Why LLM Subscription Credits Are Running Out Faster

1. The Surface Problem: Better Models, Faster-Depleting Credits

After the latest round of large language model upgrades, many subscription users have encountered a puzzling contradiction: the new models perform noticeably better at code generation, long-running tasks, and complex reasoning, yet plan credits run out much faster than before. Some users report that with the same workload, the previous version would last through an entire billing cycle, while the new version approaches its usage limit after only a handful of tasks.

This shift is not simply the result of price increases. Looking at public pricing, the per-token cost for some models has actually gone down. For example, input pricing dropped from 20 yuan per million tokens to 13 yuan, and output pricing fell from 100 yuan to 68 yuan — a reduction of roughly one-third. But the user-perceived cost has not decreased, because the total number of tokens consumed per task has multiplied, offsetting the benefit of lower unit prices.

This raises a key question: does improved model capability inevitably lead to rapidly expanding resource consumption? If product design does not simultaneously optimize call chains, output length, and task scheduling, a “stronger” model may actually create a higher barrier to use.

2. Why Token Consumption Is Increasing

2.1 Task Decomposition and Subtask Expansion

Complex tasks are rarely completed with a single model call. To improve completion quality on long-running tasks, systems often break down objectives internally — for example, performing requirement analysis, context planning, and intermediate-step generation before executing the main task. These intermediate steps may involve multiple subtasks working together, with each subtask generating its own token consumption.

Take code generation as an example. A user might simply ask for “a file upload module.” Internally, however, the model may first analyze the project structure, then generate an interface design, output the code snippets, and finally add test cases and documentation. The user only sees the final result, but the bill already includes several rounds of hidden calls.

This “invisible consumption” is the primary reason subscription users perceive their credits draining quickly. In newer models especially, systems may enable more subtasks and verification steps by default to improve task completion rates and stability, further amplifying total token volume.

2.2 Longer Outputs and Redundant Content

To demonstrate more complete reasoning and more polished expression, some models now tend to produce longer responses. Code generation may include not only the core implementation but also full comments, edge-case handling, sample usage, and even performance notes. Text generation may add more paragraphs, transitional sentences, and concluding sections.

This content is valuable for some users but redundant for others. For developers who just need a quick answer or a basic code snippet, the extra output does not improve actual efficiency — it directly consumes plan credits.

2.3 Longer Reasoning Chains

Improvements in complex reasoning often mean the model generates longer internal reasoning before producing a final answer. In math problems, multi-step logic questions, or architecture design tasks, the model may produce a large volume of intermediate derivation steps. Even if these steps are not fully shown to the user, they still count toward token consumption.

In addition, some systems add “self-verification” or “result review” steps to reduce hallucinations and improve answer reliability. These mechanisms call the model again to validate preliminary output, further increasing consumption. For users, the answers are indeed more accurate, but the resource cost per task rises accordingly.

3. The Mismatch Between Subscriptions and Real Model Costs

Subscription plans are designed to give users a predictable amount of model usage for a fixed price. But resource consumption in LLM products is not a fixed value — it depends heavily on task type, model version, and internal scheduling policies. When model behavior changes after an upgrade, users’ original usage estimates become invalid immediately.

From a product perspective, model providers need to balance “capability improvements” with “reasonable credit consumption.” If they focus only on technical benchmarks while ignoring overhead control in call chains, several problems can emerge:

• Token usage per task becomes unpredictable
• Subscription credits no longer match model capability
• User trust in the product declines
• High-value users shift to pay-as-you-go or self-hosted solutions

For heavy developers, their workflows are often already built around model calls. Once per-task consumption changes significantly, the entire project’s cost estimation, task queuing, and resource allocation are disrupted.

4. How Developers Can Assess Real Usage

Faced with invisible internal calls and output expansion, developers need a more granular usage monitoring system. The following four dimensions are a good starting point.

4.1 Average Token Consumption per Task

Track the average token consumption for different task types. For example, classify tasks into categories such as code generation, text summarization, information extraction, and conversational Q&A, then track input tokens, output tokens, and total tokens separately. After a model version upgrade, compare token changes for the same task category first, rather than looking only at unit price.

4.2 Effective Output Ratio

The proportion of generated output that is actually used is an important metric for consumption efficiency. If a code generation response returns 3,000 tokens but only 200 lines of code are actually used, the effective ratio may be less than one-third. For these tasks, prompts can be used to constrain the output range, asking the model to return only the necessary code and skip explanations and examples.

4.3 Hidden Call Count

If the platform provides call logs or debugging information, pay close attention to whether a single user request triggers multiple internal calls. Look for repeated intermediate states, redundant planning, or verification steps. Some platforms offer task-tracing panels that help developers identify which calls are necessary and which are over-engineered.

