New Research Papers Question ‘Token’ Pricing for AI Chats

New analysis reveals that the way in which AI providers invoice by tokens hides the actual price from customers. Suppliers can quietly inflate fees by fudging token counts or slipping in hidden steps. Some techniques run additional processes that don’t have an effect on the output however nonetheless present up on the invoice. Auditing instruments have been proposed, however with out actual oversight, customers are left paying for greater than they understand.

In practically all circumstances, what we as shoppers pay for AI-powered chat interfaces, reminiscent of ChatGPT-4o, is at the moment measured in tokens: invisible items of textual content that go unnoticed throughout use, but are counted with actual precision for billing functions; and although every trade is priced by the variety of tokens processed, the consumer has no direct solution to affirm the depend.

Regardless of our (at greatest) imperfect understanding of what we get for our bought ‘token’ unit, token-based billing has turn out to be the usual method throughout suppliers, resting on what might show to be a precarious assumption of belief.

Token Phrases

A token isn’t fairly the identical as a phrase, although it usually performs the same function, and most suppliers use the time period ‘token’ to explain small items of textual content reminiscent of phrases, punctuation marks, or word-fragments. The phrase ‘unbelievable’, for instance, could be counted as a single token by one system, whereas one other may break up it into un, believ and ready, with every bit rising the associated fee.

This technique applies to each the textual content a consumer inputs and the mannequin’s reply, with the worth primarily based on the overall variety of these items.

The issue lies in the truth that customers don’t get to see this course of. Most interfaces don’t present token counts whereas a dialog is going on, and the way in which tokens are calculated is tough to breed. Even when a depend is proven after a reply, it’s too late to inform whether or not it was honest, making a mismatch between what the consumer sees and what they’re paying for.

Latest analysis factors to deeper issues: one research reveals how suppliers can overcharge with out ever breaking the principles, just by inflating token counts in ways in which the consumer can not see; one other reveals the mismatch between what interfaces show and what’s truly billed, leaving customers with the phantasm of effectivity the place there could also be none; and a 3rd exposes how fashions routinely generate inner reasoning steps which are by no means proven to the consumer, but nonetheless seem on the bill.

The findings depict a system that appears exact, with actual numbers implying readability, but whose underlying logic stays hidden. Whether or not that is by design, or a structural flaw, the consequence is identical: customers pay for greater than they will see, and sometimes greater than they anticipate.

Cheaper by the Dozen?

Within the first of those papers – titled Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives, from 4 researchers on the Max Planck Institute for Software program Methods – the authors argue that the dangers of token-based billing lengthen past opacity, pointing to a built-in incentive for suppliers to inflate token counts:

‘The core of the issue lies in the truth that the tokenization of a string isn’t distinctive. For instance, take into account that the consumer submits the immediate “The place does the following NeurIPS happen?” to the supplier, the supplier feeds it into an LLM, and the mannequin generates the output “|San| Diego|” consisting of two tokens.

‘Because the consumer is oblivious to the generative course of, a self-serving supplier has the capability to misreport the tokenization of the output to the consumer with out even altering the underlying string. For example, the supplier may merely share the tokenization “|S|a|n| |D|i|e|g|o|” and overcharge the consumer for 9 tokens as an alternative of two!’

The paper presents a heuristic able to performing this sort of disingenuous calculation with out altering seen output, and with out violating plausibility underneath typical decoding settings. Examined on fashions from the LLaMA, Mistral and Gemma collection, utilizing actual prompts, the strategy achieves measurable overcharges with out showing anomalous:

To handle the issue, the researchers name for billing primarily based on character depend slightly than tokens, arguing that that is the one method that provides suppliers a motive to report utilization truthfully, and contending that if the aim is honest pricing, then tying price to seen characters, not hidden processes, is the one choice that stands as much as scrutiny. Character-based pricing, they argue, would take away the motive to misreport whereas additionally rewarding shorter, extra environment friendly outputs.

Right here there are a selection of additional issues, nonetheless (most often conceded by the authors). Firstly, the character-based scheme proposed introduces further enterprise logic that will favor the seller over the patron:

‘[A] supplier that by no means misreports has a transparent incentive to generate the shortest doable output token sequence, and enhance present tokenization algorithms reminiscent of BPE, in order that they compress the output token sequence as a lot as doable’

The optimistic motif right here is that the seller is thus inspired to provide concise and extra significant and useful output. In observe, there are clearly much less virtuous methods for a supplier to scale back text-count.

Secondly, it’s affordable to imagine, the authors state, that firms would seemingly require laws with a view to transit from the arcane token system to a clearer, text-based billing technique. Down the road, an rebel startup might determine to distinguish their product by launching it with this sort of pricing mannequin; however anybody with a very aggressive product (and working at a decrease scale than EEE class) is disincentivized to do that.

Lastly, larcenous algorithms such because the authors suggest would include their very own computational price; if the expense of calculating an ‘upcharge’ exceeded the potential revenue profit, the scheme would clearly don’t have any advantage. Nonetheless the researchers emphasize that their proposed algorithm is efficient and economical.

The authors present the code for his or her theories at GitHub.

The Change

The second paper – titled Invisible Tokens, Seen Payments: The Pressing Must Audit Hidden Operations in Opaque LLM Providers, from researchers at  the College of Maryland and Berkeley – argues that misaligned incentives in business language mannequin APIs will not be restricted to token splitting, however lengthen to complete lessons of hidden operations.

