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LLM API bills are usually the sum of separately priced input tokens, cached input tokens, and output tokens, plus any tool, search, grounding, storage, or multimodal charges. Reasoning tokens are internal work that may be included in billed output even though they are absent from the visible answer. To estimate a real workload, measure every category for the same model, tier, region, context size, and cache pattern instead of multiplying a word count by one advertised rate.
This guide explains the billing categories, gives a reusable formula and worked estimate, and sets out a fair way to compare providers without mistaking a low headline rate for a low task cost.
What an LLM API token is—and why token count is not word count
A token is a unit a model processes. Depending on the model and encoding, it can be a character, a word fragment, a complete word, or punctuation. The same paragraph can therefore produce different token counts across models, languages, and tokenizers. OpenAI’s token guidance explains the relationship between text and tokens: Understanding and counting tokens.
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The basic LLM API pricing formula
When an endpoint reports the categories separately, estimate token charges with:
token charge = (input tokens ÷ 1,000,000 × input rate) + (cached input tokens ÷ 1,000,000 × cached-input rate) + (output tokens ÷ 1,000,000 × output rate)
Use the unit rates for the exact model and pricing mode. Then add items priced outside those categories, such as tool calls, web search or grounding, cache storage, and image or audio processing. The formula is a framework, not a universal contract; each endpoint’s definitions and price table control. OpenAI’s API pricing documentation describes model-specific input, cached-input, and output rates and notes that built-in tools can add charges: OpenAI API pricing. Google and Anthropic publish additional category and feature rules in their schedules: Gemini API pricing and Claude pricing.
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Worked illustration (not a provider quote)
Suppose one workload uses 100,000 uncached input tokens, 900,000 cached input tokens, and 50,000 output tokens. At hypothetical rates of $2.00 per million input tokens, $0.20 per million cached input tokens, and $10.00 per million output tokens:
| Category | Usage | Illustrative rate | Charge |
|---|---|---|---|
| Uncached input | 100,000 | $2.00 per million | $0.20 |
| Cached input | 900,000 | $0.20 per million | $0.18 |
| Output | 50,000 | $10.00 per million | $0.50 |
| Total token charge | $0.88 | ||
These rates and usage values are arithmetic examples only. Replace them with measured usage and the live rate row for your selected model before using the result for a budget.
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What each billed token category means
Input tokens
Input, also called prompt tokens, covers what you send: instructions, conversation history, retrieved documents, tool declarations, and supported multimodal content as defined by the endpoint. Re-sending a large system prompt on every turn can make input the largest part of a chat application’s bill. Count the complete request, not only the user’s latest sentence.
Output tokens
Output, called completion tokens in some Chat Completions documentation, is what the model generates. The maximum-output setting is a ceiling, not a charge for the entire ceiling; billing follows actual usage under the provider’s rules. A model that writes longer answers, calls for more retries, or emits verbose structured data can cost more even when its input rate is low.
Cached input tokens
Cached input is a previously processed portion of a prompt that the provider can reuse. It normally has a different rate from ordinary input, but eligibility, minimum lengths, retention, and accounting vary. A cache hit is not automatically free, and some systems charge for creating or storing a cache as well as reading it.
Reasoning tokens
Reasoning tokens are internal tokens used while a model works toward an answer. They may not appear in the response text, yet they can count toward output usage. OpenAI’s token guidance says reasoning tokens count as output tokens for billing; Google’s Gemini pricing states that listed output prices include thinking tokens. The practical consequence is that visible response length is an incomplete cost measure. Record reported reasoning or thinking usage when the endpoint exposes it, and otherwise budget from observed total output usage.
How prompt caching differs by provider
There is no universal “cache discount.” The cache mechanism and its accounting are provider- and model-specific.
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OpenAI automatic prompt caching
OpenAI documents automatic prompt caching for supported requests longer than 1,024 tokens. Reuse begins with the longest previously computed prefix, starting at 1,024 tokens and increasing in 128-token increments. The response usage includes a cached_tokens value when applicable. Details and supported models can change; consult OpenAI prompt caching and the current rate table before modeling savings.
Anthropic cache writes and cache hits
Anthropic’s pricing table separates 5-minute and 1-hour cache writes from cache hits or refreshes. A workload that frequently creates short-lived prompts can therefore have a different cost profile from one that reuses a stable prefix for an hour. The table also documents prompt-length and inference-geography conditions: Anthropic Claude pricing.
Google Gemini cache and feature charges
Google’s schedule lists token rates alongside separate rules for features such as storage and grounding. Do not assume that a cached context or a grounded request is covered by the ordinary input line. Check the Gemini schedule for the selected model, modality, and tier: Google Gemini API pricing.
