Key numbers, November 2026

• An estimated 500T+ LLM tokens a week are processed industry-wide.
• 209.5T a week on the busiest public models alone, 36× a year ago, growing about 8% a week.
• An average request sends 11,000 to 79,000 input tokens, depending on the model.
• A store with 10,000 products and a support chatbot spends about $4,075 a month on tokens at list price.

What is a token? A chunk of text, about three quarters of an English word. LLM APIs bill per million tokens, with separate prices for input (prompt, context, history) and output (the reply and any hidden reasoning).

AI coding tools changed how products get built. Small teams now ship in a week what used to take a quarter, and most of that speed comes from coding tools on a flat monthly subscription. Building feels almost free.

Running what you built is a different story. Once the product is live, every AI feature inside it calls an LLM API, and the API bills per token. No subscription covers that. Tokens have quietly become the compute cost of modern software, and most teams notice only when the invoice arrives. Welcome to the token economy.

LLM token usage statistics, 2026

OSMEXO Intelligence tracks public usage data across the model market every day. As of 11 November 2026:

• An estimated 500T+ tokens a week, industry-wide. The busiest public models processed 209.5T tokens last week alone. Add the traffic that goes straight to OpenAI, Anthropic, Google and the cloud platforms, and we estimate the industry total at 500T+ a week.
• 36 times more than a year ago. The same tracked models handled 5.8T tokens in the same week last year.
• Still growing every week. The last 7 days used 196.8T tokens against 184.4T the week before, +6.8%. Over the last six full weeks, growth averaged about 8% a week, with one down week.
• Requests are getting bigger, not just more frequent. An average request to Claude Opus 5 sends 79,000 input tokens; Claude Sonnet 5 averages 40,000 and GPT-5.5 about 11,000.
• Reasoning adds hidden tokens. About half of all requests to frontier models run with reasoning on, which adds thinking tokens you pay for but never see.

What LLM tokens cost a real app every month

The coding subscription covers building the product. These tokens are the ones your product spends every day it runs. Take a simple e-commerce store:

• Catalog enrichment. Titles, tags and descriptions generated for each product before it goes live.
• Validation. Product details, categories and compliance text checked by an LLM before publishing.
• Pricing and sales analysis. Price suggestions, demand forecasts and weekly sales summaries.
• Support chatbot. Every turn resends the whole conversation, so a long chat costs far more than a short one.

A store with 10,000 products and 30,000 support conversations a month, on GPT-5.5 at list price ($5 per million input tokens, $30 per million output):

Nine-tenths of that bill is the chatbot, because every reply resends the conversation so far. Prompt caching trims the repeated part, but the bill still rises with every new customer and every new product. The store is just an example: banks extract data from statements, clinics summarise patient notes, marketing teams generate and analyse content. Every one of them pays per token, every day, and the bill tracks usage, not revenue.

What we learned building token-heavy AI tools

We built an ads campaign optimisation tool and a trends intelligence tool. Both were heavy token users.

Ads reporting. A campaign report has more than 20 dimensions and millions of rows. At roughly 60 tokens a row, one million rows is 60 million tokens, more than any model's context window. So we:

• cut the dimensions to the ones that matter for the question,
• aggregated the data and pre-calculated the basic metrics in code,
• sent the LLM only the summary, around 500 rows or 30,000 tokens.

That made it workable. It still cost real money: 30,000 tokens per report, for 200 campaigns a day, is about 180 million input tokens a month before any output.

Trends intelligence. We pulled data from YouTube, Meta, TikTok and Google Trends. Search alone was expensive, so we switched to a Tavily alternative on Apify that costs about 50% less than Tavily. The raw data was still huge, so we filtered it first and used the LLM only to make sense of what was left.

Every optimisation helped. None of them made the token bill go away. That is when we started looking at the price of the tokens themselves.

How to reduce LLM token usage, and where it stops

• Prompt caching discounts the repeated prefix only. User input, retrieved context and history are billed in full.
• Shorter prompts and aggregation work, as above, but each costs engineering time and has a floor.
• Smaller models are cheaper but not always good enough for the task.
• Enterprise discounts need committed spend most startups do not have.

All of these reduce how many tokens you use. None of them reduce what you pay per token.

Cut LLM API costs with an AI model exchange

So we built OSMEXO, an AI model exchange. It serves the same models from OpenAI, Anthropic, Google, DeepSeek, Qwen and others through one OpenAI-compatible API key, priced below the vendor's list price.

• Lower price per token from day one. Of 69 text models available today, 57 are at least 30% below list; the median is 42% and the deepest is 60%.
• Same model. Every route is checked against the vendor's own API before it serves traffic, and you can run that check yourself.
• One key, every model. Change the base URL, keep your OpenAI SDK, and try models side by side.
• Fast by request size. Each request goes to the provider that answers fastest for its size, so a 100,000-token prompt and a short chat turn each take the quickest route.

On the store above, GPT-5.5 is 45% below list on OSMEXO: the same $4,075 month costs about $2,240.

Where it pays off most:

• Analysis tasks that send 10,000+ input tokens per request
• Product FAQ and knowledge systems with large context
• Data classification and filtering at volume
• Review moderation and summarisation
• Document extraction and RAG pipelines
• Coding agents and long multi-step tool calls

Coming next: per-request discounts and best-fit models

• Per-request discounts. Today the discount is set per model. Next, you will be able to send a minimum and maximum discount with each request. If a provider behind the exchange can serve it at that price, the request runs; if not, it is declined, so you never pay more than you chose.
• Best model for your task. Pick a few models, run the same task through all of them, and get a score for each on accuracy, cost and efficiency. Then choose the one that fits your task, not the one with the loudest launch.

Track the token economy every day

Alongside the exchange we built OSMEXO Intelligence: daily token demand, model rankings, benchmarks and news across the LLM industry, free to explore.

In the token economy, tokens are as real a cost as cloud hosting. Teams that treat them that way, measuring, optimising and buying them well, will keep their AI features profitable as usage grows.

Originally published on the OSMEXO blog, with the live charts, an animated token-cost meter and an FAQ.