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The End of Low-Cost API Gateways: Large Model Experiences and the Impossible Triangle in March

chatgpt-5.4

Throughout March, I was constantly testing between various large model API hubs. It is indeed cheap. You can test out foreign models like ChatGPT, Claude, and Gemini for a small amount of money per month, which at first glance seems like finding an extremely cost-effective solution. However, after actually using it, I increasingly feel that this path has always been constrained by an impossible triangle: Quality, Stability, and Affordability—it is difficult for all three to be achieved simultaneously. By last weekend, the situation became quite clear. During the two days from 2026-03-28 to 2026-03-29, I felt a noticeable tightening of risk controls on ChatGPT channels, and Claude was no different. Many low-cost relays that were previously usable suddenly became unstable or even completely failed. For me, this basically signaled the temporary end of the low-cost API relay model.

What is an API Proxy/Gateway?

Let’s first clarify the concept. What is called an API Gateway (or “Proxy Station”) is not inherently a service provided by the model vendor itself, but rather an intermediary “forwarding layer” placed between the user and the upstream models. You send your request to the gateway, which then forwards it on your behalf to OpenAI, Anthropic, or other model providers, and finally returns the result to you. From the user’s perspective, it acts like a cheaper and more “flexible” unified entry point; from a technical and business model perspective, it is more like repackaging and redistributing upstream resources. The reason these services have been widely used is quite simple:

  • Low cost
  • Low barrier to entry
  • Wide variety of models available
  • For domestic users, it saves a lot of hassle related to registration, payment, and network environment setup. However, the problem lies precisely in this. Since it is not the official link, many conveniences are fundamentally built upon an “extra layer” rather than stable authorization.

How They Generally Work

The practices vary across different platforms, but the common patterns generally fall into a few categories.

1. Key Secondary Distribution

Some platforms essentially take their upstream API Keys and act as a unified forwarder, then divide the quota among downstream users. What you buy is their layer of package, not a package sold directly to you by OpenAI or Anthropic. The problem with this model is that if the upstream Key is restricted, the quota runs out, or the strategy is adjusted, the downstream experience will immediately fluctuate.

2. Account Pool Rotation

The term “account pool” often used in the industry can be understood as pooled management of a batch of account resources. The platform centralizes multiple accounts and calls them in rotation according to requests, which is used to distribute quota pressure and risk control pressure. The “account pool” here is not an official term from model vendors but rather a more colloquial or jargon-heavy description. It emphasizes the resource scheduling method rather than the product capability itself. Whoever has a larger pool appears more stable in the short term; however, this stability often vanishes instantly if upstream sources begin concentrated cleanups.

3. Reverse Engineering Wrappers

Another approach is not to use the official, standard APIs, but rather to study the request methods used by the web page or client, and then re-wrap these calling processes into an interface that “looks like an API” for users. This “reverse engineering” process, simply put, means not entering through the official door, but understanding how the system communicates by going through side entrances, windows, or even pipes, and then wrapping it in your own layer. The weakness of this method is also very obvious: what works today does not guarantee that it will work tomorrow. If the page structure, authentication methods, device verification, or behavioral policies change, the entire link may fail.

Actual Experience in March

After this intensive round of testing in March, my biggest takeaway wasn’t “cheap is surprisingly good,” but rather that you must continuously endure uncertainty. For the same requirement, one transit station might answer it well today, but tomorrow its intelligence might start declining; it might output stably in the morning but start throwing errors, timing out, or losing context in the evening. You think you are buying model capability, but what you are actually buying is a constantly fluctuating “probability service.” If you only use it for temporary testing of the model or for some light Q&A, this fluctuation is bearable. But once you put it into an actual workflow, the problems become very obvious.

  • Unstable quality, output level fluctuates wildly
  • Insufficient stability, prone to timeouts, errors, and disconnections
  • Poor context continuity, bad experience for long tasks
  • Model persona and style drift after switching upstream platforms
  • Spending time troubleshooting issues, the hidden cost is not low In other words, the biggest problem with low-cost transit services isn’t that they “aren’t cheap,” but that they transfer much of the certainty that should have been borne by official platforms back onto the user. On the surface, you save money, but in reality, you spend more time, mental energy, and reduce the predictability of your workflow.

Why I Say It Fell into the Impossible Triangle

After using it repeatedly recently, I increasingly feel that this type of relay service is destined to fall into an impossible triangle.

