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[2602.19467] Can Large Language Models Replace Human Coders? Introducing ContentBench
Summary
This article introduces ContentBench, a benchmark suite assessing the ability of low-cost large language models (LLMs) to perform interpretive coding tasks, comparing their effectiveness against human coders.
Why It Matters
As LLMs become more prevalent in various fields, understanding their capabilities and limitations in tasks traditionally performed by humans is crucial. This research provides insights into the potential of LLMs to automate content analysis, which could reshape workflows in academia and industry.
Key Takeaways
- ContentBench evaluates LLMs' effectiveness in interpretive coding tasks.
- Top low-cost LLMs achieve 97-99% agreement with human-coded labels.
- The benchmark suite invites community contributions for ongoing evaluation.
- Small models struggle with sarcasm, indicating limitations in nuanced understanding.
- The findings could shift focus from labor costs to validation and governance in coding tasks.
Computer Science > Computers and Society arXiv:2602.19467 (cs) [Submitted on 23 Feb 2026] Title:Can Large Language Models Replace Human Coders? Introducing ContentBench Authors:Michael Haman View a PDF of the paper titled Can Large Language Models Replace Human Coders? Introducing ContentBench, by Michael Haman View PDF Abstract:Can low-cost large language models (LLMs) take over the interpretive coding work that still anchors much of empirical content analysis? This paper introduces ContentBench, a public benchmark suite that helps answer this replacement question by tracking how much agreement low-cost LLMs achieve and what they cost on the same interpretive coding tasks. The suite uses versioned tracks that invite researchers to contribute new benchmark datasets. I report results from the first track, ContentBench-ResearchTalk v1.0: 1,000 synthetic, social-media-style posts about academic research labeled into five categories spanning praise, critique, sarcasm, questions, and procedural remarks. Reference labels are assigned only when three state-of-the-art reasoning models (GPT-5, Gemini 2.5 Pro, and Claude Opus 4.1) agree unanimously, and all final labels are checked by the author as a quality-control audit. Among the 59 evaluated models, the best low-cost LLMs reach roughly 97-99% agreement with these jury labels, far above GPT-3.5 Turbo, the model behind early ChatGPT and the initial wave of LLM-based text annotation. Several top models can code 50,000 posts for onl...