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LLM API cost is the sum of separately billed usage categories: input tokens, output tokens, and any applicable cached-input, cache-write, storage, tool, modality, or service charges. A reliable estimate measures the actual request, applies the selected model’s current rates per million tokens, and then adjusts for caching or batch processing. Input and output rates can differ sharply, and token counts vary by model, prompt history, retrieved context, and generated reasoning.
How LLM API billing works
Most text-generation APIs publish prices per million tokens. The basic estimate is:
estimated cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate) + other applicable charges
“Other applicable charges” may include cached-input reads, cache creation or writes, cache storage, tool calls, image or audio processing, search grounding, data-residency options, or a service-mode surcharge. Do not assume those categories are free; check the model’s current rate card.
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Prices in this article were checked on October 13, 2026. Provider tables change, and some pages show different rates by model, context length, processing mode, region, account terms, or effective date. Confirm the applicable row immediately before forecasting spend:
The amount you pay is therefore a workload calculation, not a single universal “price per prompt.”
Input tokens: what you send is billable
What counts as input
Input tokens are the material the model processes. They commonly include the system or developer instruction, the user’s message, conversation history, retrieved documents, tool results inserted into the context, structured JSON, and formatting characters. An application can send far more input than the visible message suggests because it resends history and instructions on every turn.
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Why input totals change
- Longer conversation history is appended on each turn.
- Retrieval returns more passages than the task needs.
- System prompts gain examples, rules, schemas, or tool descriptions.
- Tool results, function arguments, and prior outputs are fed back into the next request.
- Different models tokenize identical text differently.
Measure the request payload and the provider’s usage fields rather than converting a word count with a fixed multiplier.
Output tokens: generated text can cost more
Output tokens are generated by the model. Rate cards often price them higher than input tokens because generation consumes compute step by step. A response’s visible text may not represent all generated usage: some models expose reasoning or “thinking” tokens, and structured output can contain many syntax tokens even when the prose looks short.
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Set an output-token limit appropriate to the task, but do not use an artificially low limit that causes truncation and expensive retries. Log requested limits, actual output tokens, stop reasons, and retries so that a failed generation is not mistaken for one normal response.
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Tokens are not words: estimate with the real tokenizer
A token can represent a whole short word, part of a longer word, punctuation, whitespace, or a fragment of a non-English word. Code, tables, JSON, and languages with different writing systems can have very different token-to-character ratios. Two models can split the same prompt into different token counts.
Use a representative sample and the tokenizer or usage endpoint for the exact model. Include production system instructions, typical history, retrieved text, tool schemas, and the expected output format. Measure several short, medium, and long requests; one tiny prompt is not a dependable forecast.
- Capture the exact request sent to the API, excluding secrets and personal data.
- Record input and output token counts returned by the provider.
- Group measurements by model, endpoint, task type, and context-length band.
- Calculate average, high-percentile, and worst-case usage rather than only a mean.
- Multiply each category by the rate effective on the date you will be charged.
- Recheck after prompt, model, retrieval, or response-format changes.
Reasoning usage, where exposed, belongs in the provider’s stated billing category. If a provider includes reasoning tokens in output pricing, include them in output estimates even when they are not displayed to the end user.
Prompt caching: discounted reads are only one part of the price
Cached input reads
Prompt or context caching stores reusable content so a later request can refer to it without paying the normal uncached input rate for every token. A stable prefix might contain system instructions, a long policy manual, or a fixed set of examples. When a request qualifies for a cache hit, the provider may charge a lower cached-input rate.
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There is no universal “cache discount.” A rate card can distinguish cache reads from normal input, and the qualifying prefix, minimum size, expiration behavior, and supported models differ.
Cache creation and storage
Creating a cache can have its own write or creation charge. Some services also charge for storing cached tokens over time. Google’s pricing documentation, for example, lists storage per million tokens per hour for applicable models. A cache that is rarely reused can cost more than sending the same text normally.
Anthropic states: “A request’s prompt length counts all of its input tokens, including cache reads and cache writes.” That matters when a model has a context-length threshold: a request can cross the threshold because cached and newly written portions are both part of the prompt. See the provider’s details at Anthropic pricing documentation.
When caching tends to pay off
Caching is most promising when a substantial prefix is stable, reused many times, and retained long enough to amortize creation and storage. A useful break-even calculation is:
uncached cost over N requests − (cache-write cost + cached-read cost over N requests + storage cost) > 0
Use the provider’s actual rates and cache rules in that equation. Savings are not guaranteed for a short prompt, a one-off request, a rapidly changing prefix, or a workload with few cache hits.
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Cache implementation checklist
- Put stable instructions and reference material before variable user content when the API uses prefix matching.
- Keep volatile values, timestamps, user names, and per-request data out of the reusable prefix.
- Record cache-hit, cache-write, and uncached token counts separately.
- Measure the hit rate and lifetime; do not assume every request qualifies.
- Include storage charges and minimum-prefix requirements in the estimate.
- Verify that cached content is isolated correctly between users and tenants.
Batch APIs: lower eligible cost in exchange for latency
A batch endpoint accepts work for asynchronous processing. You submit a collection of requests, receive a job identifier, and retrieve results after processing. This model is suited to offline evaluation, document backfills, nightly classification, embedding or extraction queues, and other work that does not need an immediate answer.
