How to Estimate Your GPT-6 API Costs Before You Ship

API bills scale linearly with tokens — the formula below forecasts yours. GPT-6 pricing is simple per-token math, and you can forecast your spend before writing production code — then validate against actuals in the usage dashboard. Here's how.

Tokens 101: input vs output

Every API call bills two token streams. Input tokens are everything you send: the system prompt, conversation history, the user message, and any tool definitions. Output tokens are everything the model generates — including hidden reasoning tokens on reasoning models. Output is priced exactly 5× as much as input on every GPT-6 tier, so output length dominates your bill.

Rule of thumb: 1,000 tokens ≈ 750 English words. A typical chat turn with history runs 1,000–3,000 input tokens and 150–500 output tokens.

The one formula you need

That's it. Measure average tokens per request (from prototype logs or a pilot), multiply by expected volume, apply the per-1M rates for your tier. This starter formula omits two refinements the calculator applies: the cached-input split (cached tokens bill at 0.1×) and the Flex/Batch 0.5× tier multiplier.

Worked example: support chatbot

Assumptions: 100,000 requests/month on GPT-6 Sol ($2.00 input / $10.00 output per 1M), averaging 2,000 input tokens and 300 output tokens per request.

• Input: 100,000 × 2,000 = 200M tokens → 200 × $2.00 = $400
• Output: 100,000 × 300 = 30M tokens → 30 × $10.00 = $300
• Total $700/month

Notice output is 43% of the bill despite being 13% of the tokens. Now you see why output tokens cost 5× input tokens on every GPT-6 tier, so output length is usually the largest line item to reduce — see our 10 cost-saving tips.

Hidden costs most teams forget

• Reasoning tokens. On reasoning-heavy tiers, the model's internal chain-of-thought counts as output tokens. Complex queries can generate 5–10× more reasoning tokens than visible answer tokens.
• Retries and fallbacks. Every retry is a full re-bill. At 99% reliability and 100k requests, that's 1,000 extra calls — budget 1–3% overhead.
• Conversation history growth. Multi-turn chats resend history each turn. Model the average turn, not the first turn — it's usually 2–3× larger.
• Dev and eval traffic. Testing, evals, and staging environments often add 10–20% on top of production. Consider routing dev and eval traffic to the cheapest tier to keep that 10–20% overhead cheap.

Add a 20–30% buffer on top of your formula result. You'll land close to reality.

From spreadsheet to live tracking

Do the formula once for your launch forecast, then track actuals weekly in the OpenAI usage dashboard. If actuals drift more than 30% above forecast, something changed — usually prompt bloat or a missing max_tokens cap. Our free calculator runs the formula for you: enter requests, tokens, and tier, and it breaks down input vs output cost instantly.