Anthropic and Stacklet Make Cost per Task the AI Number to Sell

On October 7, Anthropic released Claude Haiku 5.5 at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens. Haiku 4.5 charges $1 and $5. Above 100,000 tokens the new model costs $0.50 and $2.50. Its new tokenizer uses slightly more tokens per task, so Anthropic puts the average saving at about 75% rather than 90%. The same release halved Sonnet 5.5's cache-read price to $0.10, which Anthropic says makes most agentic work on that model roughly 20% cheaper. HubSpot, one of the launch customers, said the model posted the best score it had seen on its internal test suite. Haiku's new rate matches what OpenAI charges for GPT-6 Luna, per SiliconANGLE.

A day earlier, Stacklet put Token Custodian into early preview. It's built by the team behind Cloud Custodian, the open-source cloud-governance engine, and it does for model APIs what that engine did for cloud accounts. It traces every token to a team, agent, project or cost center across providers. Finance can set budgets by team, project or environment. As a limit gets close, the tool can move the workload to a cheaper model or send it for approval. General availability is planned for the fourth quarter.

One is a price cut and the other is a governance product, but both are about AI unit economics. The price of a unit is falling at the API while the buyer's control over how many units get used is rising. For anyone selling an AI feature on a meter, that moves the number that matters from price per token to cost per finished task. It also hands the choice of model to whoever owns the budget. A vendor whose pitch is "our tokens got cheaper" is selling the half of the equation the buyer no longer worries about.

At max effort and equal list prices, Haiku costs three times as much per task

The clearest evidence comes from comparing two models that cost the same per token. Artificial Analysis lists Haiku 5.5 and GPT-6 Luna at an identical $0.10 and $0.50. At maximum effort, Haiku 5.5 costs $0.21 per task on the firm's Intelligence Index and generated 440 million output tokens across the suite. Luna at maximum effort costs $0.07 per task on 140 million. Haiku scores higher, 43 to 38, but it buys those points with roughly three times the output. The firm's launch thread says Haiku uses more output tokens than Luna to reach similar scores at every effort setting.

Users ran into the same gap. A post on r/ClaudeAI titled Haiku 5.5 cost 12x more than GPT-6 Luna for the same voxel-art demo drew more than 900 upvotes. The top replies doubted the poster's setup, and one blamed the price step above 100,000 tokens, which multiplies the rate by five. Treat the post as an anecdote. On either reading, the advertised discount depends on prompt length and effort level, and both are set inside the workflow.

The discount is real for the work Anthropic built it for. Anthropic says about 90% of Haiku 4.5's requests ran under 100,000 tokens, and it recommends Haiku 5.5 for short, high-volume jobs such as summaries, classification and subagents. SaaStr's Jason Lemkin posted on launch day that Haiku 5.5 ran 85% cheaper and 1.5 times faster on SaaStr's own AI apps. A lead-scoring call, an enrichment lookup or a routing decision fits that profile. A research agent that reads a whole account history at maximum effort doesn't. And a cheaper model only saves money if work moves to it. A much-upvoted r/ClaudeCode post a day after launch pointed out that Claude Code's built-in Explore subagent no longer runs on Haiku by default. For many users, background work they assumed was cheap was probably still running on Opus.

Buyers began capping token volume before prices fell

Token Custodian is the latest in a run of controls that began when usage broke budgets. In June, 404 Media reported that Uber's CTO had said the company blew through its AI budget in four months, and that Accenture was trying to rein in token use by non-technical staff. By July, Atlassian had put engineers on monthly AI wallets of $500 to $2,000 that cut off at the limit. Microsoft gave every division a token cap and switched its default internal model to a cheaper one. Ramp launched AI Token Spend Management on July 16 with limits by team, project or API key, and said one in three customers had a cheaper model available that handles the same task as well.

The independent number is the FinOps Foundation's. In its 2026 survey, 98% of 1,192 practitioners said they manage AI spend, up from 31% two years earlier. The foundation is also finishing a billing standard, FOCUS 1.5, that adds a field naming the person, service account or other entity behind each charge. Ratification is scheduled for December 3, according to TechTarget. Once that field exists, a vendor's AI usage lands in the buyer's cost reports attributed to a user and a workflow, next to every other provider's.

