$/MTok (Cost per Million Tokens)
Standard pricing unit for AI APIs — cost in US dollars per one million processed tokens. Listed separately for input, output and sometimes cache.
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API-Nutzung
Praktische Aspekte der LLM-API-Nutzung — Streaming, Caching, Rate Limits.
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Batch API
Asynchronous API mode that collects many requests and processes them at a significant discount — results are typically delivered within 24 hours.
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Input Token
Tokens you send to an AI model in an API call — your prompt, the context, attached documents. Billed separately from output tokens and usually much cheaper.
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Keyword Glossary (STT)
A keyword glossary for Speech-to-Text is a short, curated list of project-specific terms, brand and product names handed to an STT model as a context hint before transcription — typically through Whisper's `initial_prompt`.
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Output Token
Tokens an AI model produces as its response. Billed separately and usually three to five times more expensive than input tokens because the model has to actively generate them.
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Prompt Caching
Prompt caching is an API feature in which a provider stores recurring prompt prefixes — making subsequent requests cheaper and faster because the cached portion is not reprocessed.
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Rate Limit (AI)
Provider-enforced cap on requests or tokens per time window — it protects infrastructure and ensures fair usage across customers.
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Streaming (LLM)
Streaming means transmitting an LLM's response token by token in real time — the user sees the text word by word rather than only after the full generation completes.
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Tier Pricing
Tiered model line-up from a provider — small fast variants (Mini/Flash/Haiku) at a fraction of the price of the big frontier models. Also: volume tiers with quantity discounts.
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TPM/RPM
TPM (tokens per minute) and RPM (requests per minute) are the two common units in which AI API providers express their rate limits — TPM caps the token volume, RPM caps the number of requests per minute.
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Whisper initial_prompt
The `initial_prompt` parameter lets you pass Whisper a keyword list or sample sentence before transcription. The model treats this text as context and recognises the included terms far more reliably.
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