AI coding subscriptions, compared

Prices, quotas and token rates for AI coding plans and models
Plans tracked
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Directly comparable
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with full model pricing
Model families
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matched across plans
Live sources
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official feeds & docs

Positioning

One dot per plan and model. Default axes: tokens per money against AI score.

no training on your data trains on your data privacy unknown Pareto point Pareto frontier Target zone

Use the arrow keys to move between dots, Enter opens its numbers, Escape clears the selection. The same values are in the table below.

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Best value right now

One row per plan, strongest model, ranked by tokens per unit paid. Only plans that do not train on your data and come with API access.

Best value plans and models
Plan Model AI score Tokens / $ Tokens / mo Plan price / mo

All plans and models

Rates are tokens or requests per 1 paid unit, in your currency.

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Plans and models, sortable columns
Plan Model AI score Tokens / $ Requests / $ Tokens / mo Requests / mo Included volume Plan price / mo Privacy

Same model, different plans

Request rate per $1 paid for one model family across plans. Plain numbers, no podium.

Budget calculator

One subscription per account, no stacking. Enter what you pay per month and see the most tokens each single plan buys you.

Plan · model Tokens / mo Price · left

Ranked by monthly tokens.

Changelog

What changed for you, newest first.

Why “60 for 10” is not the answer

The sticker value hides the real economics. Here is how plans are made comparable, every step reproducible.

1

Real token prices, not marketing

Every model behind a plan has published API token prices: input, output, cached-read and cached-write. That is the ground truth behind any credit or dollar-usage number.

2

Provider credit formulas

Providers define their own credits (GLM: (in×6.9 + cached×1.7 + out×24)/10000). Those formulas come from the official docs, never invented.

3

Cache-aware workload model

Coding agents hit the cache constantly. Input is priced 5% fresh + 95% cached-write, cached-read and output at their real rates, per model and workload pattern.

Show all steps
4

Windows are caps, not volumes

A 5-hour window is a throughput limit, not a monthly quota. Weekly credits scale to a month (×4.33); 5h caps are never multiplied into fake monthly numbers.

5

Fair pattern unification

Some feeds reuse one generic workload pattern. For shared model families we use the most precise per-model pattern for both plans, so a cheap pattern cannot rig the comparison.

6

Undisclosed stays undisclosed

If a provider hides its numbers, we say so. No invented credits, no back-calculated quotas. Honesty is a feature.

Frequently asked questions