbetterthanjson","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblackwell-systems%2Fbetterthanjson/lists"}