What does it cost?
What would it cost to actually do something?
The same prices in the unit a person has: one question, one three-hundred-page document, one day of an agent working.
a short reply, no documents attached
bar is the cost, log scale
1,000 tokens in, 500 out — a paragraph of prompt and a few paragraphs back. These are published list rates only — no cached input, no batch discount, no retries and no tool calls — so treat every figure as an upper bound. Token counts differ between models, and a model's own rate can change: the table above says which prices are confirmed and which are not.
What would it cost to actually do something?
The same prices in the unit a person has: one question, one three-hundred-page document, one day of an agent working.
summarise it, ask questions of it
bar is the cost, log scale
150,000 tokens in, 2,000 out — about 500 tokens a page for 300 pages, and a summary. Long enough to cross some models' context thresholds, where the rate per token steps up. These are published list rates only — no cached input, no batch discount, no retries and no tool calls — so treat every figure as an upper bound. Token counts differ between models, and a model's own rate can change: the table above says which prices are confirmed and which are not.
What would it cost to actually do something?
The same prices in the unit a person has: one question, one three-hundred-page document, one day of an agent working.
many turns, files read over and over
bar is the cost, log scale
1,000,000 tokens in, 100,000 out — an agent re-reads its context on every turn, so input dominates. A real agent run is usually much cheaper than this because most of that input is served from cache, which this does not count. These are published list rates only — no cached input, no batch discount, no retries and no tool calls — so treat every figure as an upper bound. Token counts differ between models, and a model's own rate can change: the table above says which prices are confirmed and which are not.
What this is not
It is the price of the tokens, not the price of the job. Nobody bills a question at list rate: caching, batch discounts, retries and the four rejected attempts before a good answer are all real, and none of them are in these numbers. Read them as the floor — the part of the bill that scales with what you send and what comes back.
And it says nothing about whether the output is any good. This site holds prices and it holds capability scores, and it keeps them apart on purpose: a cheap model that cannot do the job is not cheaper. The Board is where to look for which model is actually better, test by test, and the Gap for how far the downloadable ones are behind.
What does each model cost?
16 current models — the newest in each major lab's main lines — priced per million tokens from each seller's own page wherever we may read it. Every other rate we hold is a click away.
16 of 803 rates, every one from a confirmed source.
How to read this table
Prices are per million tokens, in US dollars. Every row names the seller it came from, and where that is not the seller's own published page the mark says so: * means the number comes from a single registry or two that disagree, so it is the best available rather than a confirmed one. The same mark covers both because the remedy is the same — check the seller before you commit.
A seller with no page we may read is not a gap. DeepSeek and Kimi publish none we may fetch, so their rates are confirmed another way rather than dropped — DeepSeek from the licensed index, Kimi from two registries that agree — and each row's tooltip names which. Every other row on this page is the number the seller publishes itself.
A row is a rate, not a model. One model can appear four times at four prices — standard, batch, flex, fast — and again for audio or image tokens, and again either side of a context threshold. This page opens on the standard text rates because those are the ones a reader compares; the toggle shows everything.
Where a seller has announced a rise, it is named here. A dated increase a reader can still act on is the most useful thing this table holds, so it sits beside the current rate rather than in a footnote.
AI deflation curve
What doing GPT-5 Pro's work from twelve months ago costs today, against what GPT-5 Pro still costs.
Matching what GPT-5 Pro scored twelve months ago costs $0.090 per million tokens today, from DeepSeek V4 Flash 0731. GPT-5 Pro itself still costs $41.25 — so the same capability is 458× cheaper if you switch. Both prices are today's - the sellers do not keep last year's rate card online.
* Two registries disagree on that price — we show models.dev's figure. Treat the ratio as approximate.
Cheapest at that level today
DeepSeek V4 Flash 0731
$0.090/Mtok
index 154.41 · DeepInfra · via registry-disputed
The model that set the level
GPT-5 Pro
$41.25/Mtok
index 151.23 · 2025-10-07
The same question, three different bars
what was frontier 6 months ago
34×cheaper
bar set by GPT-5.5 Pro
cheapest now: GPT-6.1 Terra $2.00
what was frontier twelve months ago
458×cheaper
bar set by GPT-5 Pro
cheapest now: DeepSeek V4 Flash 0731 $0.090
what was frontier 2 years ago
27×cheaper
bar set by o1-mini
cheapest now: gpt-oss-120b $0.070
Three different measurements, not one at three precisions: the bar rises as the horizon shortens, so a different model clears it each time. Read together they are a price gradient across capability levels on one day, not a trend over time — the trend is the history below.
