Structured Output Benchmark (SOB)
Multi-source LLM benchmark testing JSON value accuracy across text, image, and audio inputs with 7 metrics.
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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. Source: github.com
- Ease of use8/10
Load the dataset in one line with the datasets library, or run a 5-record smoke test. Source: github.com
- Pricing value9/10
MIT-licensed and free to use; running it needs model API keys from the user. Source: github.com
- API and integration quality6/10
Python evaluation with support for OpenRouter, OpenAI, Anthropic and Google; no formal API reference described. Source: 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). Source: interfaze.ai
6.8/10 is the average of the five criterion scores above. The evidence comes from 2 different sources.Scores come from public evidence (the tool's own pages and documentation, and independent reviews); we do not test every tool hands-on. Scored 29 September 2026, on version 1 of our five-criteria scoring method.
Pricing
- Full benchmark access
- Leaderboard viewing
- 7 evaluation metrics
For the full list of what each plan includes, and current prices, see github.com.
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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