Token Economics: From Resource Metering to Sustainable Incentive Mechanisms

1. What Token Economics Actually Studies

“Token economics” is not equivalent to “token economics exists whenever tokens are used.”

In artificial intelligence, a token is usually the basic unit of measurement used when a model processes text, code, image descriptions, or multimodal information. User input is split into tokens, and model-generated content also produces tokens. Service providers typically bill based on input tokens, output tokens, context length, number of calls, or other resource consumption.

However, moving from a resource metering unit to “economics” requires more conditions. At least the following elements should coexist:

1. Scarce resources: The compute resources, GPU memory, bandwidth, inference time, or account quotas corresponding to tokens are not unlimited.
2. Participants: This includes model providers, aggregation service providers, channel providers, developers, and end users.
3. Value exchange: Users pay fees in exchange for a certain quantity or quality of model invocation capacity.
4. Costs and benefits: Service providers need to account for upstream procurement, compute resources, payment channels, operations, and risk costs.
5. Allocation and incentive mechanisms: How different users obtain quotas, how providers control usage, and how participants benefit.
6. Risk constraints: This includes account bans, abusive calls, refunds, price fluctuations, service interruptions, and data security.

Therefore, token economics essentially studies the measurement, pricing, allocation, trading, and risk management system built around limited compute resources.

2. Tokens Are More Than Character Counts; They Are a Proxy for Compute Resources

Many people understand tokens simply as “word counts,” but this is inaccurate. A token is a discretization unit used by a model when processing information internally. Different languages, symbols, code structures, and text formats may consume different numbers of tokens.

For example, for content of the same length:

• Chinese, English, and numbers may be segmented differently;
• indentation, punctuation, and variable names in code affect token counts;
• repeatedly sending context continuously increases input costs;
• longer conversation histories expand context window usage;
• longer model outputs usually mean higher inference time and resource consumption.

Therefore, a token is closer to a proxy indicator of “compute resource usage” rather than a pure text-length metric.

In actual operations, service providers generally break down costs into several categories:

1. Input Costs

Prompts submitted by users, historical conversations, system instructions, code files, and tool call results may all count as input tokens. For coding applications, automatically attached project files, logs, and context are often the main source of input costs.

2. Output Costs

Answers, code, explanations, structured data, and tool-call instructions generated by the model produce output tokens. Output is usually harder to predict than input because it depends on task complexity, model behavior, and whether the user requests a detailed answer.

3. Context Costs

In multi-turn conversations, the system may repeatedly send historical messages. Even if the user enters only one new question, the full context may be recalculated. Therefore, the true consumption of an interaction cannot be judged only by the length of the newly added question.

4. Inference and Concurrency Costs

The same number of tokens may lead to different infrastructure costs under different concurrency levels, response-speed requirements, and model scales. During peak periods, service providers also need to reserve capacity to avoid queuing, timeouts, or service degradation.

3. Basic Commercial Structure of Model API Relay Services

In some regions, users may be unable to directly use certain international model services because of payment methods, access restrictions, account verification, or network environment. As a result, API forwarding and relay models have emerged in the market.

The basic chain is usually as follows:

End user
   ↓
Relay service entry
   ↓
Quota validation, identity authentication, traffic control
   ↓
Upstream model API
   ↓
Result return and usage logging

A relay service does not necessarily own the model itself. Instead, it purchases invocation capacity through upstream APIs and provides services in a way that is more suitable for local users. It may offer a unified API, balance management, concurrency limits, log statistics, failover, and multi-model routing.

The value of such services is mainly reflected in the following aspects:

• simplifying payment and account management;
• integrating multiple upstream models into a unified entry point;
• providing users with more familiar top-up and billing methods;
• lowering the barrier to use through caching, rate limiting, and routing;
• providing developers with a unified invocation protocol;
• matching price and performance across different models.

However, relay services also bear additional costs, including API procurement costs, server costs, payment processing fees, customer support, risk review, refund losses, and uncertainty caused by changes in upstream policies.

4. Three Common Profit Models

1. Quota Resale and Price Spread

The most direct approach is to purchase upstream resources by token or invocation quota and then sell them to users at a higher unit price.

Suppose a service provider purchases a batch of model invocation quotas. The unit cost is:

Upstream cost = input token cost + output token cost + allocated infrastructure cost

When charging users, the provider also needs to include:

Sales price = upstream cost
            + payment and channel costs
            + operations cost
            + risk reserve
            + target profit

If a service provider simply marks up the upstream price, it may not cover refunds, abnormal calls, and customer service costs. Therefore, reasonable pricing should consider not only the token unit price but also the actual usage distribution of users.

2. Shared Resources and Package Plans

Some services package a set of account quotas, concurrency capacity, or invocation resources into plans, with multiple users sharing a resource pool.

The advantage of the shared model is improved resource utilization. Because different users’ usage peaks usually do not fully overlap, service providers can oversell based on statistical patterns. For example, although the total theoretical quota purchased by all users is large, the proportion of simultaneous calls may be lower.

However, shared resources also carry clear risks:

• one user consumes a large amount of resources and affects others;
• multiple people sharing the same account may trigger abnormal login or rate risk controls;
• the resource pool lacks isolation, making service quality unstable;
• users cannot easily tell whether their quota is being occupied by others;
• when a ban occurs, a group of users may be affected at the same time.

Therefore, a mature shared model should set per-user limits, concurrency caps, rate limits, and anomaly detection rather than simply expanding sales volume.

