Which Subscription Plan Lasts Longer? A Real Per-Million-Token Price Guide

In developer communities, a common question keeps coming up: “Which is more durable, the ~$20 monthly plan or the ~$30 monthly plan?” Answers tend to split into two camps—some say the cheaper one is the better deal, others insist only the pricier one is worth it. Most of these debates are pointless, because “durability” is never a property of a plan’s sticker price. It is a function jointly determined by the plan’s billing rules and your personal usage profile. This article lays out an actionable estimation method: align model tiers, convert to a real unit price, build a usage profile, and finish with a decision checklist.

1. First, Clarify What “Durability” Is Actually Comparing

Durability is fundamentally a ratio problem: under a fixed monthly budget, how much real work can a plan’s quota support? Three variables determine it:

1. Effective quota: the portion of the plan’s nominal token allowance that is actually consumable after billing rules are applied.
2. Model tier: the capability level of the model bound to the plan, which determines how many tokens a given task requires.
3. Usage profile: personal characteristics such as the distribution of usage across time periods, the input-to-output ratio, and cache hit rates.

Comparing only monthly fees, or only nominal quotas, is answering a multi-variable question with a single variable—which is exactly why “which is more durable, 149 or 199” never reaches a conclusion.

2. Step One: Align Model Tiers Before Discussing Price

Before comparing two plans, you must confirm that their bound models sit at the same capability tier. Token consumption is determined not only by task size but also by model capability:

• Different first-pass success rates: a stronger model may complete debugging or refactoring in fewer rounds; a weaker model requires repeated follow-up prompts and constant output correction, so actual consumption can be several times the nominal workload.
• Different context bloat: weaker models more easily “forget” previously agreed conventions, forcing you to resend system prompts and key context repeatedly, which further inflates input consumption.
• Different effective task cost: the cost of completing the same task ≈ real unit price × (input + output + retry consumption). When comparing across tiers, if you don’t first convert “task volume” into “consumption volume,” a direct unit-price comparison is guaranteed to be distorted.

Hence the basic industry consensus: comparing a mid-tier model with a flagship model on “durability” is unfair. Either compare unit prices within the same tier, or use “total cost to complete the same batch of tasks” as the baseline—pick one.

3. Step Two: Convert to a Real Per-Million-Token Price

Once tiers are aligned, the next step is converting nominal unit prices into real unit prices. There are only two core formulas:

• Nominal unit price = monthly fee ÷ nominal quota
• Real unit price = monthly fee ÷ effective quota

The most common conversion factor is the peak multiplier: some plans deduct quota at a multiple during peak hours (for example, a 3x deduction). In that case:

Effective quota = nominal quota × [(1 − peak usage share) + peak usage share ÷ peak multiplier]

Consider a hypothetical example (numbers are for demonstrating the calculation method only and do not represent any real product):

Variable	Plan A (hypothetical)	Plan B (hypothetical)
Monthly fee	150 CNY	200 CNY
Nominal quota	1500 MTok	1600 MTok
Nominal unit price	0.10 CNY/MTok	0.125 CNY/MTok
Peak rule	3x deduction during peak hours	No peak/off-peak distinction
Effective quota at 50% peak usage	1500×(0.5+0.5/3)=1000 MTok	1600 MTok
Real unit price	0.15 CNY/MTok	0.125 CNY/MTok

The conclusion is clear at a glance: Plan A, whose nominal unit price is 20% cheaper, turns out to be 20% more expensive in real unit price under a profile where “half of usage falls in peak hours.” If your working hours are highly flexible and you can actively shift usage off-peak, Plan A remains a good deal; if your tasks are triggered by scheduled pipelines and must run during business hours, Plan B is the more durable choice. This leads to a key insight: evaluating plans outside the context of your personal profile is meaningless.

