https://github.com/blackwell-systems/betterthanjson
https://github.com/blackwell-systems/betterthanjson
ai-agents benchmark comparison data-serialization evaluation gcf graph-compact-format json json-alternative landing-page llm llm-benchmark mcp model-context-protocol structured-data token-efficiency token-optimization toon web wire-format
Last synced: 1 day ago
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- Host: GitHub
- URL: https://github.com/blackwell-systems/betterthanjson
- Owner: blackwell-systems
- Created: 2026-06-06T21:53:39.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2026-06-24T00:17:00.000Z (16 days ago)
- Last Synced: 2026-06-24T02:13:06.963Z (15 days ago)
- Topics: ai-agents, benchmark, comparison, data-serialization, evaluation, gcf, graph-compact-format, json, json-alternative, landing-page, llm, llm-benchmark, mcp, model-context-protocol, structured-data, token-efficiency, token-optimization, toon, web, wire-format
- Language: HTML
- Size: 94.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
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README
# betterthanjson.com
**GCF is better than JSON for AI agents.** Full benchmark data proving it.
## Why JSON fails at scale
JSON was designed in 2001 for browser-to-server communication. 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.
## What GCF does differently
GCF 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.
- **79% fewer input tokens** than JSON
- **63% fewer output tokens** than JSON
- **91.2% comprehension accuracy** across 10 models and 3 providers (Anthropic, OpenAI, Google)
- **Four models hit 100%**: Claude Sonnet, Gemini 2.5 Pro, Gemini 3.1 Pro, Gemini 3.5 Flash
- **5/5 generation validity** on every frontier model with zero prior training
- **Wins all 6 datasets** on TOON's own benchmark using their datasets and tokenizer
## The benchmark
1,300+ LLM evaluations. 23 comprehension runs. 28 generation runs. 10 models. 3 providers. Deterministic ground truth, no LLM judge, reproducible from one command.
### Comprehension (can the model read it?)
| Model | GCF | TOON | JSON |
|:---|:---|:---|:---|
| Claude Opus 4.6 | **96.2%** | 84.6% | 73.1% |
| Claude Sonnet 4.6 | **100%** | 73.1% | 53.8% |
| Claude Haiku 4.5 | **96.2%** | 69.2% | 57.7% |
| GPT-5.5 | **84.1%** | 67.7% | 45.8% |
| GPT-5.4 | **78.0%** | 56.0% | 44.1% |
| GPT-5.4-mini | **71.8%** | 64.1% | 54.2% |
| Gemini 2.5 Pro | **100%** | 76.9% | 58.3% |
| Gemini 3.1 Pro | **100%** | 76.9% | 46.2% |
| Gemini 3.5 Flash | **100%** | 61.5% | 46.2% |
| Gemini 2.5 Flash | **80.6%** | 54.1% | 57.0% |
GCF > TOON > JSON on every model from every provider. No exceptions.
### Generation (can the model write it?)
| Model | GCF | TOON | JSON |
|:---|:---|:---|:---|
| Claude Opus 4.6 | **5/5** | 0/5 | 5/5 |
| Claude Sonnet 4.6 | **5/5** | 2-3/5 | 5/5 |
| GPT-5.5 | **4-5/5** | 1-2/5 | 5/5 |
| GPT-5.4 | **5/5** | 0/5 | 5/5 |
| Gemini 2.5 Pro | **5/5** | 1/5 | 5/5 |
| Gemini 3.1 Pro | **5/5** | 0/5 | 5/5 |
TOON's official decoder rejects LLM-generated output on 7 of 9 models. GCF achieves 5/5 on every frontier model with zero training.
### Token efficiency
| Format | Tokens (500 symbols) | vs JSON |
|:---|:---|:---|
| **GCF** | **11,090** | **79% fewer** |
| TOON | 16,378 | 69% fewer |
| JSON | 53,341 | baseline |
## How to try it
```bash
pip install gcf-proxy
```
Wrap any MCP server. Zero code changes. Your server keeps outputting JSON, the model receives GCF.
## Links
- [Live site](https://betterthanjson.com)
- [Full benchmarks](https://gcformat.com/guide/benchmarks.html)
- [Full eval data](https://gcformat.com/guide/eval-results.html)
- [GCF Specification](https://github.com/blackwell-systems/gcf)
- [GCF Proxy](https://github.com/blackwell-systems/gcf-proxy)
- [Playground (live three-way comparison)](https://gcformat.com/playground.html)
- [Whitepaper (DOI: 10.5281/zenodo.20579817)](https://doi.org/10.5281/zenodo.20579817)
- 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)
## License
MIT - [Dayna Blackwell](https://github.com/blackwell-systems)