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What are LLM Tokens Worth?

An initial dive into how LLM tokens translate into the economic impact for humans and business

LLMs aren’t just generating text — they’re generating economic output, one token at a time. To understand their actual impact, we need to look beyond performance benchmarks and explore what those tokens are worth in the real world.

We can measure the output of large language models (LLMs) in terms of throughput — number of tokens generated per unit of time. In traditional performance benchmarking, this is where we stop — with raw performance. However, to truly understand the real-world value of language models, we have to go a step further and analyze the economic impact of these assets on the businesses and end-users deploying and consuming AI services. This article unpacks the economics of LLM tokens [1].

The Mechanics of Token-Based Pricing

LLMs break down text into tokens, where a token might be a word fragment or punctuation. Current API pricing models for premium models such as GPT-4 are typically around $0.03–$0.06 per 1,000 tokens. This model allows for a near-microtransaction approach to pricing — every generated token is a quantifiable unit of cost.

Some companies might seek to directly integrate an API into a specific component of their application — for example, building an AI customer service agent that pipes all queries to the OpenAI API. Others will seek to have bespoke deployments of AI models in their own data centers or leverage the hardware from a cloud service provider (CSP). The varying models for deploying these solutions can lead to significant differences in the price they assign to the tokens the business serves.

The price a business sets for a token ultimately boils down to the equation in Figure 1.

.Figure 1: Token cost equation graphic. This equation was developed by the author as a common sense way to approximate what an AI service provider might charge per token given the technical/business overhead and any premiums they define for their service. — Image by author

Let’s break this down:

• Cost to Generate the Token: This includes everything required to serve the token — hardware, infrastructure, engineering, software licensing, and general operational overhead.
• Premium: The premium reflects the differentiated value the token provides. A token generated by a closed-source, proprietary model solving a high-value use case can command a much higher premium than one produced by a basic open-source model behind a simple API endpoint. This premium can also account for the service’s reliability and user/data safety.

Justifying the Cost of a Token

Regardless of the use case, the cost per token has to be justified by something. For simple API-based models, this is relatively straightforward. However, things get trickier when you try to assess the value of a token in more complex applications or business models.

Take Meta, for example (Figure 2). What’s a single token worth in the context of Meta AI integrated into Instagram? Since it’s offered as a freemium service, it’s likely a loss leader — something that brings intrinsic value tied to Instagram’s core revenue engine: ad revenue. If Meta AI makes Instagram a more engaging platform, it could drive more users to spend more time there, ultimately leading to greater ad consumption. In that case, the value of a token might be understood as a function of increased user engagement and the resulting ad impressions.

Figure 2: Diagram of Meta AI token value. This concept was developed by the author in an effort to provide an example of how token value could be perceived by a business like Meta. However, it is not based on any statements made by Meta about the economics of their Meta AI platform. — Image by Author

Alternatively, if Meta AI replaces other methods Meta previously used to attract users — like manual content curation, influencer partnerships, or promotional campaigns — then each token could represent cost savings. In this view, tokens are valuable not just because they drive revenue, but because they reduce the need for other expensive acquisition tactics.

Either way, the value of a token can be seen as either revenue gained through augmentation, costs saved through replacement, or both. There are other AI monetization models like subscription-based, SaaS Bundling, Outcome-based, and freemium, but those are out of the scope of this article.

Role of Token Economics in Human Worker Augmentation

The following section calculates the cost per token for a popular model like Llama 3.1 70B Instruct. First, we consider the average throughput of common AI accelerators — MI300X and H100 processors — as reported on their respective public pages. Then, we use the public hourly pricing from Oracle Compute Cloud to determine the cost per token.

At the time of writing, using a configuration with 2048 input tokens and 2048 output tokens, the GPUs achieved an average output token throughput of 8,500 tokens/sec across both processors using 8-bit quantized versions of the 70B model deployed using tensor parallelism across the 8 GPUs on the node. The average cost of an 8‑GPU node (average of H100 and MI300X on-demand cost) on Oracle Cloud is approximately $64 per hour. Converting this throughput to an hourly rate gives about 30 million tokens per hour (8,500 tokens/s × 3600 s). We divide the hourly cost by the number of tokens produced and we get an approximate cost of $.000002 per token or $2.09 per million output tokens.

We will add a 20% buffer for maintenance/troubleshooting-related downtime for the analysis below — bringing the tokens per hour down to 24 million. I’ll also factor in the cost of a small engineering team (3 engineers @ $100/hour each), which will be the dominant cost in the overall token economics. This brings the total cost per token to $0.000014 or $14.87 per million output tokens.

