Cost for Sentiment Analysis on 100,000 Reviews with LLM APIs
Calculate the cost of using LLM APIs for sentiment analysis at scale. Compare all models for analyzing 100,000 reviews with verified per-million-token pricing.
⚡ Your Workload
📊 Cost Summary
Cost per reviews across 153 models
Show all 153 models in a table
| Model | Provider | Input $/M | Output $/M | Cost for 100K reviews |
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How this calculator works
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 and verified by our automated scraper pipeline that runs 3× daily. No fabricated numbers — every price links to its source.
Frequently asked questions
How much does it cost to analyze sentiment on 100,000 reviews?
Sentiment analysis on 100,000 reviews costs $1-3 with budget models, $5-15 with mid-tier models, and $30-90 with frontier models. At this scale, the per-review cost is fractions of a cent, making LLM sentiment analysis dramatically cheaper than human review.
Which LLM API is cheapest for sentiment analysis?
For high-volume sentiment analysis, the cheapest models are optimal: DeepSeek V3, Gemini Flash Lite, and Groq-hosted Llama. Since output is just a sentiment label (~5 tokens), input pricing is the main cost driver. At 100K+ reviews, even mid-tier models are affordable.
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.