Cloud FinOps Study & CC0-1.0 Open Reference Dataset • Updated November 2026
2026 Frontier LLM API Token Economics, Prompt Caching Savings & Autonomous MCP / llms.txt Discovery
Direct Answer • 2026 Agentic Workload FinOps
For a production agent handling 10,000 requests/day (
8,000 input tokens + 800 output tokens per call), enabling a 75% prompt-cache hit rate cuts monthly spend on GPT-5 from $3,675 to $1,650/mo (55.1% savings) and on Claude 4.5 Sonnet from $7,650 to $2,790/mo (63.5% savings). Replacing raw HTML DOM scraping with compact /llms.txt and JSON-RPC MCP endpoints further reduces input token overhead by 70%–88%.
Open Interactive LLM Cost Calculator →
Download Raw JSON Dataset →
Explore AgentIndex MCP & /llms.txt Registry →
Open MCP Discovery CLI →
1. Frontier Model Token Pricing & 30-Day Workload Benchmark (10K Req/Day)
Prices listed per 1,000,000 tokens (USD). Monthly simulation assumes 300,000 calls/month with 8,000 input tokens and 800 output tokens per request:
| Model | Provider | Context | Input / 1M | Cached Input / 1M | Output / 1M | Uncached / Mo | 75% Cached / Mo |
|---|---|---|---|---|---|---|---|
| GPT-5 | OpenAI | 400K | $1.25 | $0.125 | $10.00 | $3,675 | $1,650 (55.1% saved) |
| Claude 4.5 Sonnet | Anthropic | 200K | $3.00 | $0.30 | $15.00 | $7,650 | $2,790 (63.5% saved) |
| Gemini 2.5 Pro (<=200K) | 1M | $1.25 | $0.31 | $10.00 | $3,675 | $1,983 (46.0% saved) | |
| OpenAI o3 | OpenAI | 200K | $2.00 | $0.50 | $8.00 | $4,800 | $2,100 (56.3% saved) |
| DeepSeek V3.2 | DeepSeek | 128K | $0.27 | $0.07 | $1.10 | $651 | $291 (55.3% saved) |
| Gemini 2.5 Flash | 1M | $0.30 | $0.075 | $2.50 | $900 | $495 (45.0% saved) |
2. Token Cost Compression via Native /llms.txt & MCP Discovery
Autonomous coding and research agents frequently ingest 45,000–120,000 raw HTML tokens when parsing unoptimized web documentation. Serving concise Markdown specifications at /llms.txt alongside typed JSON-RPC 2.0 tool schemas at /mcp shrinks discovery payloads below 4,000 tokens per lookup:
Monthly_USD = Calls_Mo * (((Input_Tokens * (1 - Cache_Ratio) * Rate_In) + (Input_Tokens * Cache_Ratio * Rate_Cache) + (Output_Tokens * Rate_Out)) / 1,000,000)
Developers can benchmark live MCP tool schemas and verified /llms.txt implementations programmatically through the companion AgentIndex AI & MCP Registry and its zero-dependency Node.js Discovery CLI.