Note: These values can change significantly with new optimizations, pricing adjustments, model sizes, and architecture variations.

Looking at Real World Use-Cases

Across various industries, AI-generated tokens translate to dramatic value when used to augment or partially replace human workers. Using the token economics of the analysis above, we apply them to three common use cases in healthcare, finance, and customer service. Table 1 provides a technical breakdown.

Table 1: Detailed analysis of AI cost and human cost for common tasks across healthcare, finance, and customer service. The key figures of merit are the implied value of tokens through savings, which helps address the core topic of this article: “What are tokens worth” — Image by author

To perform the evaluation that resulted in the metrics in the table above, we made a few key assumptions and mined data from public sources:

1. Estimate Task Size (in Words): For each use case, we researched typical examples to determine how many words a human would produce or process to complete the task.
2. Convert Words to Tokens: Using the standard English conversion rate of ~0.75 words per token, we translated each task into a token count suitable for LLM completion.
3. Determine AI Token Cost: Based on earlier analysis — including cloud compute costs and engineering overhead — we calculated the cost of generating those tokens.
4. Estimate Human Labor Cost: We used average hourly wages (sourced from the U.S. Bureau of Labor Statistics) for relevant professions to estimate the human cost per task.
5. Scale to 100 Tasks: To demonstrate impact at scale, we projected costs for 100 instances of each task.

From these inputs, we calculated potential savings by subtracting the total AI cost from the total human labor cost. The result gives a clear picture of how LLMs can reduce operational expenses for repetitive or routine workflows.

Implied Token Value (ITV)

A central metric in evaluating the economic impact of AI tokens is the Implied Value of Tokens Through Savings. This figure encapsulates the core inquiry: “What is the token worth?”. It is calculated using the equation in Figure 3.

Figure 3: Equation for calculating the implied token value through savings. This equation was developed by the author as a method for calculating individual token values for specific tasks. It does not consider factors like degradation in token value caused by hallucinations or erroneous outputs from LLMs. — Image by Author

In this context, the token’s value is inherently tied to its capacity to reduce costs by augmenting or replacing human labor.

Example of Financial Report Analysis

Consider the scenario of financial report analysis (Figure 4). Here, each token contributes an estimated $0.09 in net value to the business after accounting for associated costs. This value arises from the token’s ability to either replace or enhance human efforts in this task. Assuming the cost per token equates to the expense of employing an AI worker — approximately $0.000014 — we derive a wage-to-benefit ratio of 6,700x. This ratio starkly contrasts with traditional human labor metrics, where businesses typically aim for a revenue-to-wage ratio between 2.5x and 3x. Such human labor ratios are designed to cover wages, overhead, and profit margins.

Figure 4: Financial report analysis example graphic. This figure further helps illustrate part of the analysis in Table 1. Note again, that these examples are approximations and don’t consider all possible factors involved in delivering a final product for this task. — Image by Author

While it’s essential to acknowledge potential caveats — such as scalability challenges and the need for additional resources to serve a broader user base — the substantial 6,700x ratio offers considerable leeway. It provides ample room to accommodate increased costs while still maintaining significant benefits from integrating AI into this use case.

This analysis highlights a significant efficiency opportunity — not to replace humans but to offload routine work, freeing people for higher-value tasks.

Note: The token generation costs used in this analysis are based solely on cloud infrastructure compute costs and a small engineering team. Additional factors — such as power, maintenance, business development, marketing, or custom deployments — may affect the total cost in other settings.

Conclusion

As AI adoption accelerates, businesses must evolve how they think about cost, performance, and value. Large Language Model (LLM) tokens offer a unique unit of measure — quantifiable, monetizable, and comparable across a range of tasks.

From reducing the time it takes to triage patients or summarize financial reports, to handling customer support inquiries at scale, tokens represent potential — potential to cut costs, scale operations, and shift human labor to higher-value tasks.

While real-world deployment requires accounting for infrastructure, personnel, and variability in demand, the core insight remains powerful: each token generated by an LLM carries tangible business value. And as models get cheaper, faster, and more specialized, that value will only increase.

End notes:

[1] Please note that this article does not advocate for AI tools to completely replace human labor — which could have unforeseen, profound, and far-reaching impacts on the economic stability of households and society’s mental health.

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Disclaimer: The views expressed in this article are my own and do not reflect those of my employer. References are available upon request.