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 these token counts every model on the list costs less than a cup of coffee per month, so cost is not the deciding factor — quality is. This is the clearest case in the whole set for ignoring the cheap column and buying on writing quality instead.
How much does it cost to write a blog post with AI?
$0.0001 on Mistral: Mistral Nemo, the cheapest capable option, rising to $0.011 on Claude Sonnet 5 at the top end. GPT-5.6 Luna is the value pick at $0.0013 per run. The job is priced at 1,200 input and 2,000 output tokens — see the working below.
How did you work out the token count for this task?
A brief, outline and a page of reference notes ≈ 900 words ≈ 1,200 input tokens. A 1,500-word draft ≈ 2,000 output tokens.
What does this cost at 30 posts a month?
$0.0025 a month on Mistral: Mistral Nemo, $0.040 on GPT-5.6 Luna, and $0.336 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 these token counts every model on the list costs less than a cup of coffee per month, so cost is not the deciding factor — quality is. This is the clearest case in the whole set for ignoring the cheap column and buying on writing quality instead.
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.