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The inference margin era ended quietly. cloud llm vendors, per reports from september 21, cut…

Yethikrishna ROriginal on Threads

the inference margin era ended quietly. cloud llm vendors, per reports from september 21, cut general-purpose token prices by over 60% this month, with some base capabilities now permanently free.

the price war moved to everything around the model.

Context

Secondary pages of 24 and 25 September 2026 report price changes on 22 September: OpenAI introduced GPT-6 Sol and GPT-6 Luna at about half the token cost of the versions they replaced (GPT-5.6 Sol at 4.00 dollars input and 20.00 output per million tokens, cut 50 percent each way), Claude Opus 5.5 at 4 and 20 dollars against Opus 5 at 5 and 25 (a 20 percent list cut), an Anthropic claim of 40 percent lower cost on typical workloads from token efficiency, and one vendor cutting cache reads from 0.50 to 0.20 dollars per million tokens (minus 60 percent).

How it compares

All of this is secondary and no vendor price page was read, so the prices are unverified. Over 60 percent appears only as a cache-read price at one vendor, which is a component and not a general-purpose token price; the base token cuts reported are 50 and 20 percent. The changes are dated 22 September in these sources and not 21 September. Some base capabilities permanently free was not found. A cut by two vendors on one day does not measure margins, since price is not cost. The price war moved to everything around the model is the author's take.

Related work

Watch next

  • The OpenAI and Anthropic first-party pricing pages with dates and the source of permanently free.

Sources

  1. LLM API pricing: GPT-6 Sol and Luna (Technori, 24 Sep 2026)technori.com
  2. Frontier price cuts, September 2026 (Cafiyn Pulse, 25 Sep 2026)pulse.cafiyn.com
  3. The cache read price war, September 2026 (flozi, 25 Sep 2026)flozi.net

Provenance

The note above is reproduced unedited from the original post, first published on Threads on 21 September 2026 at 12:08 IST. Sources are the papers and datasets the note draws on.

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