It costs you around 2% session usage to say hello to claude!

1. It's off topic. 2. It's an unedicated question that could be understood why it happens with any simple google search or llm ask. Here.. let me ask my LOCAL MODEL Qwen3 Coder Next the question. No web search, no mcp, just local LLM's living in the moment: \---------------- Why does the first prompt to an LLM burn so many more tokens than the prompt contains? \---------------- Great question! When you send a prompt to a large language model (LLM), the number of tokens processed (and thus billed) is often much larger than the number of tokens in your raw input prompt. This happens due to several factors: 1. Context Window Includes History & System Prompts LLMs operate within a context window (e.g., 32k or 128k tokens). Your input prompt is only part of what gets fed into the model: Chat history (previous user/assistant turns) System instructions (e.g., “You are a helpful assistant…”) Tool/function definitions (e.g., if you’re using function calling) Retrieved context (e.g., from RAG: documents, search results) ➡️ Example: You send 100 tokens, but the full context passed to the model includes 5,000 tokens of history and retrieved docs → the LLM processes all 5,100. 2. Tokenization Overhead Some tokens represent special control tokens, such as: <s> or <BOS> (beginning-of-sequence) </s> or <EOS> (end-of-sequence) <|begin\_of\_text|>, <|end\_of\_text|>, user, assistant, system (in Llama-style tokenizers) Whitespace and punctuation can be split into multiple tokens (e.g., "don’t" → \["do", "n’t"\]). Unicode, emojis, or rare words may be broken into subword units. While usually small (a few extra tokens), this adds up in long inputs. 3. Model Internals & Activation Overhead (Less Direct, But Relevant) During inference, the model computes key-value (KV) caches for every token in the context — memory and compute grow quadratically with context length in some implementations. Though not directly counted in input token counts, the latency and resource usage scale with the full context, not just your prompt. 4. API Billing Includes Output Tokens If you’re seeing high token usage per request, remember: Total tokens = input tokens + output tokens A short prompt can generate a long response (e.g., 50 input tokens → 1,000 output tokens). Many APIs (e.g., OpenAI, Anthropic) charge per total tokens used (input + output). ➡️ Example: Prompt: “Write a poem.” (\~5 tokens) Response: \~500-token poem → 505 tokens total — and you’re billed accordingly. 5. Tool Calls & Structured Outputs If using tools (e.g., function calling, JSON mode, or structured outputs): Tools’ schemas are embedded in the prompt as text. The model may output structured data that’s tokenized differently (e.g., extra quotes, commas, braces). Some frameworks (e.g., LangChain, LlamaIndex) add extra metadata. How to Diagnose Use tokenizers directly (e.g., tiktoken for OpenAI, tokenizers from Hugging Face) to count your actual prompt tokens. Check API logs: most cloud APIs return prompt\_tokens, completion\_tokens, and total\_tokens. Log your full context — including history and system prompt — to see where tokens are going. TL;DR The “prompt” you see is just the tip of the iceberg. The model processes the entire context (history + instructions + retrieved data), plus overhead tokens — and you’re often charged for the output too. That’s why usage can far exceed your original text. Let me know if you want help counting tokens for a specific use case! 🧮