Which model for which job
The right model genuinely differs by workload, and the reason is usually arithmetic rather than benchmarks. Summarisation is almost entirely input, so the input rate is the whole bill. Content generation is the mirror image and collapses to the output rate. An agent’s cost is mostly cached input; a search index has no output side at all. Each page below prices a real monthly volume against both our rate and the official one.
Coding agents
42%Which open-weight models suit an autonomous coding agent, what a month of real agent traffic costs, and why cached-input pricing dominates the bill.
RAG and retrieval
42%What embeddings and generation actually cost for a retrieval-augmented app, priced at a real corpus size rather than per thousand tokens.
Chatbots and support
42%Costing a production chat or support assistant, including the conversation-history growth that makes chat bills grow faster than traffic.
Document summarisation
40%What it costs to summarise documents at volume, why the input rate is effectively the entire bill, and where a long-context model stops being optional.
Structured data extraction
42%Pulling schema-valid JSON out of documents at volume, priced per month, and why the retry rate — not the headline rate — decides which model is actually cheapest.
Semantic search and embeddings
40%What it costs to embed a corpus and serve semantic search over it, priced at a real corpus size — and why the token bill is rarely the expensive part.
Translation and localisation
42%Costing a machine translation or localisation pipeline, where output roughly equals input — the one workload shape where the output rate carries half the bill.
Content generation
42%Pricing long-form generation at volume: the inverse of summarisation, where a short brief goes in, thousands of tokens come out, and only the output rate matters.