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The short answer: an LLM API bill is usually the number of input tokens multiplied by the model’s input rate, plus output tokens multiplied by its output rate, with separate charges or discounts for cached context, batch processing, tools, and other features. Prices are normally listed per million tokens, and the same prompt can cost different amounts on different models because tokenizers, output length, reasoning, and pricing rules differ.
This guide shows how to estimate a real workload, understand cache and batch economics, and compare providers without being misled by a single list-price number. Provider pages change frequently; the examples and policies below were checked on October 13, 2026, so verify the linked rate cards before committing budget.
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What an LLM API actually charges for
An API request contains more than the text a user types into a chat box. The billable input can include system instructions, conversation history, retrieved documents, tool results, images or other modalities, and the current user message. The model’s generated response is billed as output. Some services also expose reasoning or “thinking” tokens, which can be included in output usage even when they are not shown in the final answer.
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Most text rate cards use a price per 1,000,000 tokens. A practical estimate is:
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estimated cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate) + other applicable charges
“Other applicable charges” should not be assumed to be zero. Depending on the model and endpoint, a request can incur cache-write or cache-creation fees, cached-input fees, cache storage, tool-use charges, image/audio/video units, search grounding, data-residency modifiers, or a different rate for a long context. OpenAI’s pricing page presents separate input, cached-input, cache-write and output columns for applicable models, while context length and processing mode can create additional distinctions: OpenAI API pricing.
Input tokens: the largest hidden variable
Input tokens are the pieces of text or other content the model processes before generating an answer. They include every message sent in the request, not just the latest question. A support bot that resends a 20-page policy, a system prompt and a long conversation on every turn can consume far more input tokens than its visible reply suggests.
Why word count is an unreliable proxy
Tokenization depends on the model. Common words may be represented by one token, while unusual words, code, URLs, punctuation, and non-English text can split into several. Two providers can tokenize identical content differently. OpenAI’s token guidance warns: “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.” Read the underlying explanation at Understanding and counting tokens.
For budgeting, measure representative requests with the tokenizer or usage fields for the exact model you plan to run. Include system messages, retrieved passages, prior turns and tool results. Do not multiply a short user-message estimate by monthly requests and call it a forecast unless those other components are measured.
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Long context can change the rate
Some rate cards apply a different price when a request crosses a context-length threshold. Check whether the higher rate applies to all tokens or only the portion beyond the threshold. The applicable model page, not a generic provider headline, is the authority.
Output tokens and reasoning usage
Output tokens are generated tokens. They usually have their own rate and may cost substantially more than input tokens. A request that supplies a small prompt but asks for a long report can therefore be output-heavy.
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Visible text is not always the full generation. Models that perform internal reasoning may count those tokens in usage; some pricing tables explicitly place reasoning or “thinking” tokens in the output category. A short final answer does not prove that the generation was inexpensive. Capture the API’s input, output and reasoning-usage fields, when exposed, and set a maximum output limit appropriate to the task.
A simple measured estimate
Suppose a measured workload averages 12,000 input tokens and 2,000 output tokens per request. At 100,000 requests, the monthly volume is 1.2 billion input tokens and 200 million output tokens. Apply the chosen model’s current per-million rates independently:
- Input component:
1,200 × input rate. - Output component:
200 × output rate. - Add cache writes, cached reads, storage, tools, modalities or other listed charges.
This is an accounting framework, not a universal price estimate. Substitute the exact rate-card values, model, region and billing terms you will use.
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Prompt caching: discounted reads are not free
Prompt or context caching stores a reusable portion of a request so subsequent calls can process it at a different rate. It is most useful when a stable, substantial prefix—such as policy text, a codebase snapshot or a long system instruction—is reused across many calls.
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Do not describe caching as one universal discount. Provider tables can distinguish:
| Charge | What it represents | Question to answer |
|---|---|---|
| Cache read | Processing tokens that matched an existing cached prefix | What is the per-million cached-input rate, and what content qualifies? |
| Cache write or creation | Preparing new content for reuse | Is creation charged once per cache, per request, or under a minimum size? |
| Storage | Keeping cached tokens available over time | Is storage metered, and is it priced per million tokens per hour? |
OpenAI lists separate cached-input and cache-write columns for applicable models: official pricing. Google’s Gemini pricing page lists storage charged per million tokens per hour for applicable models: Gemini Developer API pricing. Anthropic notes that a request’s prompt length includes all input tokens, including cache reads and cache writes, which matters if a context-length threshold is involved: Claude Platform pricing.
When caching breaks even
Estimate caching with the same traffic assumptions as ordinary billing:
- Measure the stable prefix size and the number of expected reuses during its cache lifetime.
- Apply the provider’s cache-creation rate to the write volume.
- Apply the cached-read rate to every token actually served from the cache.
- Add storage for the time the cache remains resident.
- Compare that total with sending the same prefix as ordinary input on every request.
A cache can lose money when the prefix is small, reused only once or twice, expires quickly, or carries a high creation or storage charge. Conversely, a large prefix reused many times can make the discounted read rate valuable. There is no cross-provider cache lifetime or break-even percentage that can be safely assumed; use the model’s current rules.
