Prices are quoted per million tokens, but the bill depends on how many tokens you send, how many you get back, and which pricing rules apply. Figures below were checked against the providers' own pages on 13 November 2026.
The formula
cost = input_tokens / 1e6 * input_price + output_tokens / 1e6 * output_price, times requests per day, times 30. A support bot sending 1,500 input and 400 output tokens, 1,000 times a day, on Claude Sonnet 5.5 ($2 in, $10 out per million) costs $210.00 a month; GPT-6.1 Sol lists the same prices (Anthropic pricing, OpenAI pricing). You can reproduce it in an LLM API cost calculator.
Lever 1: output costs more
Across 65 models in our price table, output is 1.0x to 10.0x the input price, with a median of 5.0x. On Sonnet 5.5 a chat turn is about 21% output by tokens but 57% of the cost, so trimming replies usually saves more than trimming prompts.
Lever 2: caching
Cached reads cost 0.1x the input price on most Claude models and on GPT-5.6 and later, and 0.05x on models such as Sonnet 5.5 and GPT-6.1 Sol; writing a cache entry costs 1.25x (Anthropic prompt caching). For a 10,000-token fixed prompt at 1,000 requests a day, our worked example falls from $690.00 to $120.72 a month. Minimum prompt lengths apply, so short system prompts will not cache.
Lever 3: batch
Anthropic, OpenAI and Google charge 50% less for jobs that can wait up to a day (OpenAI Batch API). The chatbot above falls from $210 to $105 a month.
Lever 4: very long prompts
OpenAI steps up above 272K input tokens, Gemini 3.1 Pro and Grok above 200K, Claude Haiku 5.5 above 100K. A 300K-token prompt costs about $0.61 on Sonnet 5.5 and $6.08 on GPT-6 Astra (our arithmetic from the published prices).
Compare tiers, not brands
On three reference workloads, Sonnet 5.5 and GPT-6.1 Sol cost the same; the small tier (Haiku 5.5 or GPT-6 Luna) costs $10.50, $9.00 and $8.52 a month, and flagships run 2 to 3 times the mid tier. Run the same 50 to 100 real prompts through each candidate and divide cost by passing answers. The full table is in this OpenAI API pricing breakdown alongside the other providers.
Top comments (1)
The per-call formula is right, but the bill is per task, and the conversion factor is the one most teams never measure: calls per finished task. Once you have it, a 20% per-call saving is only 20% off the bill if the call count holds.
That is the trap with cheaper models. If the cheaper one needs more tool iterations, then 0.8 x 1.15 calls = 0.92 - you saved 8%, not 20%. Same thing happens with lower reasoning effort that forces a second attempt, and with context trimming that forces a re-read.
So the fourth-and-a-half lever is not a lever, it is the denominator. Log calls per completed task next to cost per call; only the pair tells you which direction a change actually moved.