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The Frontier Wire compared every per-prompt energy and water figure in its dataset. The extremes differ 2,300-fold for energy and over 1,600-fold for water, but four independent estimates for an ordinary text prompt sit within a factor of 1.75. Scope, request length and modality explain the rest.
An analysis of company sustainability reports in The Frontier Wire's AI water and energy dataset. Every one of the six companies that publish two years of water and electricity used less water per kilowatt-hour. None used less water. Together they added about 17.7 million cubic meters in a year.
Decision models from TypeSafe, OpenAI, Perplexity, Cloudflare, Microsoft, Databricks and Ollama return typed answers with probabilities, in two request dialects. Bifrost v2.3.0 gives them one endpoint, stops silently answering decisions with chat models, and prices decisions on their own rates.
Every LLM bill is requests times tokens times price. A playbook for shrinking each factor without a quality regression: attribution, right-sized models, complexity routing, prompt trimming, prompt and response caching, Batch APIs, loop caps and budgets, plus the open-source llm-cost-reduction agent skill.
Claude usage is measured three different ways depending on how a request signs in. Subscriptions run on five-hour and weekly windows, API keys run on rate limits and spend caps, and usage-based Enterprise seats are billed per token. Where each meter lives and what drains it, per Anthropic's pages on 21 October 2026.
OpenRouter's catalog holds 468 entries under 57 publisher prefixes, but the price next to each name is the price of one endpoint, not of the model. Nineteen leading models compared on published criteria, with picks by job and the routing settings that decide which host, precision and price tier serve a request.
Every major model API now discounts a repeated prompt prefix, but the rules differ on markers, minimum lengths, lifetimes and write surcharges. A provider-by-provider implementation guide with current prices, the usage fields that prove a hit, worked cost examples and the gateway settings that keep a cache warm.
Six algorithms, four places to enforce them, one status code and a draft standard for telling clients how much quota is left. A working reference for rate limiting HTTP APIs, with the request and token limits that OpenAI, Anthropic and Google apply to model traffic.
Claude Code is included in every paid Claude plan and can also run on pay-as-you-go API keys, cloud accounts or a gateway. Which credential a developer uses decides which bill a request lands on, and context, caching and model choice decide how large that bill gets.
OpenAI's Codex and Anthropic's Claude Code now cover the same ground, from terminal to IDE to cloud sessions. They still differ on defaults that matter: who sandboxes what, how plans meter usage, what is open source and how an administrator locks a fleet down.
Frontier models now cluster around $2 per million input tokens and $10 per million output tokens, but batch discounts, cache multipliers, long-context tiers and off-peak clocks move the real bill by an order of magnitude. Every price below comes from the providers' own pricing pages as of October 15, 2026.
OpenRouter made one API key for hundreds of models the default way to prototype. Credit fees, a third party in the data path and governance that lives in someone else's account send production teams looking for alternatives. Seven options, ranked against published criteria.
Provider prompt caches cut the price of a repeated prefix by 90 percent or more but leave output tokens untouched. Semantic caches remove whole calls but can return the wrong answer. The mechanics, prices and failure modes of both, with a worked cost example.
Running a model on your own hardware is now a configuration problem more than a research one. The hard parts are sizing GPU memory, choosing between Ollama, llama.cpp, LM Studio, vLLM and SGLang, reading the licence, and putting a production API in front of a server that was built for one user.
In the spring of 2026, engineers at large technology companies competed on leaderboards ranked by how many AI tokens they consumed. By summer the leaderboards were gone and the budgets were blown. The episode is a lesson in what a token is: a unit of cost, not a unit of work.
Claude Opus 5.5 leads the independent indexes this month, but no single model is best at everything. Ten models, from $50-per-million-token flagships to MIT-licensed open weights, measured against published criteria, with the best pick for reasoning, coding, cost, long context, multimodal and self-hosting.
TypeSafe's Jev returns typed answers with probabilities instead of text. Its documentation, the first developer reports and early independent papers show where it fits in a pipeline, what the published price works out to, and why its probabilities need checking on your own data.
Output tokens cost five to six times more than input tokens, reasoning tokens bill as output, and shared API keys erase the link between a charge and its owner. This is how teams attribute and cap LLM spend, and what gateway-level governance in the open-source Bifrost gateway does about it.
API gateways were built for short request-response calls priced per request. Model traffic is priced per token, streamed for minutes and shaped differently by every provider. A mechanism-by-mechanism comparison, grounded in AWS, Kong, NGINX, OpenAI and Anthropic documentation, with Bifrost as the worked example.
Routing, gating, judging and choosing an agent's next step are decisions, not writing. Splitting decision from generation is the right architecture, and TypeSafe's Jev is the first product built for it. Trust will come from independently measured calibration, not a promise that it cannot hallucinate.
TypeSafe AI came out of stealth on September 15 with $40 million in seed funding and Jev, a model that answers typed questions about a block of text with probabilities instead of prose. The pricing is unusual, the speed claims are large, and the first independent tests are mixed.
Models that think before they answer are billed for every token of that thinking, whether or not you see it. The controls have moved from token budgets to effort levels, and the price of a request now depends on how hard the question is.