Measure the work behind the multiple.
The headline is about a meter. Your decision is about work.
A circulating chart says Anthropic subscriptions deliver more than five times OpenAI’s value. The claim is worth examining, but its unit matters: API-equivalent dollars estimate what an assumed token workload would cost at public API prices. They do not measure cash refunded, provider compute expense, hours saved or finished work. A generous allowance can be valuable; an allowance you cannot use is still unused capacity.
For a small studio, an IndustryNext project or a UES workflow, the useful question is narrower: which setup gets the required result accepted, on time, at the lowest total cost? PointCast’s calculator makes that question testable without asking for account access or private billing files.
What the original research establishes
SemiAnalysis published the original analysis on October 5, 2026. Its public methodology measures token categories against subscription-meter movements, discards incomplete steps and seeks rate ranges within ±5%. It then applies its own September agentic workload mix. It treats cache reads as free when 500 million reads produce no meter movement. That is a disclosed measurement assumption, not a promise from either provider.
The supplied chart reports $2,084 versus $11,726 of API-equivalent use on the $200 tiers. That is about 5.63×. PointCast has not replicated the experiment or accessed the paid dashboard and raw logs. The screenshot’s tiny workload percentages are not reliable enough to import as calculator defaults.
SemiAnalysis: Anthropic Subscriptions Offer 5x+ More Value Than OpenAI ↗
The price tag changes the apparent value
Official standard, short-context rates put GPT-6.1 Sol at $2 per million ordinary input tokens, $0.10 cached input and $10 output. Claude Opus 5.5 lists $4, $0.20 and $20, respectively. Corresponding short-lived cache writes are $2.50 and $5. Identical quantities in those four billing categories therefore receive twice the API valuation under the Opus rate card.
That arithmetic does not prove either model is twice as capable or that both consume identical tokens to solve a task. It shows why API-equivalent value combines quantity with a vendor’s list price. A price cut can shrink that headline metric while making an API customer’s actual bill smaller. Tokenization, reasoning settings and tool behavior make matched-task testing more informative than matched token counts.
OpenAI API pricing ↗ Claude API pricing ↗
A subscription is not a fixed monthly token bag
The current OpenAI page lists Plus at $20 and Pro at $100, $200 and $500 per month. It explicitly separates API pricing from included subscription usage. Work and Codex share usage; Pro currently has no five-hour cap, while weekly limits may apply. Anthropic lists Pro at $20 monthly and Max web subscriptions at $100 or $200, with five-hour and weekly limits.
Those windows matter. Someone who can work only on Friday cannot automatically collect every reset that occurred earlier in the week. Model-specific restrictions and shared usage across surfaces also affect what remains available. Check the account’s actual dashboard and renewal terms; a three-bar comparison cannot represent every current plan or account.
ChatGPT Work and Codex pricing ↗ Claude pricing ↗ What is the Max plan? ↗ How do usage and length limits work? ↗
Caching rewards a particular kind of work
An agent repeatedly reading a stable repository or document can generate a huge cache-read count. That is different from repeatedly generating new long answers. A cache hit reuses eligible context; it is not a fresh output token. Writes, cache lifetimes and cache misses also have prices. A calculator that prices every input token as uncached will exaggerate API cost for a cache-heavy job.
Use mutually exclusive counts for ordinary input, cache reads, cache writes and output. Include billed reasoning where applicable, tool charges and paid execution resources. Select the correct context length and processing tier. The published presets are reference snapshots; a discounted batch rate, premium speed mode or negotiated rate requires a different calculation.
OpenAI API pricing ↗ Claude API pricing ↗ OpenAI prompt caching ↗
Measure accepted results
Pick a repeatable task and define acceptance before running it: a comparison whose sources check out, a design meeting its brief, or code that passes specified tests. Count every attempt, abandoned run and correction. Record accepted tasks, elapsed time, hands-on review minutes and spending. Keep the task scope and acceptance standard stable across candidates.
A hypothetical $100 setup with $20 in extras and five review hours at $75 per hour costs $495. A $20 setup needing eight review hours costs $620. If each produces 40 accepted results, their effective costs are $12.38 and $15.50 per result. These invented numbers demonstrate the method; they are not evidence of either provider’s quality.
A small test beats a universal winner
Choose three ordinary jobs and one demanding job that you genuinely need done. Give both candidates the same source material, deadline and acceptance checklist. Let each use its normal tools, but record any missing feature or manual workaround. Review outputs without looking at the bill first, then compare the complete costs.
Keep the experiment modest. Repeatedly exhausting a plan to prove its theoretical ceiling may consume more time than the decision is worth. A routine public-information recap and a high-consequence client deliverable can justify different choices. Record why a result was rejected, whether the problem was recoverable, and how long the correction took. Those observations make the next comparison better.
Tools, speed and reliability belong in the comparison
A model name does not describe the whole product. Verify whether the plan includes the browser, connectors, file creation, coding environment and automation surface your workflow needs. A subscription login and an API key can expose different features and billing paths. Do not assume access in one interface guarantees access in another.
Measure time to a usable result, interruptions and failed runs on your own tasks. Separate passive waiting from paid human attention so you do not count every background minute as labor. Do not turn a single session or a public benchmark into a reliability guarantee. Quality gates and missing required features should override a tempting price multiple.
ChatGPT Work and Codex pricing ↗ Claude pricing ↗
Keep the spending boundary visible
Separate the base subscription from optional credits, API fallback, tools and taxes. Anthropic documents separately billed usage credits with spending controls. OpenAI distinguishes alerts from enforced API limits and warns that enforcement is not instantaneous. A budget notification is not proof that further charges are impossible.
PointCast should show the projected total and the reader’s chosen budget, but should never imply that its slider changes a provider’s settings. The calculator purchases nothing, enables no auto-reload and requests no credentials. If actual overage rates or plan coverage are unknown, it should say so instead of manufacturing a precise savings figure.
Manage usage credits for paid Claude plans ↗ OpenAI API spend limits ↗
For client work, check the agreement
Consumer subscriptions, business workspaces and API services are different purchasing choices. Compare data-training settings, retention, access controls, approved integrations and the agreement governing the work. OpenAI’s API and business documentation and Anthropic’s commercial privacy documentation describe protections that should not be casually generalized to every consumer account.
For confidential client material, follow the client’s approved tools and data-handling rules. Avoid testing with sensitive files merely to benchmark a plan. Public or synthetic tasks can establish an initial baseline. A cheaper allowance is not a substitute for authorization, contractual fit or competent review.
Data controls in the OpenAI platform ↗ ChatGPT Work cloud security ↗ Anthropic commercial data and model training ↗
The practical takeaway
Treat a large API-equivalent multiple as evidence about a particular allowance and workload, then test whether that workload resembles yours. Track a representative week, including the work that failed. Compare cash cost, accepted output and human time. Recheck the rate cards when models or plans change.
The best value is the capacity you can turn into reliable work. PointCast’s calculator should make assumptions visible, keep the arithmetic reproducible and leave room for an honest answer: there is not enough information yet to pick a winner.