Structured Output Benchmark (SOB)
Multi-source LLM benchmark testing JSON value accuracy across text, image, and audio inputs with 7 metrics.
How the 6.8 was reached
- Quality of output5/10
Seven metrics across text, image and audio, but rankings are the vendor's own and the repo has only 28 stars. github.com
- Ease of use8/10
Load the dataset in one line with the datasets library, or run a 5-record smoke test. github.com
- Pricing value9/10
MIT-licensed and free to use; running it needs model API keys from the user. github.com
- API and integration quality6/10
Python evaluation with support for OpenRouter, OpenAI, Anthropic and Google; no formal API reference described. github.com
- Solves the problem it claims to solve6/10
Measures value-level JSON correctness across text, image and audio as claimed; little independent adoption (28 stars). interfaze.ai
6.8/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
- Full benchmark access
- Leaderboard viewing
- 7 evaluation metrics
Key Features
- Multi-source input testing (text, image, audio)
- 7 distinct evaluation metrics
- JSON value accuracy per field
- Structure coverage analysis
- Type safety validation
Pros & Cons
Pros
- Goes beyond schema compliance to test actual value accuracy
- Multi-modal input support reflects real-world usage
- Separates different types of errors for better debugging
- Comprehensive 7-metric evaluation framework
Cons
- Limited to structured output evaluation only
- Appears to be research-focused rather than production tool
- No clear integration options for continuous testing
- Relatively new with limited adoption data
SOB addresses a real gap in LLM evaluation by testing value accuracy beyond just schema compliance. While valuable for researchers and developers working with structured outputs, it's primarily a benchmarking tool rather than a development platform.
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