Cost to Process 1,000 Support Tickets with LLM APIs

Each support ticket typically requires ~1,500 input tokens (system prompt, conversation history, knowledge base context, customer message) and ~400 output tokens (the AI response). This estimate assumes a RAG-enhanced support agent that retrieves relevant documentation before responding. Actual token usage varies by ticket complexity, context window size, and whether multi-turn conversations are needed.

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 process 1,000 support tickets with an LLM API?

Cost depends entirely on the model. Each ticket is input-heavy (~1,500 input / ~400 output tokens), so input price dominates. Across the models we track the gap between the cheapest and most expensive is large — commonly more than 50x. Rather than quote a figure that goes stale, the calculator above computes the exact current cost for every model, cheapest-first, from live per-million-token pricing.

Which LLM API is cheapest for customer support automation?

The cheapest option is whichever model sits at the top of the cost table above — it is sorted cheapest-first and recomputed from current pricing on every update. For support automation the lowest-input-price models win, since each ticket sends far more input than output tokens. Weigh that against answer quality for your ticket mix.

How are support ticket token costs calculated?

Each ticket uses ~1,500 input tokens (context + customer message) and ~400 output tokens (AI response). Total cost = (input_tokens × input_price_per_Mtok + output_tokens × output_price_per_Mtok) × number_of_tickets. All prices are per million tokens, sourced from official provider pricing pages.