Cost to Power 10,000 Chatbot Conversations with LLM APIs

Each chatbot conversation (multi-turn) requires ~3,000 input tokens (system prompt + conversation history + user messages) and ~1,000 output tokens (bot responses across multiple turns). Multi-turn conversations accumulate input tokens as context grows. Longer conversations with more turns will increase input tokens per conversation.

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 run 10,000 chatbot conversations with an LLM?

It depends on the model. Multi-turn chat is input-heavy (~3,000 input / ~1,000 output tokens per conversation) because context grows with each message. The calculator above computes the exact current cost for every model, cheapest-first, from live per-million-token pricing; models that support prompt caching can cut repeat-context costs substantially. See exact per-model cost in the table above.

Which LLM API is cheapest for chatbots?

The cheapest are the models at the top of the cost table above. Multi-turn chat is input-heavy because context grows each turn, so low input pricing and prompt caching matter most — models that support cached input can meaningfully reduce repeat-context costs.

How are chatbot token costs calculated?

Each conversation uses ~3,000 input tokens (context + history) and ~1,000 output tokens (responses). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_conversations. Prices are per million tokens from verified provider pricing.