3. Refunds and Account Rotation

Some service providers take advantage of upstream refund policies by applying for refunds after account anomalies or service interruptions and then purchasing resources again. This model can temporarily reduce losses, but it cannot be regarded as a stable business model.

The purpose of refund mechanisms is usually to protect consumers, not to allow service providers to repeatedly rotate accounts. If a service provider relies on refunds after bans to cover operating losses, its business model may have the following problems:

• customer acquisition price is lower than the true service cost;
• account and compliance risks are underestimated;
• the business relies excessively on upstream rules;
• no long-term, stable resource procurement channel has been established;
• low-price competition is maintained through high-risk methods.

From the user’s perspective, services that rely on refund flows usually carry high uncertainty. The service may stop suddenly, balances may be frozen, and historical records may be unrecoverable. Therefore, when choosing such services, users should not compare only surface prices.

5. Why “Unlimited Use at Low Price” Is Usually Unsustainable

The cost of model services does not disappear because the word “unlimited” appears in a plan name. So-called unlimited plans are usually capacity designs based on statistics of user behavior.

Service providers generally estimate:

• average daily calls per user;
• input and output tokens per request;
• peak concurrency ratio;
• proportion of heavy users;
• proportion of abnormal or automated calls;
• refund and bad debt ratios.

If most users have low usage, the costs of a few heavy users can be covered by overall plan revenue. But once the proportion of heavy users rises rapidly, the resource pool becomes unbalanced. At this point, service providers usually can only take the following measures:

1. limit concurrency;
2. reduce response speed;
3. limit context length;
4. charge extra for users who exceed limits;
5. suspend high-consumption models;
6. adjust plan prices;
7. manually review abnormal calls.

Therefore, “unlimited” as seen by users more accurately means “no per-call charge within a reasonable range of use.”

6. Key Metrics in Token Economics

To analyze whether a token service is healthy, the following metrics should be observed closely.

1. Average Revenue Per User

ARPU = total revenue ÷ number of paying users

This metric can reflect whether plan pricing is reasonable, but it cannot be used alone to judge profitability.

2. Average Cost Per User

Average cost = total token cost ÷ number of users

A more accurate calculation should also include server, customer service, payment, and risk-control costs.

3. Gross Margin

Gross margin = (revenue - direct resource cost) ÷ revenue

If the gross margin is too low, the business becomes highly vulnerable to upstream price increases, exchange rate changes, or increased refunds.

4. Peak Concurrency

Average call volume does not represent system pressure. Service providers also need to monitor the maximum number of concurrent requests per unit time, because peak values determine capacity procurement and system design.

5. User Segmentation

Users can usually be divided into light, medium, and heavy users. Resource consumption may differ greatly among different users. A reasonable plan should avoid allowing a small number of heavy users to completely crowd out resources.

6. Refund Rate and Ban Rate

Refund rates, account invalidation rates, and the number of service interruptions can reflect the degree to which a business depends on external rules. When these metrics continue to rise, it indicates that the business model needs to be redesigned.

7. How Users Can Reduce Token Costs

For developers and enterprise users, reducing costs should not rely only on finding a lower unit price. More importantly, they should reduce ineffective tokens.

The following methods can be used:

1. Compress Repeated Context

Do not resend unchanged background information in every request. Long-term rules can be organized into concise system instructions, and large documents can be split into snippets retrieved on demand.

2. Control Output Length

Explicitly ask the model to return conclusions, differences, or structured results, avoiding unnecessary repeated explanations. For program calls, require fixed fields to reduce redundant text.

3. Use Model Tiering

Simple classification, format conversion, and summarization tasks do not necessarily require high-cost models. Complex reasoning, code review, and critical decisions can then use more capable models.

4. Establish Usage Monitoring

Record the input tokens, output tokens, response time, and error type of each request. When abnormal growth appears in a certain feature, identify the cause promptly.

5. Set Budgets and Circuit Breakers

Set daily or monthly budgets for teams, projects, and users. When consumption exceeds a threshold, automatically degrade, pause, or require manual confirmation to prevent automated programs from causing large bills.

8. Enterprises Should Focus More on Compliance and Data Security

Although relay services lower the access barrier, they also add data paths.

Enterprises need to clarify:

• whether user input passes through third-party servers;
• whether request content is logged;
• how long logs are retained;
• whether requests contain keys, source code, or personal information;
• whether the service provider offers permission isolation;
• whether usage and bills can be exported after a failure;
• whether data deletion and audit trails are supported.

For sensitive code, business documents, and personal information, users should not send data directly to services with unclear data-processing rules simply because the price is lower. When necessary, data should be redacted, classified, and access-controlled, and auditable and manageable enterprise-level access methods should be preferred.

9. Conclusion: The Core of Token Economics Is “Resources, Prices, and Rules”

The fact that tokens become a resource does not automatically mean that token economics has been formed. Only when tokens connect compute resources, supply and demand relationships, transaction prices, participant interests, and risk constraints does a complete economic system exist.

For model relay and aggregation services, what truly determines sustainability is not short-term low prices but the following capabilities:

• accurately accounting for token and infrastructure costs;
• improving resource utilization through rate limiting and tiering;
• establishing transparent and stable billing rules;
• controlling account, payment, and data-security risks;
• maintaining service continuity when upstream prices or policies change.

For users, the value of understanding token economics is that they no longer compare only plan prices, but can judge the resource source, service stability, data risk, and long-term sustainability behind the price. For enterprises, this understanding can also help teams establish a complete model invocation budget, cost monitoring, and technology selection system.