Stacklet adds graduated action. Its CEO, Travis Stanfield, told TechTarget that the budget controls the model vendors ship are mostly on or off, and have to be set again in each tool. Token Custodian's pitch is to act across all of them as spend happens, rerouting work to a cheaper model or asking for approval when a budget binds. When the buyer's policy engine picks the model at the limit, a vendor whose feature only runs well on an expensive model is the one that gets throttled.

A CRM vendor has now shipped those controls on its own side of the bill. On October 15, HubSpot turned AI spend controls on by default, setting an account-level credit limit up to 50% above a customer's monthly credits, and let customers set limits at the feature and action level. That's finer than the on-or-off budget switch Stanfield describes, though it governs only HubSpot's own credits. HubSpot's new pay-as-you-go option bills usage beyond monthly credits only when the work gets done, which isn't a published per-task price but moves the meter toward the finished task.

Cheaper tokens could make caps moot, and GPU prices are rising underneath

Lemkin's own conclusion was that not all AI is inflationary, and the strongest objection follows from it. If small-model prices keep falling this fast, caps stop binding and governance tools become reporting tools nobody opens. That may happen for short jobs. It hasn't yet for agents, whose token counts climb with effort and context. The caps at Microsoft and Atlassian were written in dollars of usage per person, which is the number that grows when an agent runs longer, whatever the rate card says.

Stacklet's evidence also deserves a discount. The survey in its release, in which 43% of 472 respondents named proving value as their top AI-spend problem, comes from the Tokenomics Foundation, and TechTarget notes that Stacklet is a founding member. The product is a preview with no named production customer. Avalara, which is quoted, plans to trial it. Omdia analyst Torsten Volk told TechTarget that FinOps teams shouldn't decide model access alone, which is a fair warning about routing rules written by people who don't own the outcome.

And the API price isn't the whole cost. Nebius announced on September 17 that on-demand prices for some Nvidia GPUs would rise 17% to 21% from October 1, which we covered at the time as a cost pass-through problem. Vendors that rent GPUs and run their own models face rising unit costs, at least at Nebius. Vendors that buy finished tokens from Anthropic or OpenAI face falling ones. Their margins now depend on which layer they buy from.

What this means for AI vendors and buyers

If you sell an AI feature on usage, publish what your three most common workflows cost per completed task, at the effort setting and context length you actually ship. Re-run the numbers each time you change models. A buyer with a budget engine will compute it anyway, and it's better to hand over your own figure than to argue with theirs.

Make your usage legible to the buyer's tools. Break consumption down by user, agent and project, and plan for the FOCUS principal field so your line arrives attributed. A single opaque charge is the one a policy engine caps first.

Decide what to do with the Haiku saving before a customer asks. If your feature runs short, high-volume calls on Haiku 4.5, your model cost on that work can fall by as much as 90% once you switch. Procurement teams can read Anthropic's pricing page too. Say whether you'll pass some of the saving through. Put the cap and overage terms in the order form as well, since that’s the same fight over the meter's default cap Microsoft started with Copilot.

On the buying side, re-cost the agent workflows shelved on price, but per task, with the effort level pinned and the prompt length checked against the 100,000-token line. Then set budgets per workflow rather than per seat, and ask each vendor what its product does when the budget runs out.

My bet is that within two quarters at least one usage-priced GTM vendor puts a per-task price for an agent workflow on its public pricing page, next to the credit price. Token Custodian's general availability in the fourth quarter is the first test of whether buyers start asking for it.

A vendor whose pitch is "our tokens got cheaper" is selling the half of the equation the buyer no longer worries about.

If you sell an AI feature on usage, publish what your three most common workflows cost per completed task, at the effort setting and context length you ship. FOCUS 1.5, the billing standard that names who is behind each charge, is scheduled for ratification on December 3.

By Business of GTM