The Token Department

Start with a unit of work you can recognise: a research brief, a code change or an analysis. A work unit represents a completed workflow with a stated quality standard. Its consumption depends on the model, setting and tools used.Use the same definition of a work unit for demand and capacity. Benchmark task cost is a proxy for consumption. It does not directly measure the allowance a subscription grants, or the consumption of every complete workflow.MindStudio: forecasting token usage. Workflow measurement, agent overhead and peak-demand planning. Published 25 May 2026.

Test the purchase and the route together. For each eligible plan combination, assign the work to exact models and effort settings. Estimate the usable capacity those assignments provide, then subtract the work you need. That gives you headroom or a deficit. Check the reserve, required quality and monthly budget separately. The default ranking favours combinations that cover the workload and reserve. It then compares quality, using price when quality is close.

The result is a purchase and a routing allocation, with a monthly bill and estimated headroom. Provider trust and continuity of access also affect which purchases I would accept.The demonstration uses synthetic plans, models and limits. It tests capacity, quality and budget. It does not enforce redundancy: Plans A and B represent different providers, but together cost US$220 against the example’s US$200 budget. Requiring a second provider changes which purchases are eligible. Scores are synthetic. The ranking compares quality after capacity and budget checks. Measuring accepted work and review effort would be needed to compare realised value. The demonstration includes plan fees alone.

The example uses a quality-first rule within budget. Both Plan A with Model E Medium at US$80 and Plan B with Model Q High at US$140 meet its quality floor and reserve. The US$140 option buys a higher synthetic score: 88.1 instead of 82.4. That is another US$60 a month for 5.7 quality points. Choose the US$80 option if your objective is minimum cost at acceptable quality. Pay the premium when better accepted output or less review and rework justify the extra US$60.

Usable capacity − Required work = Headroom or deficit. Reserve, task quality and monthly budget are separate checks. Click a quantity or check to reveal its inputs; double-click opens the full branch. The example tests four supplied allocations using synthetic plans, models, prices, scores and limits. Capacity remains conditional on the chosen model, effort and usage windows. The 1.5× reserve is provisional.

Work I need

Forecast the workload independently of the plans you might buy.

• Activity: Days × Sessions/day × Agent factor. Count workflow runs and apply the agent multiplier once according to session scope.

• Task mix: Task volumes and their shares. Split sessions by the amount of work in each task category.

• Work-unit size: Task sessions × Tasks/session. Declare the size of a session and the unit of work used to compare capacity with demand.

Quality I require

Quality priorities determine which configurations qualify and how their scores contribute to the comparison.

• Task importance: Importance weights quality independently of volume. A rare important task can have a high quality weight while retaining a low session count.

• Model quality and effort: Exact configuration scores and retained floors. Compare the exact model, effort, capabilities and task-quality evidence.

Budget and access

Cash budget and access constrain the candidate plan combinations.

• Monthly budget: Plan fees + Required extras ≤ Budget. Reserve any required tool and dictation spend before comparing subscription fees.

• Plan eligibility: Models, interfaces, capabilities and provider rules. Keep combinations that meet the access requirements and selected plan-count limit.

Compare plans and model allocations

Test candidate plan combinations and task assignments together. Routing determines both quality and usable capacity.

• Candidate plan combinations: Generate affordable eligible purchases. Try eligible combinations, with one plan per provider and up to the selected plan-count limit.

• Task-to-model allocation: Task volume × Model share × Matched usage. Assign work to eligible exact model and effort pairs within each plan combination.

• Capacity and reserve checks: Shared pools, model caps and usage windows. Test every governing constraint and apply the reserve once.

• Compare the candidates: Safety coverage, task-weighted quality and price. Under the default ranking, prefer covering combinations, then higher quality. Price decides when quality is close.

Recommended mix

The recommendation contains a purchase and a routing allocation, together with estimated quality and capacity.

• Plans and model assignments: Recommended purchase and routes by task. Show which plans serve the workload and the exact model and effort assigned to each task.

• Monthly bill: Selected plan fees + Required extras. Report cash spending against the available monthly budget.

• Headroom or deficit: Selected usable capacity − Work needed. Calculate spare work units for the chosen mix and check the reserve separately.

Illustrative inputs

The example assumes five active days, four sessions per day, one agent and one task per session: 20 completed workflows per week. The monthly budget is US$200 with no required extras. The reserve target is 1.5× average demand, or 30 work units. All plans, models, prices, quality scores, usage rates and limits are synthetic.

