OpenAI Cost Calculator

The fastest way to understand OpenAI pricing is to run a concrete scenario. Take a customer-support chatbot handling 10,000 conversations a month, with about eight exchanges per conversation. Each request carries roughly 1,500 input tokens — the system prompt plus the running conversation history — and produces around 200 output tokens. That adds up to 80,000 requests, or about 120 million input and 16 million output tokens per month.

On GPT-6 Luna, at the October 2026 rates shown in the calculator above, that workload costs about $20 a month. The same traffic costs about $400 on GPT-6.1 Sol and about $2,000 on the flagship GPT-6 Astra — a 100× spread for identical traffic. Nothing about the product changed — only the model did. Model choice, not traffic volume, is usually the single biggest lever on an OpenAI invoice.

Document pipelines behave differently: summarization jobs are input-heavy — long documents in, short summaries out — so they favor models with cheap input rates and benefit disproportionately from batch discounts. For the full scenario math on chat workloads, see how much a chatbot really costs to run; if you're weighing the API against a ChatGPT Plus seat, the breakeven is worked out in API vs subscription.

Chat applications resend the entire conversation history with every turn, so input usage grows with the square of conversation length: turn ten costs far more than turn two, even though the user typed a similar message. Long system prompts compound the same way — a 700-token system prompt is billed on every single request, which across a million requests means 700 million input tokens spent before any user text is even processed.

Verbose outputs are the other silent cost: models default to polite preambles and richly formatted JSON unless you instruct otherwise, and every one of those tokens is billed at the higher output rate. On the flip side, OpenAI discounts cached input for repeated prompt prefixes and charges roughly half price through the Batch API for jobs that can wait — leaving those unused is a real loss.

Most teams over-buy intelligence. A practical pattern is model routing: send easy, high-volume requests to a small model and escalate only ambiguous cases to a larger one. Combine that with three habits — trim the system prompt to what the model actually needs, cap or summarize conversation history after a few turns, and set explicit limits on response length.

Before committing, measure instead of guessing: paste a representative prompt into the token calculator to see what it really costs across models, and use the GPT vs Claude comparison to check whether an equivalent Anthropic model prices better for your input/output mix.

List prices only mean something next to the alternatives, so here is the October 2026 table the calculator uses, per million tokens (input / output). OpenAI's current GPT-6 line runs three tiers on the same 1.05M-token context: GPT-6 Astra at $10.00 / $50.00, GPT-6.1 Sol at $2.00 / $10.00 and GPT-6 Luna at $0.10 / $0.50. The previous generation is still on sale and still in the calculator: GPT-5.6 Sol $4.00 / $20.00, Terra $2.00 / $12.00 and Luna $0.20 / $1.20 — so the GPT-6 Sol and Luna tiers cost half of their GPT-5.6 equivalents, and there is no GPT-6 Terra. One catch applies to every tier: prompts above 272K input tokens are billed at 2× input and 1.5× output for the whole request.

The striking part is how closely Anthropic now matches OpenAI tier for tier: Claude Fable 5.1 at $10.00 / $50.00 equals Astra, Sonnet 5.5 at $2.00 / $10.00 equals Sol and Haiku 5.5 at $0.10 / $0.50 equals Luna, with Opus 5.5 ($4.00 / $20.00) in between. Google's Gemini 4 Argon joins the $2.00 / $10.00 tier on introductory pricing until 30 November 2026 and doubles to $4.00 / $20.00 after that; Gemini 3.8 Flash costs $0.75 / $3.75. Even Llama 4 Maverick ($0.20 / $0.60) is no longer the floor — Luna undercuts it on both sides of the meter. With list prices this close, the honest comparison is cost per task on your own prompts. Our GPT vs Claude comparison does the head-to-head, the cheapest LLM API guide covers the budget end, and this Sol vs Terra comparison breaks down the previous generation's tiers.