What the same ability costs over time
Each line is one level of ability, named after the model that first reached it, and shows the cheapest price you could pay on each date for any model at least that good. Work that needed GPT-4 in March 2023 cost $37.50 per million tokens; today the same work costs $0.17. Newer levels start high and fall the same way, a year or two behind.
Reconstructed, not observed. Prices come from the CC-BY-4.0 AI Price Index's record of what each seller charged and when, which covers fewer sellers than the readings at the top of this page. A model counts once Epoch AI's Capabilities Index scores it at or above the level, and not before its own date. A step up is real: the cheapest qualifying model was withdrawn or repriced.
cheapest price per million tokens, blended 3:1 input to output · log scale
| Work at the level of | When it was new | Cheapest today | Today | Change |
|---|---|---|---|---|
| GPT-4 level | $37.50Mar 2023 | Gemini 2.5 Flash-Lite (Jun 2025) | $0.17 | 214× cheaper |
| o1 level | $26.25Dec 2024 | GPT-6 Luna | $0.20 | 131× cheaper |
| GPT-5 level | $3.44Aug 2025 | GPT-6 Luna | $0.20 | 17× cheaper |
Why the multiples in this section differ
The numbers at the top use today's prices only. They take the model that was best six, twelve or twenty-four months ago, look at what it still costs, and divide by the cheapest model today that is at least as good. GPT-5 Pro still costs $41.25; DeepSeek V4 Flash 0731 does the same work for $0.090, so it is 458× cheaper. No old price is involved. It answers: if I am still paying for last year's best model, how much would I save by switching today?
The numbers in this chart compare then with now. They take a model's price when it was new and divide by the cheapest model at least as good today. GPT-4 cost $37.50 in Mar 2023; Gemini 2.5 Flash-Lite (Jun 2025) matches it for $0.17, so it is 214× cheaper. It answers: how far has this ability fallen in price since it first appeared? The older the ability, the longer its price has had to fall.
Why one model can show two different multiples. Where a model appears in both places, both start from its own price today. They differ in where they look for the cheapest match: the top uses every seller we read, and this chart uses only the licensed price index, because it is the one source that records what prices were in the past and it covers fewer sellers. That is why the two are never drawn on one line.
The cheapest models we can price118 scored models carry a price
| Model | Blended /Mtok | Context |
|---|---|---|
| allam-2-7b | $0 | — |
| GLM-4.5-Flash | $0 | — |
| GLM-4.6V-Flash | $0 | — |
| GLM-4.7-Flash | $0 | — |
| Ternary Bonsai 27B | $0 | — |
| GLM-OCR | $0.030 | — |
| Llama Prompt Guard 2 22M | $0.030 | — |
| Tev1 4B Experimental | $0.032 | — |
| gemma-4-E4B-it | $0.040 | — |
| Prompt Guard 2 86M | $0.040 | — |
| Granite 4.0 H Micro | $0.041 | — |
| qwen3.7-flash | $0.055 | — |
Cheapest overall, not filtered to the capability tier — the tier comparison is above. Looking for a particular model rather than the cheapest? Every rate we hold.
What we published before, and what changed
Until 13 September the curve computed its ratio from an aggregator's route price — the price of whichever seller the router happened to use that day. That source is no longer used, for two independent reasons: its terms forbid republishing what we took from it, and it turned out to measure something other than a rate card. Measured over six days of it, no model whose price moved was sold by a single seller, and a third of the movements returned to a price the model had already held.
| Date | Ratio, route prices | Cheapest at that level then |
|---|---|---|
| 2026-09-06 | 55× | DeepSeek V4 Flash 0731 |
| 2026-09-07 | 29× | GLM-5.3-Flash |
| 2026-09-08 | 37× | DeepSeek V4 Flash 0731 |
| 2026-09-10 | 37× | DeepSeek V4 Flash 0731 |
| 2026-09-12 | 69× | DeepSeek V4 Flash 0731 |
| 2026-09-13 | 69× | DeepSeek V4 Flash 0731 |
The two series are not comparable and the difference is not a correction. The old reading for 13 September was 69× on route prices; the same day answers 458× on rate cards, because a router can route to whichever seller is cheapest at that moment and no seller's published card offers that. The cheap end of this market turns out to be a routing outcome rather than a list price, and that is worth saying rather than quietly swapping one number for the other.
