How to stop overpaying for LLM API tokens

A practical guide to what frontier model tokens actually cost, and where the cheap supply hides. Updated from live 2026 market data.

Why the sticker price is not the market price

The big model providers publish per-token list prices (GPT-5.x family runs roughly $2.00–$10.00 per million at retail). That price pays for brand, sales motion, and margin on a very thin layer. The same frontier models are also available through leaner resale pools that buy capacity at volume — and resell it at a fraction of list.

We run one of those pools. Same model family, OpenAI/Anthropic/Responses-compatible endpoints, priced at the numbers below.

What you should actually pay (USD per 1M tokens)

Model	Our input	Our output	Retail ref	You save
GPT-5.6	$0.80	$4.80	$2.00 / $10.00	~60%
GPT-5.6 Sol	$0.80	$4.80	$2.00 / $10.00	~60%
GPT-5.6 Terra	$0.32	$1.92	$0.80 / $4.00	~60%
GPT-5.5	$0.80	$4.80	$2.00 / $10.00	~60%
GPT-5.4	$0.40	$2.40	$1.00 / $6.00	~60%
GPT-5.4 Mini	$0.12	$0.72	$0.30 / $1.80	~60%

Three rules for cheaper inference

Don't buy retail for batch or agent workloads. Anything automated with predictable volume should never run at list price. Reseller pools are the same weights, cheaper pipe.
Buy credits, not subscriptions. You only pay for tokens you actually consume. No unused-allowance burn.
Keep one key for every harness. If your tooling speaks OpenAI or Anthropic, one endpoint and one key should cover Claude Code, Codex, SDKs, and agents alike.

Is a resale pool safe?

Check three things: real upstream verification (we probe model identity on every provider we resell), a stable API contract, and payment rails that clear instantly. We publish our model list and our agent-facing llms.txt so machines can audit us too.

Try it now — buy $5 of credits, no signup.