4. Step Three: Factor Hidden Variables Into the Profile

The real unit price is also affected by several “hidden variables” that are often overlooked yet can easily swing durability by several times:

1. Input/output price gap: most model services price output tokens at several times the rate of input tokens. In coding Agent scenarios, the output share is high (generating code, multi-round revisions), which significantly raises the real average price.
2. Cache discounts: code assistants and Agents repeatedly send the same system prompts, repository summaries, and conversation history. If the platform offers prefix caching discounts, the cost of the cached portion is usually far below the standard input price; without caching, the same workload consumes more quota.
3. Long-context surcharges: some services charge a higher unit price for context beyond a certain length, so long-session scenarios need separate estimation.
4. Rate limits: quota remains unused but you hit per-minute request, token, or concurrency caps—effectively a form of premature durability loss. Automated pipeline scenarios especially need to verify rate-limit parameters.
5. Shared quota pools: some plans allow multiple models to share a single quota pool. Routing simple tasks to lightweight models and complex tasks to the main model can significantly extend quota lifespan.
6. Agent loop consumption: token consumption for automated multi-round tasks is nonlinear. A single coding task involving retrieval, planning, coding, and self-testing can easily consume hundreds of thousands of tokens, so evaluation cannot rely on a rough estimate of “a few dozen chat rounds per day.”

5. Let the Data Speak: The Two-Week Profiling Method

Rather than asking “which plan is more durable,” spend two weeks first drawing your own usage profile: start with pay-as-you-go billing, record the input/output tokens, time period, and cache hits of each call, then use that to project the real unit price under each plan. The following script demonstrates the basic approach:

import csv
def profile(log_path):
    """Compute monthly usage and peak share from call logs
    Log fields: date, tokens_in, tokens_out, is_peak, cached
    """
    t_in = t_out = peak = cached = 0
    with open(log_path, newline="") as f:
        for row in csv.DictReader(f):
            a, b = int(row["tokens_in"]), int(row["tokens_out"])
            t_in += a; t_out += b
            if row["is_peak"] == "1":
                peak += a + b
            if row["cached"] == "1":
                cached += a
    total = t_in + t_out
    print(f"Monthly consumption ~{total/1e6:.1f} MTok, output share {t_out/total:.0%}")
    print(f"Peak-hour share {peak/total:.0%}, cacheable input share {cached/t_in:.0%}")
    return total, peak / total
def effective_quota(nominal, peak_share, peak_ratio=3.0):
    """Quota equivalence conversion under peak-multiplier billing"""
    return nominal * ((1 - peak_share) + peak_share / peak_ratio)
def real_price(fee, quota):
    return fee / quota
total, share = profile("usage.csv")
# Example using "Plan A": 150 CNY monthly fee, 1500 MTok nominal, 3x peak deduction
print(f"Real unit price ~{real_price(150, effective_quota(1500, share)):.3f} CNY/MTok")

Once you have the profile, plug each candidate plan into the calculation to compare real unit prices, and check against your daily average consumption to estimate whether the quota will last a full month. With Baidu AI Cloud, for example, enterprise users can first observe the call distribution of each service through the console’s usage statistics, then decide between pay-as-you-go and monthly plans—measure first, buy later, is the most effective step for controlling costs.

6. Plan Selection Decision Checklist

Based on the analysis above, check each item when choosing a plan:

1. Model tier: does the plan’s model lineup cover your main task tiers? Have you converted task costs when comparing across tiers?
2. Quota basis: does the nominal quota distinguish input from output? For output-heavy scenarios, recalculate using the output unit price.
3. Peak/off-peak rules: what is the peak multiplier? Does it conflict with your working hours? Can you actively schedule around peaks?
4. Rate-limit parameters: are the per-minute request, token, and concurrency caps above your peak demand?
5. Caching and long context: is there a prefix caching discount? Are there surcharges for long sessions?
6. Over-quota policy: when quota runs out, does billing switch to pay-as-you-go or does service stop hard? Is the overage unit price acceptable?
7. Adjustment flexibility: can you upgrade mid-term? Can unused quota at month-end roll over?

7. Conclusion

“Which plan is more durable” is a good question, but the right way to open it is not to compare sticker prices—it is to measure. First align model tiers, then convert billing rules into real unit prices using the formula, and finally quantify your personal usage habits with a two-week profile. Do these three steps and you will arrive at an answer that belongs only to you—one that may differ completely from community opinion, but is certainly closer to the facts. Data is always more reliable than debate.