Cost to Classify 10,000 Emails with LLM APIs

Each email classification requires ~500 input tokens (email subject + body) and ~10 output tokens (the classification label/category). Classification is extremely output-light, making it one of the cheapest LLM workloads. Models with very low input pricing are ideal. At scale, this becomes a high-volume, low-cost-per-unit pipeline.

Formula: cost = (input_tokens × input_price_per_Mtok + output_tokens × output_price_per_Mtok) × quantity / 1,000,000

All prices are per million tokens, sourced directly from official provider pricing pages. No fabricated numbers — every model in the table links to its detail page, which cites the official pricing source.

How much does it cost to classify 10,000 emails with an LLM?

It is one of the cheapest LLM workloads because the output is a short label (~10 tokens) against ~500 input tokens. Exact cost still depends on the model; the calculator above computes the exact current cost for every model, cheapest-first, from live per-million-token pricing, so you can see the real cost at your email volume in the table above.

Which LLM API is cheapest for email classification?

Since output is minimal, the cheapest models with low input pricing win — the calculator sorts them cheapest-first, so the top of the table is your lowest cost per email. At high volume even mid-tier models stay affordable; compare exact figures above.

How are email classification token costs calculated?

Each email uses ~500 input tokens (email content) and ~10 output tokens (classification label). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_emails. Prices are per million tokens from verified provider pricing.