🧠 200k / 128k / 2M Limits • Lost in the Middle Guard • Prompt Caching Savings
LLM Context Window & Token Budget Visualizer
Packing too much context into an LLM degrades recall and spikes API costs. Allocate your token budget across system prompts, RAG documents, and conversation history safely.
⚙️ Model & Token Allocations
🟢 System Prompt & Tool Schemas: 2,500 tokens
Core system rules, role directives, and OpenAPI function definitions.
🔵 Retrieved RAG Documents: 35,000 tokens
Top-k text chunks, PDFs, knowledge base articles retrieved from Vector DB.
🟣 Chat History & Prior Turns: 12,000 tokens
Previous user and assistant messages in multi-turn threads.
🟡 Reserved Max Output Tokens: 4,096 tokens
Max tokens allocated for LLM response generation.
📊 Visual Capacity Utilization
Optimal (26.8%) System RAG Docs History Output Headroom
Total Tokens Used
53,596
Remaining Headroom
146,404
Est. API Call Cost
$0.160
✅ Retrieval Recall Status: Excellent (>99.2%)
Context utilization is under 60%. The model has ample attention heads available to search needle-in-a-haystack references without positional degradation.
💰 Prompt Caching Discount Potential
Caching System + RAG docs saves up to 90% on subsequent queries
-85.4%