Current price-schedule example and why dates matter
Provider prices are changeable schedule values, not permanent market statistics. On October 13, 2026, Google’s Gemini 3.1 Flash-Lite Standard schedule listed text, image, and video input at $0.25 per 1 million tokens and output at $1.50 per 1 million tokens. That figure is specific to that model and Standard tier on the date checked; the same page lists other modalities and service tiers. Recheck it before committing a budget.
| Provider documentation | What the published schedule distinguishes | How to use it |
|---|---|---|
| OpenAI | Model-specific input, cached-input, and output columns; separate tool charges where applicable | Select the exact model, service mode, and tool set; do not infer a universal OpenAI rate. |
| Google Gemini | Model, modality, tier, output-thinking inclusion, storage, and grounding rules | Use the row matching modality and tier. The dated Gemini 3.1 Flash-Lite example above is not a timeless rate. |
| Anthropic Claude | Input, output, 5-minute cache writes, 1-hour cache writes, and cache hits or refreshes, with prompt-length and geography conditions | Model cache creation and reuse separately; verify USD rates and regional conditions. |
How to compare two providers fairly
A useful comparison holds the workload constant and measures completed tasks, not just a price-per-million headline.
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- Choose a representative task set. Include the prompts, conversation history, retrieved context, tools, schemas, and file or image mix your application actually sends. Use enough samples to capture short and long cases.
- Pin the deployment conditions. Record model name and version, context length, service tier (such as standard, batch, flex, or priority where offered), region or inference geography, and modality. A different tier or region can change the applicable rate.
- Measure input usage. Capture total input tokens for identical requests. Do not estimate from word count, because tokenization differs by model, language, and encoding.
- Measure output and internal work. Capture generated tokens and any reported reasoning or thinking tokens. If reasoning is included in output billing, include it in the output quantity rather than counting only visible characters.
- Measure cache behavior. Track cache eligibility, cache writes, cache reads or hits, retention window, and the percentage of requests that actually reuse the prefix. Apply the provider’s separate rates to each category.
- Add non-token charges. Include tool calls, search or grounding requests, cache storage, image or audio processing, and any other line item on the endpoint’s schedule.
- Measure successful work. Compare quality, completion rate, retries, latency requirements, and the number of calls needed to finish a task. A cheaper call can produce a higher cost per successful result if it needs more attempts or longer outputs.
- Run sensitivity cases. Recalculate with low, typical, and high context lengths; different cache-hit rates; and realistic output or reasoning ranges. This exposes the assumptions that matter most to the budget.
OpenAI summarizes the central warning this way: “A lower price per million tokens does not necessarily produce a lower total cost: models can tokenize the same text differently and generate different amounts of output or reasoning.” The quotation is from the OpenAI Help Center’s Understanding and counting tokens.
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Create one row per model and workload scenario. The following columns are sufficient for a first-pass estimate:
- Model, API endpoint, version, region, and service tier.
- Requests per period and average plus high-percentile input tokens.
- Uncached input tokens and cached input tokens per request.
- Output tokens, including billed reasoning or thinking tokens where applicable.
- Input, cached-input, and output rates in the schedule’s stated currency and units.
- Cache-write, cache-storage, tool, search, grounding, image, audio, or file charges.
- Retry rate and the percentage of requests that complete successfully.
For each row, multiply usage by the matching rate, sum the token categories, add non-token charges, and multiply by request volume. Keep one column for the date the price page was checked. When a provider changes a rate or model, duplicate the old row rather than silently overwriting it; this preserves the assumptions behind a forecast.
Common budgeting mistakes and fixes
Using words as a proxy for tokens
Problem: “One thousand words” is treated as a fixed token quantity. Fix: tokenize representative prompts with the selected model or measure actual API usage. Include punctuation, repeated instructions, code, and non-English text.
Counting only the visible answer
Problem: Internal reasoning, tool instructions, and hidden context are omitted. Fix: use the endpoint’s usage report and include reasoning or thinking tokens when they are billed as output.
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Applying a cache discount to every request
Problem: A theoretical cache hit rate is used as though every request reuses the same prefix. Fix: separate writes, hits, refreshes, retention windows, and misses, then use observed reuse rates.
Comparing unlike price rows
Problem: A standard text request is compared with a batch or priority request, a different context tier, or another region. Fix: match model, modality, tier, context condition, geography, and billing unit before comparing.
Ignoring extra features
Problem: The spreadsheet contains only input and output tokens even though the application uses search, grounding, tools, storage, images, or audio. Fix: copy every applicable line item from the endpoint’s current schedule and model it separately.
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Treating a dated rate as a promise
Problem: A price checked once is carried into a long-term forecast. Fix: retain the check date, set a review cadence, and revalidate model availability and terms before launch or renewal.
What “How much do LLM API tokens cost?” really means
There is no single token price. The amount depends on whether the token is uncached input, cached input, output, or billed reasoning; the model and tokenizer; context size; cache behavior; service tier; geography; modality; and extra features. The only defensible answer for a production system is a workload estimate built from measured usage and the exact current schedule row.
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