The synthetic minimum quality score is 75. Importance weights quality independently of volume. Weighted quality = Sum of (Importance × Task score) ÷ Sum of importance.

Pool and research caps use matched allowance units per week. Peak caps use units per minute. Model Q · High has an assumed peak use of 8 units/minute; Model E · Medium has 4. A and B represent separate providers. Their shared pools are checked independently.

Illustrative candidate comparison

Model Q uses 88 allowance units/week; Model E uses 44 at the same task volume. The two assignments change both quality and usable capacity within Plan A. Plan B’s higher-effort allocation covers the reserve and has the higher quality score. The default ranking recommends it at US$140/month.

For each candidate, divide full-forecast usage by each matched limit. Cover = 1 ÷ Largest fraction used. Usable work C = Work needed D × Cover. Headroom or deficit = C − D. Test C ≥ D × Reserve and Plan fees + Required extras ≤ Budget separately.

The recommended allocation has 32.7 work units of capacity against 20 needed, giving 12.7 spare. Its 30-unit reserve target fits. Capacity stays conditional on the declared work mix, timing and matched limits.

The current selector uses exact Arena category median task costs as allowance proxies and inferred allowances. Measured mean usage of complete workflows can refine expected demand; stress cases and peak windows remain separate checks. The example supplies four allocations to illustrate the selection rule. The full selector searches its eligible catalogue.

Default ranking favours covering combinations, then task-weighted quality. Within 0.5 quality points it prefers lower monthly cost. If no combination covers the reserve, coverage differences above 0.5 percentage points take priority. Headroom is reported as an output.

Start with the work you actually do. Research, coding, analytics, slides. Put a volume against each task type. An overall leaderboard cannot tell you which mix you need.

Then state the standard each task must meet. If you are a lawyer, fabricated case law can invalidate the result. For analytics, you need reproducible calculations. A rare task can still require your most capable model.

How often you do something and how costly an error would be are separate questions. Treat image generation as its own workload, with separate quality and allowance evidence.

Illustrative shares of task counts. Quality criteria are separate judgements. Estimate token consumption from the work performed.

Classify your use as light, regular, high or multi-agent for a first estimate. Longer tasks and parallel agents change how much capacity you need.

Your session history should refine that estimate. Categorise the tasks and record the models and settings used. Include context, tool calls and retries across the whole workflow. One session may contain several tasks, with very different costs.

For each task type, multiply its volume by the assigned model shares and usage per task. Sum the result within the relevant allowance pools. Count background work and agent expansion once. Keep the units consistent with the provider's limits.

The current selector estimates use from Arena's median task costs and inferred allowances. Those estimates remain conditional. Measured mean consumption of complete workflows can improve the forecast. Keep separate checks for stress cases and short usage windows.

Start with a rough estimate. Refine it when the purchasing decision warrants the effort.

Qualitative usage profiles. Frequency, task duration and parallelism guide the first estimate; actual consumption comes from usage records.

Choose a model and an effort setting together. A Pareto comparison first removes options that cost more and score lower.

In Arena's Overall snapshot, Opus 5.5 High costs US$1.80 per task and scores +14.33% net improvement. Astra Max and Fable 5.1 Max both cost more and score lower. Opus dominates them in this comparison.

Then choose among the remaining trade-offs. Sol 6.1 Max costs US$0.59 per task and scores +11.72%: 67% lower median cost than Opus for a 2.61-percentage-point gap. Sol also sits within 0.94 percentage points of Fable 5.1 Max, at 91% lower cost.

I would choose Sol 6.1 Max as my default, and pay for Opus High when the additional quality matters. Check your task category and acceptance criteria before following that choice.Arena Agent Pareto, Overall category, captured 11 October 2026 at 22:34 HKT. Values use 50th-percentile task cost over the preceding 14 days; ranking dated 8 October. Net improvement is a benchmark measure. The chart shows selected configurations and their point-estimate frontier. Rankings carry uncertainty; task-specific acceptance quality and subscription capacity require separate checks.Arena Agent Pareto, Overall category, captured 11 October 2026; ranking dated 8 October.Arena: categories and task cost. Costs sample at most the first three tasks of a session.Arena: evaluation methodology.The Pareto plot uses observed data for selected Overall configurations. Benchmark net improvement is distinct from task-specific acceptance quality. Cost per task is distinct from subscription allowance consumption.

Source: Arena Overall, 50th-percentile cost, median task cost over the last 14 days; captured 11 October 2026, 22:34 HKT, ranking dated 8 October. Selected configurations and their point-estimate frontier. Net improvement is a benchmark score.

