Eight hundred million? No — one-point-three billion now.

The stack grew wider; the headline had to bow.

README.md

@@ -2,7 +2,7 @@
 
 # FreeLLMAPI
 
-**One OpenAI-compatible endpoint. Fourteen free LLM providers. ~800M+ tokens per month.**
+**One OpenAI-compatible endpoint. Fourteen free LLM providers. ~1.3B+ tokens per month.**
 
 Aggregate the free tiers from Google, Groq, Cerebras, SambaNova, NVIDIA, Mistral, OpenRouter, GitHub Models, Hugging Face, Cohere, Cloudflare, Zhipu, Moonshot, and MiniMax behind a single `/v1/chat/completions` endpoint. Keys are stored encrypted. A router picks the best available model for each request, falls over to the next provider when one is rate-limited, and tracks per-key usage so you stay under every free-tier cap.
 
@@ -33,7 +33,7 @@ Aggregate the free tiers from Google, Groq, Cerebras, SambaNova, NVIDIA, Mistral
 
 ## Why this exists
 
-Every serious AI lab now offers a free tier — a few million tokens a month, a few thousand requests a day. On its own each tier is a toy. Stacked together, they add up to roughly **800 million tokens per month** of working inference capacity, across dozens of models from small-and-fast to reasonably capable.
+Every serious AI lab now offers a free tier — a few million tokens a month, a few thousand requests a day. On its own each tier is a toy. Stacked together, they add up to roughly **1.3 billion tokens per month** of working inference capacity, across dozens of models from small-and-fast to reasonably capable.
 
 The problem is that stacking them by hand is painful: fourteen different SDKs, fourteen different rate limits, fourteen places a request can fail. FreeLLMAPI collapses that into one OpenAI-compatible endpoint. Point any OpenAI client library at your local server, and it routes transparently across whichever providers you've added keys for.