Every plan gets one number: how many coding turns of mixed work its allowance covers in a month. The allowance is taken at API list prices, and each kind of work is done on the plan’s cheapest model that is good enough.
One turn is an average agentic coding request: 2.9M input tokens, 98% read from cache, and 9.8K output tokens. These are the averages Slopalytics publishes from T3 Code usage, observed Nov 10, 2026. Each model’s turn is priced from its own token prices, so cheap cache reads count for a lot.
Context is the same at every reasoning setting. Output scales with the setting, by the cost ratio Artificial Analysis measured against the model’s high setting. A model listed without a setting is treated as high and labelled “default reasoning”. A model with no cache price pays full input price.
The alternative unit is a benchmark task: one Artificial Analysis Intelligence Index task at the cost they measured. It is light on tokens and caching, so it favours models with expensive cache reads.
Nobody runs one model all month. Work is split into three kinds, each needing a model of a certain score:
The Work mix setting says how your turns divide between them:
A plan’s value is one number: the turns of that mix its allowance covers in a month. For each kind, the plan uses whichever of its models and reasoning settings stretches the allowance furthest: the cheapest setting that is good enough, not the maximum. The plan always chooses, so an extra model never lowers its value. Claude Max can do everything five Claude Pros can, and comes out at five times Pro or more.
A model that falls short of the score a kind of work needs still counts, for less: every 2 points short halves what a turn counts. A model 2 points under counts half a turn, 4 points under a quarter. While a plan has a model within 6 points of a kind of work, models further short are not used for it, however cheap: retrying a weak model does not add up to a strong one. A plan with nothing that close still does the work on its best model, at the small credit the halving gives, so on a mix with hard work a plan of small models shows how little it gets you. One number needs some exchange rate between quality and quantity, and no data source gives one; this is ours. It decides how plans without strong models fare. Turns of balanced work per dollar if it were different:
| Halves every | 1 point | 2 points (used) | 3 points | 5 points |
|---|---|---|---|---|
| Claude Pro | 23 | 42 | 50 | 57 |
| Claude Max | 52 | 53 | 53 | 56 |
| ChatGPT Pro 100 | 0.96 | 4.7 | 7.9 | 12 |
| Google AI Ultra 20× | 14 | 18 | 19 | 20 |
| Copilot Pro+ | 3.9 | 5.2 | 5.6 | 6 |
| Muse Code Power | 0.018 | 0.9 | 4.7 | 14 |
| Kimi Max | < 0.01 | 0.022 | 0.11 | 0.34 |
| Mistral Vibe Student | < 0.01 | 0.1 | 2.7 | 32 |
The order among plans with strong models holds across these; plans whose best model is far below a kind of work move by orders of magnitude, so read their figures there as “very little”, not as a precise amount. The hard-work score is set by the best model on the market, which today is Anthropic’s, so plans from other providers count at a discount there.
ChatGPT Pro 100 has no frontier model. Its closest for hard work is GPT-6 Astra at 56.9, 4.7 points short, so a turn on it counts 20% of a hard turn. That turn costs $4.37 at API prices, because OpenAI’s cache reads are expensive and an agentic turn is mostly cache reads, so each hard turn uses up a lot of the allowance. GPT-6.1 Sol is cheaper but more than 6 points short, and is not used while GPT-6 Astra is within reach. On Balanced, ChatGPT therefore gets 4.7 turns per dollar; on Mostly light, with no hard work, 132. Which mix you pick matters most for plans like this.
A model the plan caps, such as Fable at half of the weekly limit on Claude Max, may take only that share of the allowance. Anthropic’s help page says Fable draws from the regular weekly limit, so its meter is treated as a cap inside that limit, not as extra usage. Min. intelligence hides plans whose strongest model scores below the threshold.
With Engineering index on, scores come from Artificial Analysis’s Engineering Index instead: engineering knowledge, reasoning, agentic coding and terminal use, broader than coding alone. It runs a few points higher, so the normal and light bars are carried over by a straight-line fit between the two indexes across every model; the hard-work bar follows the best model on that index.
