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LLM output formats: Markdown, JSON, and raw text compared for token efficiency

By PulseAugur Editorial · [1 sources] ·

Choosing the right output format for language models is crucial for both cost efficiency and accuracy. Raw text is the cheapest but least reliable, while Markdown offers a balance for human readability and model understanding. JSON provides machine-native reliability, essential for automated systems like ERPs, but requires careful schema design to avoid token bloat from verbose keys, nesting, and whitespace. Structured parsing with schema validation is recommended over simple prompt-based extraction to ensure predictable and accurate outputs for downstream automation. AI

IMPACT Optimizing LLM output formats can significantly reduce costs and improve the reliability of AI-driven automation workflows.

RANK_REASON Article discusses best practices for LLM output formats and token efficiency, which is a practical application of LLM technology.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM output formats: Markdown, JSON, and raw text compared for token efficiency

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Article discusses best practices for LLM output formats and token efficiency, which is a practical application of LLM technology.
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  1. dev.to — LLM tag TIER_1 English(EN) · Guilherme Michels ·

    Markdown vs JSON vs Raw Text for LLMs: Which Format Uses Fewer Tokens?

    <p>Every prompt you send to a language model has a price and a failure mode. The format you feed in — and the format you ask the model to return — quietly decides how much you pay per document and how often the output breaks your automation downstream.</p> <h2> Why token efficien…

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