We also publish the price rises sellers have announced in advance. When one of those dates arrives, this page is where we will say whether the price actually moved as they said it would.
How it is measured
Why capability is held fixed. Not what models cost — every comparison site answers that, and it answers the wrong question. Prices fall both because models get cheaper and because they get better. This holds capability constant and lets price move.
Both prices are today's. Sellers do not keep last year's rate card online, and the licensed index that does record price history covers only some models — so a genuine then-versus-now is not available for every model, and this does not pretend otherwise. What it compares is the cheapest model that clears the old frontier's level against what the model that set that level still costs — both current, both checkable. The observed series starts the day the stage first ran and accrues forward; the history of fixed levels further up is reconstructed from the licensed index's dated records and is kept as its own series.
The reconstruction prices each past day, it does not observe it. For a date in the past, the bar is the closed frontier as Epoch had it then — Epoch dates every score, so that half needs no price history at all — and both prices are the ones the CC-BY-4.0 AI Price Index records as effective on that date. Nothing about it is mixed into the readings at the top of this section, because the two use different seller sets: the observed series carries seven sellers read first-hand and two registries, the reconstruction carries the licensed index alone. On 14 September the twelve-month bar answered 458× here and 7.6× reconstructed, and the whole of that gap is one disputed registry row the licensed index does not carry. A model also does not enter a reconstructed reading before Epoch dates it, so nothing from 2026 can be the cheapest thing clearing a 2024 bar.
A seller's own dated price wins over the index's copy of it in the reconstruction, exactly as it does everywhere else — but no first-party row we hold can currently be applied to a past date, because a price can only be dated by the day the seller says it took effect and all 456 first-party rows carry no such date. The day we first read a page is when we found out, not when the price changed, and back-projecting it would put today's prices onto two years of history. So the reconstruction is the licensed index throughout, every row records that it applied no first-party number, and the rule will start doing work the moment a seller publishes an effective date.
Blended 3:1 input:output, the field's convention, and held fixed across the whole series. The ratio is stored with every price we record, so changing the convention later cannot silently rewrite this history.
Free and batch tiers are excluded. A rate-limited promotional tier is not a price, and one $0 entry would sink every capability tier to nothing. Batch pricing is roughly half, in exchange for latency measured in hours — a different product, and mixing it in would show a cost halving nobody experiences.
These are the sellers' own published prices. Six labs publish a rate card we can read directly — OpenAI, Anthropic, xAI, Google, Z.ai and Voyage. Where a seller publishes no page we may read, the number comes from a source that can be held to account for it instead: DeepSeek from the CC-BY-4.0 AI Price Index, which carries a dated, confidence-marked record for every rate, and Kimi from two independent MIT registries published only where they agree with each other. The cheapest row below names which seller it came from and, where it was not the seller's own page, which kind of source it was.
How much of this is a seller's own number: 350 read from the seller’s own page · 88 cross-checked between two registries · 63 from the licensed index · 54 from a single registry, marked * · 4 where two registries disagree, marked *. The marked ones are published rather than withheld, and are the best available numbers rather than confirmed ones.
Capability scores are Epoch AI's Capabilities Index, and a model only enters this instrument if it appears in both, matched on name with no fuzzy matching: 118 scored models currently carry a price, and 50 of them clear the tier.
An earlier version of this curve used an aggregator's route price. It has been replaced rather than adjusted. A route price is the price of whichever seller the router happens to use, so it moves for reasons that are nothing to do with a seller's decision — measured on six days of it, no model whose price moved was sold by a single seller and a third of the movements returned to a price the model already held. A rate card does not behave that way, so this is the better input as well as the one we may publish.
Capability data: Epoch AI, “AI Benchmarking Hub”, used under CC-BY 4.0. Prices: the sellers' own published pricing pages; the AI Price Index (CC-BY-4.0); and LiteLLM and models.dev (both MIT) where two registries agree.