4.4 Credit Depletion Rate

Track credit consumption rate by hour or day. When the rate suddenly accelerates, do not simply attribute it to “using more.” Instead, check whether task types, model versions, or call parameters have changed. Establishing a baseline consumption rate helps catch anomalies early.

5. Practical Strategies to Reduce Token Consumption

Even without changing the model’s internal call chain, developers can significantly reduce token consumption by adjusting how they use the service. The following strategies have proven effective in practice.

5.1 Optimize Prompts and Compress Input

Many tasks do not require the full context. By streamlining prompts, removing irrelevant background, and using structured input, you can reduce input tokens. For example, in coding tasks, paste only the relevant function instead of the entire file. In text tasks, explicitly request: “Answer based on the following content only. Do not expand the background.”

Example comparison:

Original prompt:
Please read the following project background, then analyze the code.
Background: Our project is an e-commerce platform using a
microservices architecture, including order, product, and user
modules... (500 words)
Optimized prompt:
Analyze the order state transition logic in the following code.
Return only the conclusion: ... (120 words)

This change alone can save 20% to 40% of input tokens.

5.2 Constrain Output Length and Format

Explicitly instructing the model to output only the necessary parts can effectively reduce output tokens. For example:

• “Return only the code. Do not explain.”
• “Total length must not exceed 300 words.”
• “Return in JSON format with the following fields…”
• “Do not include examples in the output.”

Although models do not always strictly follow length constraints, sufficiently clear instructions usually shorten the output significantly.

5.3 Break Down Long Tasks to Avoid Regeneration

Long tasks tend to consume large numbers of tokens in intermediate steps. If a task contains multiple independent sub-goals, split it into several shorter calls. This controls the scale of each output and allows retrying only the failed segment when something goes wrong, instead of regenerating the entire result.

For example, when generating a full technical proposal, proceed step by step:

1. Generate the table of contents first
2. Generate each chapter separately
3. Do a final formatting pass

Although the total number of calls increases, the token scale of each call is controllable, and wasted output is reduced.

5.4 Build Local Caching and Result Reuse

For high-frequency, similar requests, a local caching mechanism can help. Identical questions, identical contexts, or identical code modules do not need to hit the model every time. Store historical results in a local database or object storage, and reuse them directly — or perform incremental completion — when a highly similar request appears.

This approach is especially effective for code generation, document processing, and test case generation. In team collaboration environments, a shared cache can prevent multiple people from repeatedly consuming credits for the same work.

5.5 Use Fallback Strategies and Model Routing

Not every task requires the most capable model. For simple classification, format conversion, or information extraction, a lighter model version — or even a rules engine or template engine — may be sufficient for preprocessing. Reserve the high-capability model for tasks that genuinely require complex reasoning or high-quality generation.

A common pattern:

• Simple tasks: local rules or lightweight models
• Medium tasks: general-purpose models
• Complex tasks: high-capability models

This routing strategy can significantly reduce overall consumption while maintaining quality for core tasks.

6. Product-Level Improvements for Credit Management

As model capabilities improve, providers also need to revisit credit management and call scheduling mechanisms. Several areas deserve attention.

6.1 Increase Transparency of Subtask Calls

Users have a right to know what internal calls a task triggered. After a task completes, the product could provide a brief consumption breakdown, such as: “This task involved 3 subtask calls — requirement analysis consumed 800 tokens, code generation consumed 4,200 tokens.” This transparency helps users understand where consumption comes from and builds trust.

6.2 Reduce Unnecessary Internal Calls

For simple tasks, the system can skip complex planning and verification stages. It can dynamically select a call path based on task complexity instead of enabling the full flow for every task. This tiered-call mechanism can lower average consumption without sacrificing output quality.

6.3 Apply Default Output Length Constraints

At the product level, default output length thresholds can be introduced. For example, general chat output could default to no more than 1,500 tokens, with developers able to manually raise the limit. This prevents accidental expansion while preserving user control.

6.4 Provide More Granular Usage Reports

Showing token consumption by task type, model version, and call time helps users locate abnormal consumption points. Reports should indicate the number of internal calls corresponding to a single user request, subtask types, and output token distribution.

7. Conclusion

Improved model capability does not inevitably lead to faster credit depletion. The real issue is whether task complexity, call chains, and output strategies are optimized in tandem with subscription limits. When per-task token consumption grows severalfold, the cost advantage of lower unit prices is quickly diluted. The user’s actual experience becomes: “The model is stronger, but I’m more hesitant to use it.”

For developers, building usage monitoring, optimizing prompts, splitting long tasks, using caching, and implementing tiered model routing are all practical ways to reduce consumption. For model providers, controlling resource expansion while improving capability will be critical for user retention and long-term reputation.

As intelligent tools become embedded in production workflows, a “good” model is not just one with strong capabilities — it must also offer predictable costs, explainable consumption, and manageable resources. Only when all three are in place can large language models become reliable productivity infrastructure.