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LlamaIndex splits benchmarks into frontier and specialized categories

Jerry Liu@jerryjliu0
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Why we picked it

The practical implication is that teams evaluating models should build their own benchmark for the tasks they actually run, optimizing for the price-performance frontier rather than leaderboard accuracy. Practitioners choosing between frontier models and cheaper alternatives for document parsing and extraction can use ParseBench and ExtractBench as ready-made reference points. It also signals LlamaIndex pushing harder into long-horizon workflow research around document agents.

LlamaIndex founder Jerry Liu argues benchmarks fall into two types: those measuring frontier intelligence, such as TerminalBench and GDPval, where accuracy matters more than cost, and those measuring specialized intelligence, where the cost/accuracy Pareto curve matters more than accuracy alone. He notes that as type 1 benchmarks saturate, they become type 2, and that the largest frontier models at xhigh reasoning effort likely already saturate type 2 tasks but at price-prohibitive cost and latency. LlamaIndex built ParseBench and ExtractBench for cost-efficient document understanding and extraction, and is now investing in type 1 with ExtractBench Pro for document tasks that even frontier models struggle with.

Original · Jerry Liu

There are two main types of benchmarks: 1. those that measure frontier intelligence (terminalbench, gdpval, etc.). accuracy matters more than cost. 2. those that measure specialized intelligence. the pareto curve of cost/accuracy more than accuracy alone. both are important, but they are distinct benchmark categories. * as (1) saturates, they become (2). * the largest frontier models at xhigh likely already saturate (2), but they do so at a cost/latency point that's completely price-prohibitive. * every user should define (2) for the tasks they care about the most. we're big fans of both. we have so far dedicated outsized attention to (2) especially related to document tasks. we fundamentally think users should have the most price-efficient tools to understand, extract, and read over their documents. we've built parsebench, extractbench for this purpose. we are starting to invest in (1) as well, because certain document tasks are so challenging that even frontier models struggle (e.g. see extractbench pro). we are continuing our investment in long-horizon workflow research at @llama_index 💬 5 🔄 0 ❤️ 8 👀 1010 📊 7 ⚡

  • Ethan Mollick11-02 06:30原文
#LlamaIndex#benchmarks#ParseBench#ExtractBench#document AI

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