LLM Subscription Plan Billing Models and Token Evaluation Methods

As large model capabilities continue to evolve, cloud providers and AI service vendors have widely introduced subscription-based or credit-based plans. Compared with traditional pay-as-you-go pricing, which states an explicit price per million tokens, subscription plans often provide only a unified “quota” or “request cap.” This creates a core pain point: how much real token capacity does each plan actually offer? Because LLM billing involves four different types of tokens, and providers apply vastly different weighting rules to each type, comparing plan prices directly is meaningless. Developers and enterprise users need a scientific, objective quantitative evaluation method to see through this billing black box.

1. Penetration Testing: Isolated Measurement of Token Consumption

The most effective way to objectively evaluate the real value of a plan is to reverse-engineer its internal credit deduction system. A classic reverse-engineering experiment design has been proposed by a technical laboratory in the industry. Its core idea is to decompose complex mixed-token consumption and use “isolation tests” to observe, one variable at a time, how a specific token type consumes the total quota.

LLM interactions typically involve the following four core token types, whose deduction weights in credit systems differ sharply:

1. Fresh Input: newly added prompt tokens in each request that hit no cache. Test method: dynamically generate and replace different context content in each request to ensure the cache mechanism is completely avoided.
2. Cache Write: the process of writing new context into the server-side cache. Test method: construct a brand-new long context to force the system to perform a cache write.
3. Cache Read: reusing cached context. Test method: pre-write a large fixed context, then carry that context repeatedly across multiple requests while modifying only a very small amount of instructions at the end.
4. Output: the response content generated by the model. Test method: design prompts that require the model to output as long a coherent text as possible while keeping input tokens minimal.

Through these four isolation tests, we can separately derive the consumption rate of each token type against the subscription quota.

2. Eliminating Error: Discrete Quota Monitoring and Multi-Interval Averaging

In actual testing, the biggest obstacle developers face is data precision. The remaining quota percentage returned by most providers’ web consoles or APIs is often displayed only as an integer (for example, changing from 17% to 18%). This discrete jump makes the consumption of a single request difficult to measure precisely.

To solve this problem, the test design introduces a “multi-interval averaging method”:

1. Continuously send a large number of test requests of the same type.
2. Monitor and record the points at which the quota jumps (for example, 17% to 18%, 18% to 19%).
3. Count the total number of tokens sent or received between these two jump points.
4. Repeatedly record multiple complete 1% jump intervals, and average them to smooth out and offset system latency or statistical error.

Ultimately, through this rigorous series of stress tests, we can reach a relatively precise conclusion: “for every 1 million tokens of a given type consumed, what percentage of the plan’s total quota is deducted.”

3. Close to Real Scenarios: Weighted Reverse Inference Based on Business Load

Once the individual deduction rates for the four token types are obtained, how can they be translated into an evaluation of the plan’s overall value? This requires combining them with the real distribution of business load.

Take an intelligent coding tool as an example. Its typical workload characteristics are as follows:

• Cache Read accounts for a very high share (usually 60%–80%): because a large project codebase context must be passed in repeatedly.
• Fresh Input accounts for a medium share: the user’s newly added modification instructions.
• Output accounts for a medium share: the generated code snippets.
• Cache Write accounts for a very low share: it occurs only when the project is first loaded.

Suppose a flagship subscription plan is measured to have very high deduction rates for various token types, especially minimal discounts for Cache Read. Then even if the plan performs reasonably well on Fresh Input, its “equivalent token throughput” when handling intelligent coding scenarios will be greatly reduced. Conversely, if another plan offers a large preferential weight for Cache Read, its real business capacity will rise exponentially.

Developers can use the measured deduction rates as weighting coefficients, combine them with the token distribution shares of their own business, and calculate the “percentage of subscription quota consumed per 1 million mixed business tokens.” Only through this composite metric can they stay clear-headed amid marketing rhetoric such as “priced on par with flagship models” and make selection decisions that truly match ROI. Below is a simple weighted calculation example:

# Pseudocode: calculator for the real capacity of an LLM subscription plan
# Deduction rates for each token type derived from isolation tests
# (unit: percentage of subscription quota consumed per million tokens)
rates = {
    "fresh_input": 1.2,
    "cache_write": 1.5,
    "cache_read": 0.15,
    "output": 3.0
}
# Define the token distribution shares of the target business scenario
# (such as an intelligent coding tool); percentages must sum to 100
workload_profile = {
    "fresh_input": 15,
    "cache_write": 5,
    "cache_read": 70,
    "output": 10
}
total_cost_rate = 0
for token_type, percentage in workload_profile.items():
    # Calculate the percentage of quota consumed per 1 million mixed tokens
    total_cost_rate += rates[token_type] * (percentage / 100)
print(f"Quota consumed per 1 million mixed business tokens: {total_cost_rate:.2f}%")

4. Automated Evaluation Practices on General Cloud Architectures

For enterprise users, performing the tests above manually is time-consuming and error-prone. To establish a routine evaluation mechanism, a general cloud-native architecture can be used to build an automated evaluation pipeline.

1. Stress-test node scheduling: use a container platform to deploy multiple concurrent test instances, writing separate test scripts for the four token scenarios to achieve concurrent isolation testing.
2. Request orchestration and throttling: introduce a message queue to buffer and serialize API requests, avoiding triggering provider rate limits while ensuring stable request frequency.
3. Logging and parsing: output the token usage and response time of each request to a unified logging service. At the same time, use scheduled jobs to scrape quota changes from the console page and record jump intervals.
4. Data analysis and monitoring: configure dashboards in a monitoring and alerting platform to aggregate and calculate the average deduction rate of each token type in real time, and generate business-scenario fit reports.

This automated testing architecture, built on general cloud components, is not only suitable for evaluating current LLM APIs, but can also quickly complete cost and performance benchmarking when more emerging model services are integrated in the future.

5. Conclusion

Pricing strategies for LLM subscription plans are becoming increasingly complex. Relying solely on vendor-published nominal data or simple price comparisons can no longer guide serious enterprise technology selection. By using isolation tests to precisely measure the consumption rate of each token type, and then performing weighted reverse inference based on real business load distribution, developers can truly see through the economics behind the “hidden credit system.” This quantitative evaluation mindset will become an indispensable core capability for controlling costs and optimizing architecture during AI application deployment.