DETERMINISTIC COST ENGINEERING
FOR_LLM_APP_UNITS
Token-exact pricing across GPT-5.4, Claude 4.6, Gemini 3.1, and DeepSeek. No estimates. No guessing. Just math.
↳ Adjusts how tokens split between input (your prompt) and output (model response). Chatbots = high input. Code generation = high output.
| Model | Provider | Input cost | Output cost | Cost/req | Monthly | Flags |
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FAQ
How accurate is the LLM token calculator?
For OpenAI models (GPT-5, GPT-5 Mini, GPT-4.1 Nano), we use a tiktoken-style 4-characters-per-token count so GPT figures stay aligned with OpenAI billing. For Claude and Gemini models, we use Google’s recommended 4-characters-per-token approximation, clearly labeled as ~estimated.
What is prompt caching and how much does it save?
Prompt caching allows you to reuse repeated input tokens across API calls. When enabled, it saves 90% on input token costs (you pay only 10% of the normal input price). This is especially valuable for RAG pipelines and chatbots with large system prompts.
How much does the Batch API save?
The Batch API processes requests asynchronously and saves 50% on both input and output costs. It’s ideal for non-real-time workloads like data processing, content generation, and evaluation pipelines.
What are reasoning tokens?
Models like Claude Opus 4.6 (in extended thinking mode) use additional thinking tokens before generating their response. These reasoning tokens are billed at the output token rate and can be 2–4× the output token count, significantly increasing costs.