These embody inner mannequin calls, speculative reasoning, software utilization, and multi-agent interactions – all of which can be billed to the consumer with out visibility or recourse.

Not like standard billing, the place the amount and high quality of providers are verifiable, the authors contend that at present’s LLM platforms function underneath structural opacity: customers are charged primarily based on reported token and API utilization, however don’t have any means to verify that these metrics replicate actual or needed work.

The paper identifies two key types of manipulation: amount inflation, the place the variety of tokens or calls is elevated with out consumer profit; and high quality downgrade, the place lower-performing fashions or instruments are silently used instead of premium elements:

‘In reasoning LLM APIs, suppliers usually preserve a number of variants of the identical mannequin household, differing in capability, coaching knowledge, or optimization technique (e.g., ChatGPT o1, o3). Mannequin downgrade refers back to the silent substitution of lower-cost fashions, which can introduce misalignment between anticipated and precise service high quality.

‘For instance, a immediate could also be processed by a smaller-sized mannequin, whereas billing stays unchanged. This observe is tough for customers to detect, as the ultimate reply should still seem believable for a lot of duties.’

The paper paperwork cases the place greater than ninety p.c of billed tokens have been by no means proven to customers, with inner reasoning inflating token utilization by an element higher than twenty. Justified or not, the opacity of those steps denies customers any foundation for evaluating their relevance or legitimacy.

In agentic techniques, the opacity will increase, as inner exchanges between AI brokers can every incur fees with out meaningfully affecting the ultimate output:

‘Past inner reasoning, brokers talk by exchanging prompts, summaries, and planning directions. Every agent each interprets inputs from others and generates outputs to information the workflow. These inter-agent messages might devour substantial tokens, which are sometimes in a roundabout way seen to finish customers.

‘All tokens consumed throughout agent coordination, together with generated prompts, responses, and tool-related directions, are usually not surfaced to the consumer. When the brokers themselves use reasoning fashions, billing turns into much more opaque’

To confront these points, the authors suggest a layered auditing framework involving cryptographic proofs of inner exercise, verifiable markers of mannequin or software identification, and impartial oversight. The underlying concern, nonetheless, is structural: present LLM billing schemes rely on a persistent asymmetry of knowledge, leaving customers uncovered to prices that they can not confirm or break down.

Counting the Invisible

The ultimate paper, from researchers on the College of Maryland, re-frames the billing downside not as a query of misuse or misreporting, however of construction. The paper – titled CoIn: Counting the Invisible Reasoning Tokens in Industrial Opaque LLM APIs, and from ten researchers on the College of Maryland – observes that the majority business LLM providers now disguise the intermediate reasoning that contributes to a mannequin’s last reply, but nonetheless cost for these tokens.

The paper asserts that this creates an unobservable billing floor the place complete sequences could be fabricated, injected, or inflated with out detection*:

‘[This] invisibility permits suppliers to misreport token counts or inject low-cost, fabricated reasoning tokens to artificially inflate token counts. We confer with this observe as token depend inflation.

‘For example, a single high-efficiency ARC-AGI run by OpenAI’s o3 mannequin consumed 111 million tokens, costing $66,772.3 Given this scale, even small manipulations can result in substantial monetary affect.

‘Such info asymmetry permits AI firms to considerably overcharge customers, thereby undermining their pursuits.’

To counter this asymmetry, the authors suggest CoIn, a third-party auditing system designed to confirm hidden tokens with out revealing their contents, and which makes use of hashed fingerprints and semantic checks to identify indicators of inflation.

One part verifies token counts cryptographically utilizing a Merkle tree; the opposite assesses the relevance of the hidden content material by evaluating it to the reply embedding. This permits auditors to detect padding or irrelevance – indicators that tokens are being inserted merely to hike up the invoice.

When deployed in exams, CoIn achieved a detection success fee of practically 95% for some types of inflation, with minimal publicity of the underlying knowledge. Although the system nonetheless will depend on voluntary cooperation from suppliers, and has restricted decision in edge circumstances, its broader level is unmistakable: the very structure of present LLM billing assumes an honesty that can not be verified.

Conclusion

Moreover the benefit of gaining pre-payment from customers, a scrip-based foreign money (such because the ‘buzz’ system at CivitAI) helps to summary customers away from the true worth of the foreign money they’re spending, or the commodity they’re shopping for. Likewise, giving a vendor leeway to outline their very own items of measurement additional leaves the patron at the hours of darkness about what they’re truly spending, by way of actual cash.

Like the dearth of clocks in Las Vegas, measures of this type are sometimes aimed toward making the patron reckless or detached to price.

The scarcely-understood token, which could be consumed and outlined in so some ways, is probably not an acceptable unit of measurement for LLM consumption – not least as a result of it could price many occasions extra tokens to calculate a poorer LLM lead to a non-English language, in comparison with an English-based session.

Nonetheless, character-based output, as instructed by the Max Planck researchers, would seemingly favor extra concise languages and penalize naturally verbose languages. Since visible indications reminiscent of a depreciating token counter would most likely make us a bit of extra spendthrift in our LLM periods, it appears unlikely that such helpful GUI additions are coming anytime quickly – a minimum of with out legislative motion.

* Authors’ emphases. My conversion of the authors’ inline citations to hyperlinks.

First printed Thursday, Might 29, 2025