Beating OpenAI's structured outputs on cost, accuracy and speed
We leveraged a novel technique, schema-aligned parsing, to achieve SOTA on BFCL with every LLM.
Beating OpenAI's structured outputs on cost, accuracy and speed launched on August 14, 2024 with 8 votes, #1,861 of 5,471 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
What it does
We leveraged a novel technique, schema-aligned parsing, to achieve SOTA on BFCL with every LLM. Using BAML , we achieved state-of-the-art results for every model in the Berkeley function-calling benchmark (BFCL) — nearly solving it 1 . BAML is more accurate and cheaper for function calling than any native function calling API. It's easily 2-4x faster than OpenAI's FC-strict API. BAML's technique is model-agnostic and works with any model without modification (even open-source ones). gpt-3.5-turbo , gpt-4o-mini , and claude-haiku with BAML work almost as well as gpt4o with structured output (less than 2%) We used our prompting DSL (BAML) to achieve this[2], without using JSON-mode or any…from boundaryml.com
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