How much does an LLM API cost? There is no useful answer in a model’s advertised token rate alone. Your bill depends on what the product sends and receives, which model completes the work, how often it runs, and whether caching, tools, media, or service tiers add charges. Estimate cost per completed task from representative usage, then multiply by realistic volume.
The eight factors below give product engineers and buyers a practical way to find the cost drivers before launch and verify them afterward. Provider prices and rules change, so check the current rate card for the exact model and features you plan to use.
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1. Model choice and workload fit
Different models can charge different rates, tokenize the same text differently, and produce different amounts of output or reasoning. A lower price per million tokens therefore does not necessarily mean a lower cost to complete a task. OpenAI advises evaluating representative tasks and total usage rather than relying on visible response length alone (OpenAI guidance on latency and task optimization).
Set a quality target first, then compare candidate models on the same representative inputs. Record whether each result is good enough for the feature, how many input and output tokens it uses, and any separately metered reasoning. Compare cost per successful task, not just the headline input rate.
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2. Input and output mix
A starting estimate for a text request is:
Request cost = input tokens × input rate + output tokens × output rate
Use the provider’s units and rates for the selected model; rate cards commonly express token pricing per million tokens. The input is often much more than the latest user message. Count system instructions, conversation history, retrieved passages, schemas, and tool results when they are sent to the model. Output length also matters, and some rate cards distinguish categories such as cached input or cache writes.
OpenAI, Anthropic, and Google publish model-specific pricing categories; consult the applicable current tables rather than treating one model’s rate as a general API price (OpenAI API pricing, Anthropic API pricing, Google Gemini API pricing).
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3. Prompt caching
Caching can lower the charge for repeated prompt content, but eligibility, cache operations, and pricing differ by provider and model. Measure how much of your traffic actually repeats in an eligible way; do not apply a cache discount to every input token in a forecast.
- OpenAI’s pricing page distinguishes cached input and cache writes for applicable models.
- Anthropic lists cache writes separately from cache hits or refreshes.
- Google lists cache input rates as well as storage charges.
OpenAI’s caching overview describes its behavior this way: “The API caches the longest prefix of a prompt that has been previously computed, starting at 1,024 tokens and increasing in 128-token increments.” This is OpenAI’s stated behavior in that overview, not a universal rule for other providers or a replacement for current model-specific documentation (OpenAI prompt caching overview).
4. Context length and pricing thresholds
Long histories and retrieved context increase input usage. They can also affect which pricing tier applies: some schedules distinguish long-context usage, while others include a large context window at standard pricing. Verify both the context limit and any price thresholds for the exact model you intend to use.
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For example, Anthropic’s current pricing documentation says Claude 4.6 and later models and Claude Mythos Preview have the full 1M-token context window at standard pricing. That statement is specific to those models and Anthropic’s pricing terms; it does not establish the same treatment for other models or providers (Anthropic API pricing).
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Tool-enabled features can cost more in two ways. Tool descriptions and schemas, plus returned results, may add tokens to model input. Server-side tools or grounding services can also have separate usage charges. Include both in the estimate.
- Anthropic’s pricing documentation itemizes model token usage, including the
toolsparameter, and additional server-side tool charges. - Google lists separate Google Search and Maps grounding charges for applicable models and tiers.
Count calls as well as tokens. An agent workflow that makes several model turns or tool calls for one user action should be costed as a multi-step task, not as a single request (Anthropic API pricing; Google Gemini API pricing).
6. Modality
A text-token estimate is not a reliable proxy for an image, audio, video, or document feature. Those inputs and outputs may use different rate categories or tokenization. Google’s pricing table separates text, image, video, and audio pricing in several model sections and states that document tokens are billed at the image-token rate. Check the modality table for the exact model and direction of traffic—input versus generated output—before estimating (Google Gemini API pricing).
7. Processing and service tier
Asynchronous batch processing may cost less where it is offered, while priority or faster service may carry a premium. OpenAI, Anthropic, and Google all show pricing differences by processing or service tier. A discounted batch rate belongs in your forecast only if delayed completion fits the feature; a priority rate belongs there only if the product needs that service. Confirm current model eligibility and the rate card before relying on either option (OpenAI API pricing; Anthropic API pricing; Google Gemini API pricing).
8. Request volume and operating pattern
Per-request cost becomes a monthly bill only after multiplying by usage. Include retries, repeated context across turns, and multi-call agent flows; estimate normal and high-usage scenarios separately. Track tokens and tool calls by feature, customer, and model, then compare the estimate with provider invoices.
Rate limits are not a per-token price, but they constrain throughput. If the expected traffic cannot be served within the applicable limits, the product may need a different architecture or service tier, which can alter costs. Check the current provider documentation and limits for the model and account you will use (OpenAI API pricing; Anthropic API pricing; Google Gemini API pricing).
How to estimate an AI app’s API costs
- Choose representative tasks. Use realistic prompts, histories, retrieved passages, tool results, and expected responses for each product feature.
- Set a quality bar and compare models. Test the same tasks with each candidate model; note whether the output meets the feature’s needs and record total usage, not just visible answer length. OpenAI recommends testing representative tasks and comparing total tokens and task cost (OpenAI guidance on latency and task optimization).
- Break down each task’s usage. Record model, input tokens by category, output tokens (including separately metered reasoning), cache writes and hits, storage duration if applicable, tool calls, modality, and processing tier.
- Apply current rates line by line. Use the selected model’s applicable input, output, cache, tool, modality, and tier rates. Add separate storage or per-call fees where relevant. Do not assume a price applies across models, providers, regions, or service tiers.
- Multiply by realistic volume. Estimate requests per user or day and monthly traffic. Model retries and multi-call flows explicitly, and calculate ordinary and high-usage cases.
- Validate after launch. Compare measured usage with estimates and invoices, then revise assumptions using actual traffic and cache hit rates.
For a fair provider comparison, hold the quality target, input/output distribution, context length, cache hit rate, tools and grounding calls, modality, latency tier, and monthly volume constant. There is no universal cheapest model without a specified workload and quality requirement.
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