Quality

If you want to improve the quality, you must ensure that the upstream models are truly usable, have sufficient quotas, and experience as little throttling or degradation as possible. This task itself is not cheap.

Stability

If you want to achieve stability, you have to handle more risk control, account loss, network fluctuations, rate limiting, and backup lines. You even need to implement more complex scheduling and fallback mechanisms yourself. These are all costs.

Cost-Effective (or “Good Value”)

Once you have truly solidified the first two things, the price cannot continue to be suppressed so low. The ability to maintain ultra-low prices over the long term often indicates that the underlying cost has not been genuinely solved; it has merely been deferred or spread out across a future collective failure.

Therefore, many intermediaries that appear to be offering “high cost-performance” are actually maintaining a fragile balance. This balance holds up when things are calm, but once upstream risk controls tighten, this equilibrium can easily collapse.

Last weekend, it was pretty much obvious.

What really made me decide to give up on further tinkering was the change from 2026-03-28 to 2026-03-29. My own feeling is that the channels related to ChatGPT showed very noticeable tightening during those two days, and Claude’s risk control also strengthened in tandem. Previously, we could barely maintain a “usable” state by switching lines, models, or packages. That method suddenly no longer works. I don’t want to make an absolute judgment here like saying “the entire industry is dead,” because someone will always say they have some other usable channel. But from the perspective of actual utility for ordinary users, this path of low-cost API relay is at least no longer worth me continuing to invest time in. The concept of being cheap only has meaning when it’s predicated on “being able to complete the task reliably.” If even basic reliability cannot be guaranteed, low cost becomes an illusion.

Why This Pattern Was Inherently Fragile

On the surface, it might seem like the vendors are “deliberately restricting” people. However, if you look deeper, this type of pattern was built on a very fragile foundation to begin with.

Vendors like OpenAI and Anthropic did not design their products based on the logic of “secondary distribution gray resale.” Their official terms already have explicit restrictions regarding the reselling of API Keys, bypassing limitations, reverse engineering, or circumventing protective measures. OpenAI’s service agreement explicitly lists “buying, selling, or transferring API Keys,” “bypassing rate limits or protective measures,” and “circumventing usage restrictions” as prohibited activities; Anthropic’s commercial terms also clearly reserve space to constrain misuse, unauthorized access, and service abuse.

In other words, these relay stations are not innovating within an officially encouraged ecosystem; they are surviving in the gaps of official governance boundaries. As soon as model vendors start cleaning up seriously, this pattern will naturally be the first to take a hit.

There is also a very realistic background factor: many foreign model services already have barriers related to region, payment, and account systems. Since official support regions are limited, many users are blocked out, which created the demand for relays. But just because the demand exists doesn’t mean the pattern is stable. It only indicates that there is a market for gray alternative solutions; it does not guarantee long-term certainty.

Final Conclusion

After going through all this, my conclusion is actually simpler. Codex has a better cost-performance ratio, at least from my own usage experience; it seems more suitable for actual production environments. I haven’t heard much feedback about large-scale account bans in China, so the overall mental burden is lower. Of course, Codex isn’t without constraints. The five-hour limit and weekly limits are there, and when I use it now, I don’t act as recklessly as before—I don’t ask everything or dump everything on it to process. For many problems, I will first run them through my own mind, break them down myself, and then decide whether to spend the current quota. Looking at it this way, limits aren’t necessarily all bad. Objectively, they force the human brain to get re-involved—they force you to organize your thoughts first, make judgments first, and prioritize, rather than outsourcing all thinking. I have written about this before: a model that is slightly “less intelligent” isn’t necessarily all bad because it forces the user to maintain a basic level of critical thinking. Looking back now, this judgment still holds true. I am not considering Claude for now. It’s not that its capabilities are weak; quite the opposite, they are still very strong. But given the uncertainty of account bans even with personal paid subscriptions, it is not suitable as my primary solution at this stage. Regarding domestic models, I choose to continue observing. It’s not yet time for me to commit heavily or bind long-term. So, in the end, I returned to a very simple choice: subscribing to ChatGPT Plus and using it for now while continuing to watch how the large model industry evolves. Many times, the cheapest option is not necessarily the most cost-effective; the one that is the easiest to use is actually the true value proposition.

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