Cost for Sentiment Analysis on 100,000 Reviews with LLM APIs

Each sentiment analysis requires ~300 input tokens (the review text) and ~5 output tokens (sentiment label + confidence). This is one of the cheapest LLM workloads per unit — the output is minimal. At 100K+ reviews, the total cost is still low even with mid-tier models. Budget models can handle this at near-zero cost per review.

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 analyze sentiment on 100,000 reviews?

It is one of the cheapest LLM workloads per unit — ~300 input and ~5 output tokens per review. At 100K+ reviews the per-review cost is fractions of a cent for the lowest-priced models. The calculator above computes the exact current cost for every model, cheapest-first, from live per-million-token pricing, so you can read the exact total for your volume in the table above.

Which LLM API is cheapest for sentiment analysis?

For high-volume sentiment analysis the cheapest models — those with the lowest input pricing — are optimal, and the calculator ranks them cheapest-first at the top of the table. Output is just a label, so input price is the main cost driver.

How are sentiment analysis token costs calculated?

Each review uses ~300 input tokens (review text) and ~5 output tokens (sentiment label). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_reviews. Prices are per million tokens from official provider pricing.