How to Use LLM API Cost Calculator
The LLM API Cost Calculator helps teams estimate the real operating cost of a language model feature before traffic arrives. Enter the number of API calls you expect per day, the average input tokens per request, and the average output tokens per request. The result table updates immediately and compares Gemini 3.5 Flash, Gemini 3.1 Pro, Gemini 2.5 Flash-Lite, Claude Opus 4.7, Claude Sonnet 4.6, Claude Haiku 4.5, GPT-5, GPT-5 mini, DeepSeek V3, and other popular models.
Use the scenario buttons when you want a quick starting point. A support bot usually has many short requests, code completion has a very high call count but shorter outputs, RAG applications have larger prompts because retrieved passages are included, agent workflows often carry tool history, and long document summary jobs can create very large input prompts. After choosing a preset, adjust the numbers to match your product telemetry or forecast.
The currency selector changes every money value in the result area, including the headline, daily cost, monthly cost, and full comparison table. The Batch API toggle applies a 50% discount for work that can run asynchronously. The context caching toggle adds a cache-hit slider, which is useful when your requests reuse the same system prompt, tool schema, or stable context block. Your last inputs are saved locally in the browser so the calculator opens with the same setup next time.
Formula & Theory - LLM API Cost Calculator
The LLM API Cost Calculator uses a simple token pricing model. Prices are stored as static model data in the page and expressed per one million tokens.
Single request cost =
(input tokens / 1,000,000) × input price
+ (output tokens / 1,000,000) × output price
Monthly cost =
single request cost × daily API calls × 30When Batch API is enabled, the calculator applies:
Batch-adjusted cost = normal cost × 0.5When context caching is enabled, the cache-hit portion of input tokens is priced at 10% of normal input price:
Effective input tokens =
uncached input tokens + cached input tokens × 0.1Gemini 3.1 Pro includes a long-context tier rule in the calculation. If the average input token count is above 200,000 tokens, the calculator doubles the input and output price for that model. This makes large prompt workloads easier to reason about because the model that looks cheap for short requests may become more expensive when every request contains a very long context.
The savings percentage compares each row with the cheapest model under the current settings. If one model costs 100 dollars per month and the cheapest costs 60 dollars, moving to the cheapest model saves 40% of that model’s spend. This is a practical way to scan the table: the cheapest model is obvious, but the savings column tells you how costly each upgrade is.
Use Cases for LLM API Cost Calculator
The LLM API Cost Calculator is useful during product planning, vendor comparison, and model migration. A product manager can estimate whether a new AI feature fits a monthly budget. An engineer can compare Gemini API pricing calculator scenarios against Claude and GPT options before selecting a default model. A finance team can translate token forecasts into daily and monthly spend without reading every provider’s pricing page.
For customer service bots, the calculator shows how much high request volume matters even when each request is short. For RAG systems, it reveals the impact of retrieved context and makes context caching easier to justify. For agent workflows, it helps estimate the cost of repeated tool calls and accumulated conversation history. For long document summary, it highlights why large-window models are powerful but not automatically cheap.
The calculator is also helpful for SEO and market research terms such as gemini 3.5 flash pricing, gemini api pricing calculator, gemini vs claude pricing, and cheapest llm api 2026. Instead of presenting a static table, it lets readers model their own workload and immediately see how traffic, token size, discounts, and currency affect the result.