While building with LLMs, engineers should neither spend time counting tokens nor fret about cost estimation.
Unstract’s Token Calculator tool lets you calculate and compare costs for OpenAI, LLaMA, Claude, Gemini and other popular models.
Many LLM APIs charge based on the number of tokens processed. Calculating tokens helps manage and predict costs, especially for large-scale or commercial applications.
Most LLMs have a maximum context length measured in tokens. Some models might have a limit of 2048 or 4096 tokens. Knowing the token count helps ensure that inputs do not exceed these limits, as the model may not process inputs beyond this length effectively.
Understanding token counts can help optimize the performance of LLMs. Token-heavy requests can increase latency (response time) and computational load. Minimizing token usage improves processing speed and user experience.
Crafting effective prompts often involves managing token counts to include all necessary information without exceeding model limits. This is particularly important for complex tasks that require detailed instructions.
In research and development, token counts are used to evaluate model performance and compare different models. Standardized token counts help ensure that evaluations are fair and comparable.
Consistent token counts can help maintain consistency in the model's responses. If an LLM is expected to generate structured outputs — like summaries or JSON objects — the response might be incomplete if it exceeds token limits.
At Unstract, we focus on leveraging LLMs to automate document extraction. Effectively managing tokens is crucial for controlling costs, performance, and output quality—particularly in complex document extraction workflows. These processes often involve large inputs, requiring predictable and cost-efficient results.
Unstract is the leading open source IDP 2.0 platform that fully leverages LLMs for structured document data extraction from unstructured documents.
LLMWhisperer is a document-to-text converter. Prep data from complex documents for use in Large Language Models.
Get started in minutes, not months. It’s that easy.
Intelligent Document Processing 2.0 (IDP 2.0) Platform Powered by Large Language Models.
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