Cost = (input tokens ÷ 1M × input price) + (output tokens ÷ 1M × output price). Prices come from our daily-verified model data. Batch and cached-input discounts are not applied — they only ever make these numbers smaller.
“Needs chunking” means the model's context window can't hold this job in a single pass, so the real cost is higher than the figure shown and quality usually suffers.
Should you just pick the cheapest?
At 5,000 tickets the gap between the cheapest and the most expensive model is real money, which makes this the classic case for routing: a budget model drafts, and tickets it flags as uncertain escalate to a premium model or a human.
How much does it cost to answer a support ticket with AI?
$0.0001 on Mistral: Mistral Nemo, the cheapest capable option, rising to $0.0053 on Claude Sonnet 5 at the top end. GPT-5.6 Luna is the value pick at $0.0006 per run. The job is priced at 3,500 input and 350 output tokens — see the working below.
How did you work out the token count for this task?
The ticket, prior thread history and three retrieved help-centre articles ≈ 2,600 words ≈ 3,500 input tokens. A 260-word reply ≈ 350 output tokens.
What does this cost at 5,000 tickets a month?
$0.403 a month on Mistral: Mistral Nemo, $2.80 on GPT-5.6 Luna, and $26.25 on Claude Sonnet 5. Batch APIs typically halve these figures for work that can wait, and prompt caching cuts the input side further when the same context is reused.
Is the cheapest model the right choice for this task?
At 5,000 tickets the gap between the cheapest and the most expensive model is real money, which makes this the classic case for routing: a budget model drafts, and tickets it flags as uncertain escalate to a premium model or a human.
Are these prices current?
Yes. Every figure on this page is computed from our model pricing data, which is checked daily against each provider's official pricing page. When a provider changes a price, these numbers change with it.