One line per product, running from its cheapest tier to its dearest. In the per-month view, hover a plan to see its equal-deal line: plans above it give more per dollar, plans below give less. The Pareto line, off by default, marks the most value available at each price; the plans on it are circled with a dotted ring.
For each product, the line gives the price of its cheapest tier that covers the number of turns of mixed work you need a month, stepping up where you outgrow a tier. The lowest line at your usage is the cheapest plan; the dotted Cheapest overall line traces it across all products, with a dotted ring at each plan where the cheapest option changes. Unlike Price vs value, this does not assume you use your whole allowance.
Pick a monthly budget. Every combination of plans that fits in it is checked, up to the number you allow (two by default), and the ones covering the most turns of mixed work are listed, with the best single plan for comparison. Each plan counts as in Price vs value, on the chosen work mix. Plans in a setup simply add up; giving each kind of work to the plan best at it would do better. A plan is only added if it earns its price: it must bring at least a tenth of the setup’s own turns per dollar, otherwise the setup without it is the better buy. Only plans one person can buy monthly are used: no team seats or yearly billing, and student plans once. Several accounts at one provider are allowed by default and can be turned off; providers’ terms may not allow them. The figures assume you use every plan’s whole allowance.
Moving to another account loses the prompt cache, so the agent reads its working context again at full input price. Each switch is charged as 150K tokens of context at the difference between the input and cache price, about half a turn on most models, taken from the account switched to. The same re-read costs a small plan as much as a big one, so it takes a bigger share of a small plan. You switch to an account each time the one before it runs out: about once a day (30 times a month) for plans with a five-hour limit, about four times a month for weekly pools, and once a month for monthly allowances. The account doing the most work is used first and is not charged. Cache-write surcharges, re-exploring a codebase in a new session, and the friction of a second agent are not counted, so the real loss is higher.
Each plan’s largest allowance at API prices divided by its price. 1× costs the same as paying the API for the same model; above 1× the provider is subsidising you. Allowances inferred from another model’s (Kimi K3) are left out.
Each dot is a model at one reasoning setting; left is cheaper per turn, up is smarter. By default there is one line per provider across its standard monthly plans, and each model sits on the cheapest of those plans that includes it, so a model only the bigger tiers include (Fable, Gemini 4 Carbon, Muse Wonder Full) still appears. A dot is kept only if it scores higher than everything cheaper on its line; settings that cost more and score less are hidden. The dashed line is the API: the cheapest model at each score bought by the token. All tiers draws every plan as its own line.
Every plan is graded by the weakest of its inputs: published when the provider states the allowance, on its site or in the subscriber’s account; measured when it was read off real accounts; derived when it is a published or measured figure scaled by a multiple the provider has stated; estimated when it rests on an assumption of ours; anecdotal when only unverified user reports exist. Estimated and anecdotal plans are drawn hollow and tagged “est.”.
The provider states the allowance. GitHub Copilot gives monthly AI credits worth $0.01 each. OpenCode Go gives a dollar allowance per model. Z.ai gives weekly credits and a token-to-credit formula; the range runs from the worst token mix at peak hours to the best off-peak. Mistral states the monthly allowances of Vibe Pro and its Education version in the subscriber’s account: €127.50 of Vibe Code usage and €12.75 of API usage (read 12 November 2026).
Not published, but independently measured on real accounts with a described method.
The measurements priced usage with 92–97% of tokens as cache reads; the turn profile used here has 98%. OpenAI moves to a shared token allowance in December 2026; its figures need re-measuring after that.
Only unverified user reports exist. Treat these as a rough guide. A figure resting on a single report is shown from half to double it.
MiniMax, Devin and ChatGPT Go publish no absolute allowance, and no measurement or credible report exists. They sit on the price-only row below Price vs value, show as “?” in Subsidy vs API, and are listed in Model access. Meta One includes no coding tool; Meta’s coding plans are Muse Code.
Model scores and prices come from the Artificial Analysis Data API, synced Nov 11, 2026 (734 model entries, Intelligence Index v5). Plan prices and limits are maintained by hand, last checked Nov 12, 2026; every plan links its sources on the Plans page. Logos are from the lobe-icons set and belong to their owners.