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When batch is a good fit
- The result can arrive minutes or hours later.
- You can tolerate a job-level failure and retry individual records.
- You have a large, predictable queue that benefits from a lower processing rate.
- You are running evaluations, backfills, enrichment, or periodic reports.
When synchronous processing is better
- A user is waiting in an interactive product flow.
- The next operation depends immediately on the response.
- Fresh external data or a short-lived authorization context is required.
- Your volume is too small for batch overhead to matter.
Batch workflow
- Separate latency-sensitive requests from offline work.
- Validate each request locally, including model, token limit, tools, and metadata.
- Submit a bounded batch and persist its job identifier.
- Poll or receive completion notification according to the provider’s rules.
- Match each result to its input identifier and record usage, errors, and retries.
- Calculate the final bill using the batch rate and any non-token charges.
How to compare models and providers fairly
Compare the complete workload, not one input-price cell. The following dimensions prevent misleading conclusions:
| Comparison axis | What to measure or verify |
|---|---|
| Workload volume | Representative input and output tokens, including system prompts, history, retrieval, tools, and exposed reasoning. |
| Input/output mix | Apply each model’s own input and output rates; output-heavy tasks can reverse an input-only ranking. |
| Tokenizer and behavior | Run equivalent tasks and measure tokenization, response length, reasoning usage, retries, and truncation. |
| Cache economics | Check expected hit rate, cached-read price, write price, minimum prefix, expiry, and storage. |
| Batch fit | Confirm asynchronous suitability, model eligibility, limits, completion timing, and batch rates. |
| Context length | Look for long-prompt thresholds that change the rate for part or all of a request. |
| Extra features | Price tools, images, audio, video, search grounding, residency, and other capability-specific units. |
| Quality and latency | Test the same acceptance criteria and response-time target; the lowest list price may require more retries or post-processing. |
OpenAI presents applicable prices per one million tokens with separate input, cached-input, cache-write, and output columns. Context length and processing mode can add further distinctions; inspect the exact model row at OpenAI API pricing.
Google’s page includes explicit effective-date changes for some Gemini models, with rates shown through December 31, 2026 and different rates beginning January 1, 2027. Tie any quoted figure to its model and effective date rather than treating it as permanent.
Worked cost calculations
The following numbers are illustrative assumptions, not provider quotations. Replace them with the rates for your model and billing account.
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Example 1: ordinary synchronous request
Assume a model rate of $3 per million input tokens and $15 per million output tokens. A request uses 120,000 input tokens and produces 8,000 output tokens:
- Input: 120,000 ÷ 1,000,000 × $3 = $0.36
- Output: 8,000 ÷ 1,000,000 × $15 = $0.12
- Total token charge: $0.48, before any tools or modality charges
If the application resends 120,000 input tokens on every turn, ten turns would use 1.2 million input tokens, not 120,000.
Example 2: deciding whether to cache
Suppose 100,000 tokens are stable and 20,000 are unique per request. You expect 50 requests. Compute the uncached cost for all 120,000 tokens on each request. Then compute the cache-write charge once, cached-read charges for the 100,000-token prefix on each qualifying request, normal input charges for the 20,000-token variable portion, and any hourly storage. Caching is favorable only when the second total is lower and the cache behavior is reliable.
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Example 3: batch versus immediate processing
For 10 million input tokens and 1 million output tokens, calculate the synchronous total using the standard rates. Recalculate with the provider’s published batch rates, then add any submission, storage, tool, or failed-record costs. A nominal 50% token discount does not make batch appropriate if delayed results cause user-visible retries or duplicate work.
Why a bill is higher than expected
- History growth: trim or summarize old turns, and measure the complete payload before sending it.
- Retrieval oversupply: reduce irrelevant passages, duplicate chunks, and oversized metadata.
- Long outputs: tighten schemas and output limits while preserving enough room for valid results.
- Hidden reasoning or retries: inspect usage fields, stop reasons, timeouts, and automatic retry behavior.
- Cache misses: check prefix order, minimum size, expiration, model support, and tenant isolation.
- Context threshold: identify whether a long-prompt tier applies to the request.
- Extra modalities or tools: separate token charges from image, audio, search, or tool-service charges.
- Rate-date mismatch: verify the effective date, region, plan, and account terms used in your forecast.
A practical budget-control routine
- Define an accepted quality and latency target before choosing the cheapest rate card.
- Collect production-like samples and record input, output, cache, reasoning, tool, and retry usage.
- Build a per-task cost model with low, typical, and high-percentile token volumes.
- Apply current model rates and label the date, context tier, region, and billing assumptions.
- Route interactive work to synchronous APIs and eligible offline work to batch processing.
- Introduce caching only after measuring stable-prefix size, hit rate, write cost, and storage duration.
- Set alerts on spend, tokens per request, cache-hit rate, output length, and retry rate.
- Recalculate after changing prompts, retrieval settings, model versions, tools, or provider pricing.
The dependable answer to “How much does an LLM API cost per token?” is therefore model- and workload-specific: measure both directions of token use, include every applicable feature charge, and evaluate caching or batching with their full rules rather than a headline discount.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