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Batch APIs: lower cost in exchange for latency
A batch endpoint accepts a collection of requests for asynchronous processing instead of returning each answer immediately. That delay is the trade-off for lower eligible processing cost. Batch is a strong fit for offline evaluation, document backfills, nightly classification, embeddings or other jobs where minutes or hours of queue time are acceptable.
Published discounts checked in October 2026
| Provider documentation | Published batch treatment | Qualification |
|---|---|---|
| Anthropic Batch API | 50% discount on input and output token prices | Verify model support, limits, timing and current terms on the pricing page. |
| Google Gemini API Batch API | 50% cost reduction advertised | Eligibility, model, region and request limits can vary; check the current page. |
Anthropic’s details are in its pricing documentation; Google’s are in Gemini pricing documentation. These discounts are provider-specific examples, not a promise that every model, account or geography receives the same reduction. A batch job can also have size limits, completion windows, cancellation rules and separate failure handling.
How to decide whether batch fits
- Use synchronous calls when a user is waiting, an agent must take the next action, or a strict response-time objective applies.
- Use batch when work can queue, results can be retried or reordered, and a lower token rate is worth delayed completion.
- Use a hybrid when interactive traffic is urgent but backfills, analytics and evaluation can run asynchronously.
Recalculate with the actual batch rate rather than simply halving a synchronous invoice. Tool calls, modalities, long-context surcharges and storage may remain separately billable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare models and providers fairly
A single “price per million tokens” cell is not a total-cost comparison. Build a workload sheet with the following fields:
| Axis | What to measure or verify |
|---|---|
| Workload volume | Representative input and output tokens, including system prompts, history, retrieval and exposed reasoning. |
| Input/output mix | Apply each model’s own input and output rates; an input-only comparison misleads output-heavy jobs. |
| Tokenizer and generation | Run equivalent tasks and record token counts, output length and reasoning usage. |
| Cache behavior | Reuse frequency, eligible prefix, cache-read and write rates, expiry and storage. |
| Batch fit | Asynchronous tolerance, model eligibility, queue limits, completion window and batch rates. |
| Context length | Thresholds that change the rate for a long prompt or part of a request. |
| Extra features | Tools, images, audio, video, search grounding, residency and other units or modifiers. |
| Quality and latency | Whether the model meets accuracy and response-time requirements on the same task set. |
The cheapest rate card can produce the highest acceptable-result cost if it needs more output, retries, tool calls or human correction. Compare measured end-to-end workload cost at the quality and latency you actually require.
A repeatable monthly cost-estimation process
- Define the workload. Separate interactive, batch-eligible and cacheable traffic. Record requests per day, peak concurrency and retention requirements.
- Measure tokens. Sample real requests for input, output and reasoning. Include every automatically appended message and retrieved document.
- Select the exact model and date. Record the model name, provider, region, context tier and the date the rate card was checked.
- Split billable categories. Put ordinary input, cached reads, cache writes, storage, output and feature charges in separate spreadsheet columns.
- Run scenarios. Calculate no-cache, expected cache-hit, low-cache-hit, synchronous and batch cases. Vary output length because it often dominates the total.
- Add operational headroom. Include retries, failed tool calls, traffic spikes and a rate-change review. Do not hide negotiated, committed-use or reseller terms inside a public list-price estimate.
- Validate against invoices. After launch, compare usage records with the provider bill and investigate any category that differs from the forecast.
Important date and pricing qualifications
Provider pricing is a moving target. The figures and discounts cited here were visible on October 13, 2026, not a permanent tariff or an independent benchmark. Google’s pricing page displays model rates with explicit effective-date changes, including rates shown through December 31, 2026 and a different rate beginning January 1, 2027. Tie every quoted number to the model and date on the live page.
The linked pages cover selected first-party APIs. They do not establish reseller prices, negotiated enterprise contracts, committed-use discounts, every geography, taxes or every available model. Check account-specific terms before making a purchase decision.
Common billing mistakes and fixes
Budgeting from the visible prompt
Symptom: the invoice is several times higher than a word-count estimate. Fix: log the serialized request, including system text, history, retrieval and tool output, then measure tokens for the selected model.
Using input price to forecast an output-heavy job
Symptom: a report generator costs more than expected despite short instructions. Fix: model output tokens separately, cap unnecessary verbosity and include reasoning usage when the API reports it.
Calling every cache hit a saving
Symptom: caching adds fees without reducing the bill. Fix: include creation, read and storage charges, then test the actual reuse count and expiry behavior.
Assuming batch is an instant 50% discount
Symptom: a job is ineligible or misses a delivery deadline. Fix: confirm model support, limits, queue timing and the provider’s current batch rate before moving production traffic.
Ignoring non-token charges
Symptom: token arithmetic looks correct but the invoice is higher. Fix: audit tools, images, audio, video, search grounding, storage, long-context tiers and regional or service modifiers.
Bottom line: estimate the workload, not the headline rate
LLM API pricing becomes predictable when you measure the request the provider actually receives. Keep input, output, cached reads, cache writes, storage, batch eligibility and extra features as separate line items. Then compare equivalent tasks for tokenization, reasoning, quality and latency. Caching can reward stable, heavily reused context; batching can reduce eligible asynchronous processing costs; neither is automatically cheaper. Recheck the official rate cards at deployment and whenever the model, context length or traffic pattern changes.
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