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A price per million tokens is only a rate—not a forecast of your AI bill. Your actual cost depends on how much input, cached input, output, and sometimes reasoning or tool usage a task consumes, plus the pricing and contract terms that apply to your account. The useful comparison is therefore cost per successful task, measured on your own workload.
Why a token rate does not predict the bill
Providers can charge different rates for input tokens, cached input, and generated output. Some features, tools, or services may have additional charges. OpenAI’s Enterprise rate card, for example, calculates charges across input, cached-input, and output tokens and notes that other applicable feature charges and fees may be added. Its published rates apply to eligible token-based Enterprise agreements; customers should consult their agreement for discounts and commercial terms. OpenAI Enterprise pricing.
Even with the same prompt, two models may tokenize the content differently or use different amounts of output and reasoning tokens. A lower per-token rate can consequently cost more for a completed task. OpenAI’s guidance on understanding and counting tokens likewise cautions that rates alone do not establish total task cost.
There is no verified business-wide average AI token bill in the official pricing and help materials cited here. Published rates are provider-specific examples, not evidence of what a typical company spends.
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What can contribute to AI usage costs?
Input, cached input, and output
Input covers tokens sent to the model; output covers tokens generated in response. If a provider offers discounted cached input, qualifying repeated prompt content may be charged at a different rate. The applicable pricing page or agreement determines which categories are billed and how.
Reasoning, tools, and agent loops
Some tasks involve intermediate model activity that is not apparent from the final answer alone. Google states that managed-agent inference includes standard input, output, and intermediate input or reasoning tokens generated during agentic loops; tool fees apply separately under its relevant pricing rules. See Google Cloud’s generative AI pricing.
OpenAI’s API pricing also lists charges for certain tool calls or storage in addition to token prices. Tokens used by built-in tools are billed at the selected model’s rates. A workflow that calls tools repeatedly can therefore have costs beyond a single prompt-and-answer exchange. Check the provider’s current definitions and usage reporting rather than assuming all providers bill these activities alike. OpenAI API pricing.
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Context length, service, and account terms
Pricing may depend on the model, context band, service or endpoint, geography, and account arrangement. The API pricing page, for instance, includes separate long-context pricing and other billing details; its displayed short-context rate should not be assumed to cover every request. Enterprise agreement discounts, pay-as-you-go rates, committed capacity, and promotions are not interchangeable.
Published rates are examples, not a business forecast
The following OpenAI rates were checked October 4, 2026. They illustrate how much rates can vary by model and billing route; they are not a normalized comparison between providers or a prediction of any particular customer’s bill.
| OpenAI pricing example | Input per 1 million tokens | Cached input per 1 million tokens | Output per 1 million tokens | Scope |
|---|---|---|---|---|
| GPT-6 Astra, Enterprise rate card, Standard mode | $10 | $1 | $50 | Published for eligible token-based Enterprise use; agreement terms govern discounts and commercial conditions. Source. |
| GPT-6 Luna, Enterprise rate card | $0.10 | $0.01 | $0.50 | Published for eligible token-based Enterprise use; agreement terms govern discounts and commercial conditions. Source. |
| gpt-6-astra, API pricing page, listed short-context table | $10 | $1 | $50 | The same table lists cache writes at $12.50 per 1 million tokens. Long-context pricing and other billing details also apply where relevant. Source. |
Do not treat these figures as evergreen. Confirm the current model, context band, endpoint or service, and your account’s actual terms before estimating spend. The API page also describes eligible regional-processing endpoints for models released on or after March 5, 2026, as carrying a 10% uplift, and says GPT-5.6 Sol promotional pricing is available at least through November 21, 2026. Both details are conditional and time-sensitive, so verify present eligibility and terms on the pricing page.
When prompt caching may reduce repeated-input costs
When requests reuse a stable prefix—such as fixed instructions or reference material—caching may lower the cost of qualifying repeated input. OpenAI documents automatic Prompt Caching for supported API prompts longer than 1,024 tokens. It caches the longest matching prefix in 128-token increments, and cached usage is reported in the API response. OpenAI says caches are typically cleared after 5–10 minutes of inactivity and removed within one hour after last use. These are OpenAI’s documented behaviors, not guarantees for other providers or every model. See OpenAI Prompt Caching documentation.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to estimate your own workload
- Choose representative tasks. Sample ordinary requests and high-volume or unusually demanding cases from the real workflow. Record whether each task succeeds to the required standard, not just how many tokens it uses.
- Capture usage by request. Record the model, enabled features, input tokens, cached input tokens, output tokens, and any reported reasoning or tool usage. Use the provider’s current definitions and response fields; usage categories are not necessarily identical across services.
- Apply the right rates. Use the current price for each usage category and context band. Confirm whether the account uses API pay-as-you-go, an eligible Enterprise agreement, committed capacity, a promotion, or another arrangement.
- Add non-token charges. Include separately priced tool calls, storage, modalities, or other features, along with agent-loop activity where applicable. Check service tier, geography, and agreement terms.
- Test caching where it fits. If requests share stable prefixes, compare requests with and without verified cache usage. Base any savings estimate on observed cached-token counts, not an assumed hit rate.
- Compare cost per successful task. Evaluate spend alongside quality, latency, context needs, capacity, and contract predictability. A cheaper token rate is not useful if the workflow needs more tokens or fails more often.
A simple calculation for a request is: (input tokens × input rate + cached-input tokens × cached-input rate + output tokens × output rate) ÷ 1,000,000, then add any applicable non-token charges. Use the actual rates and units in the relevant provider’s pricing terms; this formula does not replace provider-specific billing rules.
Choosing between pay-as-you-go and committed capacity
Capacity commitments can change how usage is purchased and accounted for, but they are not automatically cheaper. OpenAI describes Scale Tier for Enterprise customers as pre-purchased token capacity for a specific model snapshot with a minimum 30-day term; some models use combined input/output accounting. Compare the commitment with the workload’s measured volume, required capacity, term, and contract conditions. OpenAI Scale Tier.
For scale, OpenAI’s published Scale Tier example says each GPT-4.1 input unit costs $110 per day for 30,000 input tokens per minute, and each output unit costs $36 per day for 2,500 output tokens per minute; each unit is purchased for at least 30 days. Those figures describe that specific offer, not a general benchmark or necessarily the best fit for a different model or workload.
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What to compare before choosing a model or plan
| Comparison area | What to verify |
|---|---|
| Usage mix | Input, cached input, output, reasoning, and agent-loop usage actually reported for representative tasks. |
| Cache treatment | Cached-input rates, cache-write charges if any, eligibility, and observed cache usage. |
| Task efficiency | Tokens needed to complete the same task, plus quality and success rate—not just visible response length. |
| Context and features | Applicable context band, tools, modalities, storage, and any separate fees. |
| Service and contract | Geographic or service-tier adjustments, discount eligibility, capacity, commitment term, and agreement conditions. |
| Operational fit | Latency and capacity requirements alongside cost per successful task. |
For ongoing oversight, retain usage records at the level needed to reconcile provider invoices against workload, model, and feature. Token-category visibility makes it easier to spot a shift in output volume, cache usage, or tool activity than a single monthly total does.
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