Cost to Classify 10,000 Emails with LLM APIs
Calculate the cost of using LLM APIs for email classification and routing. Compare all models for classifying 10,000 emails with verified per-million-token pricing.
⚡ Your Workload
📊 Cost Summary
Cost per emails across 153 models
Show all 153 models in a table
| Model | Provider | Input $/M | Output $/M | Cost for 10K emails |
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How this calculator works
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 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 classify 10,000 emails with an LLM?
Classifying 10,000 emails costs under $1 with most budget models, $2-5 with mid-tier models, and $10-30 with frontier models. Email classification is one of the cheapest LLM workloads because output is just a short label (~10 tokens).
Which LLM API is cheapest for email classification?
Since classification needs minimal output, the cheapest models with low input pricing are best: DeepSeek V3, Gemini Flash Lite, and Groq-hosted Llama models. At high volume, even GPT-4.1 Mini is cost-effective at ~$0.01 per email.
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.