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A new benchmark for testing LLMs for deterministic outputs

Introducing SOB: A Multi-Source Structured Output Benchmark for LLMs - Interfaze

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What is A new benchmark for testing LLMs for deterministic outputs?

SOB is a benchmark designed to test how well large language models can produce structured outputs like JSON. Unlike traditional benchmarks that only check if a model returns valid JSON schema, SOB measures whether the actual values inside each field are correct. It tests models across three input types: text, images, and audio. The benchmark includes evaluations of over 20 models using seven different metrics, with results displayed on a public leaderboard. This makes it useful for anyone choosing an LLM for tasks that require reliable structured data output, such as data extraction, form filling, or API integrations. The freemium model means you can explore the benchmark and results without payment, though some features may require a subscription.

Key features

Multi-source input testing

Evaluates LLM performance on JSON outputs generated from text, image, and audio inputs

Field-level accuracy measurement

Checks correctness of individual JSON field values, not just schema validity

20+ model coverage

Includes results from numerous language models on a public leaderboard

Multiple evaluation metrics

Uses seven different metrics to assess structured output quality from different angles

Freemium access

Public benchmark results and leaderboard available without charge

Pros & cons

Advantages

  • Focuses on practical accuracy rather than just technical schema compliance, which matters for real-world applications
  • Tests multiple input types, giving a broader picture of model capability across different data sources
  • Public leaderboard allows easy comparison of how different models perform at your specific task
  • Free access to benchmark results helps teams make informed model selection decisions

Limitations

  • Limited detail available about how the benchmark was constructed or which specific domains it covers
  • Leaderboard may not include the newest models, as adding new models to benchmarks takes time
  • Results show overall performance but may not reflect your specific use case or data characteristics

Use cases

Selecting an LLM for data extraction projects where output accuracy directly affects downstream processes

Evaluating which model to use for automated form filling or entity recognition tasks

Comparing model performance before building LLM-based APIs that return structured data

Assessing whether an LLM can reliably generate JSON outputs for database imports

Testing multi-modal AI pipelines that need to extract structured information from documents or images

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Pricing

Free

Free

Access to public benchmark results, leaderboard comparisons, and evaluation metrics across 20+ models

Premium

Contact for pricing

Likely includes custom benchmark runs, detailed analysis, and possibly API access for automated testing (specific features not publicly detailed)

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