## A Real Cost Curve

A Real Cost Curve

In early 2023, a leading Chinese flagship model's API was priced at around 120 RMB per million tokens. By the mid-2024 price war, mainstream flagship models had generally fallen into the range of a few RMB to a few dozen RMB per million tokens, with some lightweight models dropping below one RMB. In three years, input prices have dropped by more than two orders of magnitude.

For big tech companies, this is the inevitable result of economies of scale and market competition. But for independent developers, this curve means something entirely different—your unit-cost mindset has to be rebuilt from scratch.

Here's a concrete comparison. An AI writing assistant with 1,000 daily active users consumes roughly 8,000 tokens per conversation per user. At early-2023 prices, daily costs would be around 960 RMB; at current flagship model prices, it might cost just a few RMB to a few dozen RMB. The same product goes from "we can't survive without subscription fees" to "free + premium unit economics can actually work."

The Three Real Impacts of the Price War

1. Access Costs: The Barrier Collapsed, But Hidden Costs Surfaced

The good news is obvious: scenarios you couldn't afford to enable before, you can now. Batch document summarization, long-context analysis, real-time conversation memory—these "money-burning features" under the old pricing can now be turned on by default.

But the bad news is that the hidden costs of integration haven't dropped proportionally—in some scenarios, they've actually risen:

• Vendors frequently adjust model versions and pricing, requiring continuous migration work. A model deprecation or version switch might mean redoing your prompt engineering.
• Multi-vendor strategies become standard. To hedge against a single vendor's price hikes, price cuts, or quality degradation, you need to maintain multiple SDK adapters.
• Cheap access invites abuse: free users spamming requests and malicious calls mean rate limiting and risk control go from "optional" to "mandatory."

2. Cost Structure: From "Model Fees Dominate" to "Engineering Fees Dominate"

This is the most easily overlooked change. Once model call costs drop 100x, your total cost structure gets reshuffled:

• The relative share of servers, bandwidth, and databases rises
• Development and debugging time becomes the biggest line item
• Vendor-switching adaptation costs can eat up months of savings on call fees in one go

In other words, the price war didn't reduce the total cost of AI applications—it changed where costs are distributed. Smart developers now optimize not for "cost per token" but for "total time from idea to launch for each feature."

3. Model Selection: Cheap Prices Gave Rise to a New "Per-Scenario Model Selection" Workflow

After prices fell, a counterintuitive phenomenon emerged: developers actually started using more different models.

In the past, to save money, all requests were routed to the cheapest model; now that prices are low enough, developers prefer fine-grained allocation by scenario:

• Initial screening, classification, format conversion → lightweight models, cost nearly negligible
• Core generation and reasoning → flagship models
• Batch offline tasks → models with batch-processing discounts

A typical multi-model routing architecture can cut overall costs by another 60%-80% compared to an "all-flagship" approach, without degrading the core experience. Under the old pricing regime, this simply wasn't worth doing—the savings couldn't cover the development costs.

Recommendations for Developers

1. Account for costs by "segment," not by "application." Don't just calculate average costs. Break your product into feature segments, and calculate token consumption and price sensitivity for each. You'll find that 80% of your calls don't actually need a flagship model.

2. Build an abstraction layer—don't weld vendor SDKs into your business code. A thin unified calling interface can save you enormous migration time when vendors change prices, switch versions, or degrade quality. Time is the most expensive resource an independent developer has.

3. Set unit economics metrics and monitor them continuously. Model call cost per daily active user, average cost per request, cost share from free users—turn these three numbers into a dashboard. In the price war era, cost overruns no longer look like "too expensive" but "so cheap you stop paying attention"—until the day volume scales up.

4. Use the price war to run product experiments. The low-cost window is the best time to test willingness to pay. Features you didn't dare make free before can now be opened to users at extremely low marginal cost, letting you quickly validate whether demand is real.

5. Beware the "cheapest" trap. Some ultra-low-price models have fluctuating stability, speed, and consistency of results. Keep a backup model for critical paths—the cost of failover is far lower than the cost of widespread user complaints.

Conclusion: Cheap Isn't the Endgame—Fast Is

Three years into the price war, the biggest winners are the developers who converted their cost savings into iteration speed. With the same budget that used to validate a single scenario, you can now afford five rounds of trial and error and validate three directions.

And to be fast, toolchain integration becomes critical. If you want to compare the calling performance of multiple models on a single platform, manage keys and billing centrally, and avoid bouncing between multiple vendor dashboards, try Thistoken's aggregated API service—registration link: https://api.thistoken.ai/register

Spend your time on the product, not on reconciliation and adaptation—that's the real dividend the price war offers independent developers.

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