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If you're still using BERT-style token classification models for NER tagging in production, you should probably reevaluate.

Last summer, we replaced our token classification model with Google's Gemini 2.5 Flash Lite for NER tagging people, companies, and locations on millions of news articles per day. At first, it felt wrong and overkill to replace a well-established, standard approach with a generative model. However, on our own evaluation datasets, the LLM outperformed every BERT model we had implemented previously because it brings so much more contextual understanding to the task.

There are a few obvious advantages to using LLMs for NER tagging. For example, LLMs can easily handle text like "Michael and Jennifer Smith" and correctly extract both "Michael Smith" and "Jennifer Smith" as separate people. They are also much better at handling formatting issues and messy edge cases you inevitably encounter in real-world data at scale.

Deployment is also dramatically simpler: instead of managing model serving infrastructure, you're calling an inference API that you can parallelize and scale easily. Additionally, you automatically benefit from LLMs getting better and cheaper over time without changing anything on your end, provided that you have a solid eval dataset.

We're now processing close to 1B input tokens and producing 100M output tokens per day on this pipeline alone. The most popular pre-trained NER models on Hugging Face are still downloaded millions of times per month, which tells me that structured text extraction with LLMs is one of the most underrated applications right now.

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