1. The Root Cause: The Economics of Quotas Under Heavy Workloads

Current mainstream AI coding assistants generally adopt a “credit/quota” billing model: free quotas are distributed monthly, supplemented slightly by daily check-ins or task rewards, with pay-as-you-go or subscription upgrades required for overages. While friendly to light users, this model presents two structural issues for heavy users:

1. Heavy task consumption is non-linear. Consider a real-world task: converting a 75-page PDF to Markdown (extracting images to a separate directory, converting formulas to LaTeX). The entire process took about 1 hour and 50 minutes, consuming nearly 500 credits. If the monthly free quota is only a few thousand credits, a single large task can consume a substantial portion.
2. Subscription tiers mismatch real-world needs. Suppose a service offers 3,500 free credits monthly, and a $10 top-up only buys an additional 3,500 credits. When “paid credits ≈ free credits,” the pricing structure is likely designed to push users toward higher-tier subscriptions. In this scenario, blindly topping up or upgrading subscriptions yields a very low marginal return.

Therefore, the correct sequence of action is not “pay first,” but rather “audit first, optimize second, and only consider paying last.”

2. Step One: Establish Usage Auditing to Find the Real Credit Drains

The prerequisite for cost optimization is knowing where your credits are going. It is recommended to log an entry for every task:

# usage_log.py: Log the consumption of each AI task
import json, datetime
def log_task(task_name, model_tier, credits_used, duration_min, note=""):
    record = {
        "time": datetime.datetime.now().isoformat(timespec="seconds"),
        "task": task_name,
        "model_tier": model_tier,        # light / flagship
        "credits": credits_used,
        "duration_min": duration_min,
        "note": note,
    }
    with open("usage.jsonl", "a", encoding="utf-8") as f:
        f.write(json.dumps(record, ensure_ascii=False) + "\n")

After accumulating data for two to three weeks, perform an aggregate analysis. You will typically draw two conclusions:

• Consumption is highly concentrated: The top 20% of tasks often consume 80% of the credits.
• High consumption frequently occurs in “deterministic phases”: For instance, in the earlier PDF case, tasks like “cleaning up page screenshots”—pure file operations where the model generates almost no intellectual value yet continues to bill by the token. The original author avoided a massive credit drain by proactively interrupting the session and switching to local command-line tools.

Solution: Decouple deterministic phases from the model session and delegate them to local scripts. Tasks like cleaning output directories, renaming files, or format validation can be handled in a dozen lines of code with zero credit consumption:

# cleanup.py: Offload deterministic work like "cleaning screenshots" from the session
from pathlib import Path
fig_dir = Path("figures")
for img in fig_dir.glob("*.png"):
    if img.stat().st_size < 1024:   # Clean invalid screenshots smaller than 1KB
        img.unlink()

This step alone can usually cut over 30% of wasteful consumption, making it the most cost-effective optimization.

3. Step Two: Tiered Model Routing to Spend Credits Wisely

3.1 Selecting Models by Scenario Tier

Most platforms offer multiple model tiers, ranging from flagship to lightweight (variously named Flash, Mini, Turbo, etc., across different platforms). An empirically sound tiering principle is as follows:

Task Type	Examples	Suggested Tier
High-frequency, low-difficulty	Code completion, comment generation, format conversion, simple refactoring	Lightweight
Medium complexity	Single-module bug fixes, unit test generation	Primarily Lightweight
Low-frequency, high-difficulty	Architecture design, cross-module debugging, long-context analysis	Flagship

Over the past couple of years, lightweight models from various vendors have improved significantly. For daily tasks, the capability gap between them and flagship models is far smaller than the price gap. Reserving flagship models for scenarios that genuinely require deep reasoning is the core method for controlling costs.