Once you have candidate model assignments, compare the plans that can support them. Check access to the exact models, settings and tools. Test each governing allowance, including shared pools, separate model caps and the periods over which they reset.

The same plan can deliver different capacity under different routes. In the synthetic example, Plan A supports 21.8 or 43.6 work units per week. Changing the assignment also changes its quality score. Capacity and routing are part of the same decision.

Cursor gives a concrete example of why routing belongs in the purchase decision. Selecting a third-party model draws down its Other Models pool at that model’s token rates. At the captured benchmark task costs, Sol Max would use about a third as much of that pool per task as Opus High. That could support about three times as many comparable tasks from the same pool, if your usage matches the benchmark. The plan’s other included capabilities also contribute to its value.Cursor models and pricing: Cursor Models and Other Models are separate pools that reset monthly. Third-party usage draws from Other Models at the selected model’s application programming interface (API) rate. Input, output and cached tokens have different rates. Pool consumption and the subscription’s cash fee are separate quantities. Cursor’s legacy Max Mode concerns context and billing; it differs from a model’s Max reasoning effort.Cursor: task-based usage estimation discussion. Users request usage ranges and explained model choices. The response identifies output, tools and extra context as sources of uncertainty. August–September 2026.

For a company, group people by workload and intensity. Long coding runs need a different forecast from brief research requests. Aggregate under the actual commercial terms, including shared and individual allowances. Buying everyone the same plan without checking the work is procurement by shrug.

I also need to trust the provider. Anthropic has shut me off three times in 2026 without explaining why. That experience changes what I am willing to depend on. A provider can also become a competitor to businesses that use its models.These examples establish overlap between supplier and customer products. Cursor offers Anthropic models, while Anthropic introduced Claude Code. Chegg announced CheggMate, built with GPT-4 in April 2023; OpenAI introduced ChatGPT study mode in July 2025. My inference is that a model supplier can also compete in its customers’ market. These sources do not establish copying or intent.

My answer is multiple models and redundancy across providers. I want a primary route and a usable alternative, with the context, prompts and tools ready to move. Test the alternative on the work it may need to take over. Buying that continuity may cost more. It belongs in the plan decision.

The table compares the default synthetic allocations with demand of 20 work units and the provisional target of 30. Changing inputs in the interactive example does not update this table. Fees, quality scores, models and allowances are illustrative.

A plan that runs out at the deadline is a false economy. Forecast the busy periods as well as average use. Longer tasks and repeated attempts can push demand above the forecast.

My starting capacity target is 1.5 times estimated average use, giving a 50% buffer. Test it against actual variation within each governing window. The right reserve depends on the work and how costly it is to run short.1.5× is the author’s provisional starting target. It adds 50% to estimated average demand. It does not imply a quantified probability of sufficient capacity. Refine it using observed variation within the actual reset windows.

Author’s starting assumption, normalised to average demand. Check the buffer against observed variation within each usage window.

Define value as completed work that meets the required standard per dollar spent. Record review time, failed attempts and rework alongside it. The allowance you can draw down is one input to that outcome.

Compare the forecast with what actually happens. Where did capacity run out? Which tasks used more than expected? Adjust the routes and plans from those records as workflows, models and user skills change.

As you get better at delegating, you may find more work worth giving to agents. More useful output can justify higher total spending. Judge the allocation by both the bill and the work delivered.

Author’s proposed review cycle. Accepted work meets the stated quality standard. Value divides accepted work by total spend; review time, failed attempts and rework remain visible alongside it. Demand growth is an unmeasured possibility.

Assume US$200 per user each month in plan and token spend. Cut avoidable spend by 10%, while keeping required quality and capacity. That saves US$20 a month per user. Across 10,000 users: US$2.4 million a year. At 100,000: US$24 million. These are illustrative gross savings before the cost of running this process; US$200 is an assumption.The corporate calculation assumes US$200 per user per month and a 10% reduction in avoidable spending. US$200 is an illustrative baseline. No organisation-wide average has been established here. The individual case assumes US$100 of monthly savings. These examples show scale; realised savings depend on the waste available to remove. Subtract the operating cost of maintaining evidence, routing and reviews to estimate a net saving.

For an individual, saving US$100 a month adds up to US$1,200 a year. Your actual saving depends on the waste available to remove. For a company, the decision includes how much useful work the spend enables.

Illustrative annual savings in plan and token spend, in US dollars. Corporate examples assume US$200 per user per month and a 10% reduction. The individual example assumes a US$100 monthly saving.