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Estimate an LLM inference bill by measuring representative requests, pricing input and output separately, adding any cache or feature charges, and scaling the result to an explicit traffic forecast. A token count is a planning estimate—not an invoice. Reconcile it against the usage returned by the provider and its current rate card before relying on the forecast.
What determines an LLM inference bill?
For a simple text-only request, estimate input and output as separate quantities because they often have different rates:
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request_cost = (input_tokens / 1_000_000 * input_rate) + (output_tokens / 1_000_000 * output_rate)This is only the starting point. Depending on the model and API, cached input, cache writes, context-length tiers, images, audio, tools, regions, or service tiers may have separate rates or billing units. Check the exact model and service context on the provider’s current OpenAI pricing page or Anthropic pricing documentation; prices can change.
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How do you build a representative estimate?
1. Define the unit of work
Choose an actual application action, such as answering one support question or summarizing one document. Record what the API call will really contain, not just the user’s message:
- System and developer instructions, user content, and retrieved context.
- Tool definitions and the expected number of tool calls.
- Images, audio, files, or other modalities sent with the request.
- The output target or limit, plus any relevant conversation history.
A plain-text string count may miss parts of this request. Google’s Gemini token guide and OpenAI’s token documentation describe provider-specific ways to count their API inputs.
2. Count input with the selected provider and model
Use the counter supported for the API and model you plan to ship. OpenAI points to tiktoken for plain-text tokenization and documents a token-counting API for full Responses inputs, including message roles and boundaries, images, files, tools, and conversations. Google’s Python client offers client.models.count_tokens(model=..., contents=...) for Gemini. These are provider-specific approaches; one model’s tokenizer is not a universal counter for other providers.
For example, this Gemini pattern counts the contents before sending a request:
from google import genai
client = genai.Client()
response = client.models.count_tokens(
model="YOUR_GEMINI_MODEL",
contents="Text representative of your request",
)
print(response.total_tokens)Use the current Gemini token guide for the supported package setup and request shapes. For OpenAI, follow the current token-counting guide; do not assume a plain-text tiktoken count includes every structured or multimodal part of a request.
3. Estimate output separately
Use outputs from representative examples or a realistic target length. A maximum output setting is a ceiling, not a prediction of what each request will generate. Some models also use reasoning tokens that may count as output usage even when they are not displayed as answer text. Google exposes thinking-token usage in its usage fields; OpenAI documents that usage can include reasoning and structural tokens that are not visible in the returned text.
As a rough English-language intuition only, OpenAI gives estimates of about four characters or three-quarters of a word per token. This is not a reliable conversion for every language, model, or request structure. The eventual count should come from the selected provider’s counter and returned usage.
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4. Apply the correct rate categories
For every model in the estimate, capture its provider, exact model, applicable geography or service tier, rate-card date, and assumptions. Use the correct input and output rates, then add separately billed categories that apply to your request. OpenAI’s pricing table separates input, cached input, cache writes, and output, and includes context tiers and additional feature pricing. Anthropic’s pricing documentation distinguishes cache writes from cache hits.
For a request mix, estimate each class independently rather than blending unlike requests:
period_cost = sum(requests_in_class * estimated_cost_per_request for each request_class)The estimate should use published list prices or your actual negotiated rates, clearly identified. Do not assume a single input rate covers every context length, modality, tool, cache state, or service tier.
How should you account for prompt caching?
Model caching separately from ordinary input. Count a prompt as cached only when the provider’s feature and the actual request conditions allow it. Repeated meaning is not enough by itself: OpenAI describes prompt caching in terms of reuse of a matching rendered prefix. Its prompt caching guide recommends tracking cached-token and cache-write usage alongside latency and realized cost.
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Make the cache assumption explicit in the forecast—for example, no cache, or a hit fraction based on observed requests. Apply the provider’s cache-read and cache-write rates to the eligible tokens, not to the entire prompt by default. Anthropic also lists cache writes and cache reads as separate pricing categories.
How do you turn request cost into a launch forecast?
Multiply the estimated unit cost by expected volume, then add the request classes that actually occur in the product:
estimated_period_cost = requests_per_period * estimated_cost_per_representative_requestInclude user journeys, retries, tool-driven follow-up calls, and background jobs where relevant. Build low, base, and high scenarios by varying traffic, input size, output length, and cache-hit assumptions. These are your planning cases, not provider-published usage statistics.
Keep the forecast’s scope clear. API inference charges are not the same as a full deployment budget, which may also include infrastructure, observability, staffing, taxes, or other expenses. Do not present one model, traffic level, or request size as universally representative.
How do you validate the forecast in Python?
Run representative requests, count inputs before sending where the provider supports it, and inspect usage from the returned response. For Gemini, the token guide describes usage fields for input, output, thinking, cached content, tool use, and total tokens. Read the actual field names for the API version and response type you use; providers do not expose identical usage objects.
# After a representative request, inspect the provider response's usage object.
# Use the field names documented for your specific API and response type.
print(response.usage_metadata)Compare the forecasted input and output with returned usage, including any reasoning, cache, modality, or tool categories. After launch, aggregate realized usage and cost by model, endpoint, feature, user journey, and time period. Recalculate when the model, prompt, retrieval strategy, tools, traffic mix, cache behavior, or price changes. OpenAI recommends evaluating representative tasks when comparing models and inspecting actual usage; Google’s API returns detailed token usage information.
How can you compare models fairly?
Compare the same representative task and request mix on each candidate, then use measured token usage and the appropriate current rates to estimate cost per task and per period. A lower input price per million tokens does not guarantee a lower total bill: models may tokenize the same text differently and produce different output or reasoning usage.
- Input and output token counts for the same task.
- Model-specific tokenization and reasoning/output behavior.
- Cached-input and cache-write rates under the prompt conditions you expect.
- Context-length tier, modality, tool charges, and any regional or service-tier adjustments.
- Projected cost per task and period, using published list prices or clearly identified negotiated rates.
Recheck each provider’s official pricing immediately before shipping, then reconcile the estimate with representative returned usage. The estimate is useful when its workload assumptions are explicit and its actuals are measured; it is not a substitute for either.
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