3.2 Replace “Gut Feelings” with a Private Evaluation Set

Whether the “lightweight tier is good enough” should not be judged by impression, but by data. Public leaderboards (third-party comprehensive evaluations, held-out tests, public benchmarks) only provide a rough reference—different evaluations focus on different areas, and most official questions are not public. Relying on a single leaderboard easily leads to distorted conclusions. A more reliable approach is to build a small-scale evaluation yourself:

1. Extract 20 to 50 representative samples from your recent real tasks (a typical refactoring request, a real bug scenario, a document parsing task).
2. Fix the prompt and output format, and run them separately using candidate models.
3. Score them on correctness, completeness, and rework frequency, while recording credit consumption.
4. Calculate “problems solved per unit credit” as the cost-effectiveness metric.

# eval_harness.py: A minimal batch evaluation framework
import json
TASKS  = [json.loads(l) for l in open("tasks.jsonl", encoding="utf-8")]
MODELS = ["light-tier", "flagship-tier"]
for model in MODELS:
    score, cost = 0, 0
    for t in TASKS:
        result = run_task(model, t["prompt"])      # Replace with your execution entry point
        score += grade(result, t["expect"])        # Rule-based scoring or manual grading
        cost  += result["credits"]
    print(f"{model}: Score {score}, Cost {cost}, Cost-effectiveness {score/max(cost,1):.3f}")

The conclusions are often surprising: on a private task set, a lightweight model might achieve 90% of the flagship model’s score for only 1/5 of the consumption. This measured ratio is the true basis for routing decisions.

4. Step Three: Compliantly Integrate Free Resources to Extend Your Budget

Under compliant circumstances, there are several common paths to expand available quotas:

1. Maximize a single channel’s free mechanisms: Monthly free quotas, daily check-ins, and task rewards add up. It is recommended to use them at fixed times to avoid expiration waste.
2. Multi-channel division of labor: Different cloud providers and model platforms offer free trial quotas or starter resources. For example, place light tasks like format conversion and document summarization within Channel A’s free quota, and core coding tasks in Channel B. Model service platforms like Baidu AI Cloud typically offer both pay-as-you-go and free trial resources, making them suitable as one component in a channel mix. The key is to manage a matrix of “Channel × Remaining Quota × Task Specialization.”
3. Beware of gray-area methods: Tools that rely on “multi-account switching” to multiply quotas seemingly double your credits, but they generally violate Terms of Service and carry ban risks. Furthermore, migrating session records and task contexts across accounts easily introduces consistency bugs (practitioners have reported frequent issues when resuming tasks). For teams, the collaboration disruption caused by an account ban is far more expensive than the credits saved, making this an inadvisable long-term solution.

5. Step Four: Data-Driven Payment Decisions

After completing the first three steps, the payment decision becomes simple arithmetic:

Monthly True Deficit = Monthly Average Consumption − Stable Free Quota
Break-Even Point     = Subscription Fee ÷ Pay-As-You-Go Unit Price

• Small and unstable monthly deficit → Pay-as-you-go; buy what you need.
• Large and stable monthly deficit, and consumption is below the break-even point → Subscription is more cost-effective.
• “Paid credits ≤ free credits” (e.g., the earlier scenario where $10 = 3,500 credits) → Indicates that this tier’s pricing is unreasonable. Skip it entirely and evaluate whether a higher tier matches your consumption structure, or switch to a pay-as-you-go platform.

6. Institutionalize Optimization into Your Workflow

Individual developers are advised to conduct a 15-minute monthly review: update consumption distributions, review the model routing table, and eliminate low-cost-effectiveness tasks. Teams should add two layers of mechanisms:

• Unified Gateway: Route all model calls through a unified entry point, natively providing consumption statistics and budget control capabilities.
• Budget Alerts: Set monthly thresholds for projects or individuals, triggering an alert at 80% to avoid end-of-month bill shocks.

7. Conclusion

The essence of running out of credits is a mismatch between “task consumption structure” and “billing structure.” The resolution can be summarized in four steps: Audit first to cut wasteful consumption in deterministic phases; tier second to let lightweight models handle high-frequency tasks; integrate third to compliantly utilize free resources across channels; pay last, letting data dictate the payment method. By following this sequence, most developers can cut their actual costs by more than half, while reserving true flagship computing power for the scenarios where it matters most.