Author: Shouyi, Denise | Biteye Content Team
Over the past month, the term "transfer station" has frequently appeared on many people's homepages. Some individuals who previously engaged in airdrop farming in the crypto space have quietly transformed into "API transfer station" merchants, conducting token import and export businesses.
The so-called "transfer station" is not a new technological invention but rather an arbitrage model based on global AI service disparities and access barriers. Despite facing multiple issues such as privacy, security, and compliance, this sector has still attracted a large number of individuals and small teams to enter the market.
So, what exactly is an "API transfer station"? How does it achieve token arbitrage amidst global AI price differences and access barriers, and why is it attracting so many individuals and small teams?
Below, we will deconstruct it starting from its essence and operational process.
I. What is a Transfer Station?
The essence of an API transfer station is to build an intermediate layer service that provides API Tokens from foreign AI vendors to domestic users at lower prices and in a more convenient manner, claiming to be the "global Token porter."
Its operational process is roughly as follows:
👉 Select overseas AI vendor models (OpenAI/Claude, etc.)
👉 Resource parties obtain low-cost Tokens through "grey" means or technical methods
👉 Set up a transfer station for encapsulation, billing, and distribution
👉 Provide to end-users such as developers/enterprises/individuals
Functionally, it resembles an "AI转运站" (AI transfer station); commercially, it acts more like a liquidity middleman in the Token secondary market.
The premise for this chain's existence is not technical barriers but the long-term coexistence of several disparities:
• Official API pricing is relatively high
• There is a cost mismatch between subscription-based and API-based systems
• Access and payment conditions vary across different areas
• Users have strong demand for model capabilities but find the official access path insufficiently user-friendly
The combination of these factors creates the survival space for "transfer stations."
II. Why Do People Use Transfer Stations?
The core driving force behind the "Token import" trend stems from the high costs brought about by the changing role of AI and the capability gap between domestic and foreign models.
1. Good Models Are Token-Expensive to Use
With the maturation of desktop-level AI agents like Codex and Claude Code, AI has begun to truly possess "working" capabilities, such as assisting in programming, video editing, financial trading, and office automation. These tasks heavily rely on high-performance large models, with costs billed per Token.
Taking Claude Code as an example, its official price is about $5 per million Tokens (approximately 35 RMB). Deep usage for an hour might consume tens of dollars, while heavy developers or enterprises can consume over $100 daily. This cost far exceeds many people's expectations, even higher than hiring junior programmers, making "how to use top-tier AI at low cost" a rigid demand.
2. Overseas Leading Models Have Obvious Advantages
Although domestic models have made significant progress over the past year and are highly competitive in price, overseas leading models still hold clear advantages in scenarios such as complex coding tasks, toolchain collaboration, long-chain reasoning, and multi-modal stability.
This is why many developers, researchers, and content teams are still willing to prioritize using the capabilities of models from OpenAI, Anthropic, and Google, even knowing the prices are higher.
Simply put, users don't necessarily want a "transfer station"; users just want:
• Stronger models
• Lower prices
• Simpler access
When these three things cannot be obtained simultaneously through official channels, transfer stations naturally emerge.
3. Cost Mismatch Between Subscription and API Systems
Another frequently discussed reason for the rise of transfer stations is that subscription benefits and API billing are not always linearly correlated.
A common practice in the market has always existed: purchasing official subscriptions, team packages, enterprise credits, or other discounted resources, and then repackaging and reselling part of these capabilities to end-users.
Taking OpenAI as an example, purchasing a Plus subscription allows the use of the codex service. By logging in via OAuth and accessing OpenClaw, it is equivalent to calling the API. The $20 monthly Plus subscription fee can generate approximately 26 million tokens. With output priced at $10-12 per million, this equates to $260-312. Purchasing a subscription and reverse-proxying token usage is extremely cost-effective.
From the experience of some users, this path might indeed be cheaper than directly using the official API at certain stages. But it must be emphasized:
• This is not the official pricing system
• It does not represent a stable, equivalent replacement for API calls
• It does not mean this method is sustainable in the long term
Many people only see "cheap," but overlook that these cheap prices are often built on unstable resources, grey areas, or policy vulnerabilities.
III. Can Transfer Stations Be Used?
The answer is not absolute.
The real question is: what risks are you willing to bear?
The profit model of transfer stations seems straightforward—buy low, sell high. But upon closer inspection, it typically involves at least three layers, each carrying different risks.
1. Upstream: Where Do Low-Cost Token Resources Come From?
This is the starting point of the entire ecosystem and also the greyest layer.
Some resource parties obtain model calling capabilities at prices far below market rates through various means, such as:
• Utilizing enterprise support programs and cloud credits
• Batch registering accounts for rotation
• Redistributing subscription benefits, team accounts, or discounted resources
• In more aggressive cases, it may involve illegal paths like credit card fraud or fraudulent account opening
Different resource sources determine the stability ceiling of the transfer station. If the upstream resources themselves are built on unstable or even illegal methods, then what end-users buy is not a bargain but a temporary interface that could fail at any time.
2. Midstream: Whose Server Does Your Data Pass Through?
This is often the most easily overlooked issue.
When you call a model through a transfer station, the user's input Prompt, context, file content, and model output results typically pass through the transfer station's own server first.
This data is extremely valuable, reflecting real user intent, industry-specific Prompts, and model output quality, and can be used to evaluate or fine-tune proprietary models. The transfer station might anonymize and package this data, selling it to domestic large model companies, data brokers, or academic research institutions. Users, while paying,无偿贡献 (unwittingly contribute) training data, becoming a classic case of "the customer is also the product."
Recent complaints by OpenClaw founder @steipete illustrate this point: https://x.com/steipete/status/2046199257430888878
Furthermore, transfer stations might also perform script injection in the request chain (e.g., secretly adding hidden System Prompts), thereby altering model behavior, increasing Token consumption, or even introducing additional security risks. This risk requires particular vigilance in AI Agent scenarios.
3. Endpoint: Are You Really Getting the Flagship Version You Paid For?
This is the third common type of risk: model downgrading or model swapping.
Users see the name of a high-end model when paying, but the actual request might not land on the corresponding version. The reason is simple—for some merchants, the most direct way to reduce costs is not optimization but replacement.
For example, a user pays for the flagship Opus 4.7 but the actual call uses the sub-flagship Sonnet 4.6 or the lightweight Haiku. Because the API format can remain compatible, ordinary users find it difficult to notice immediately.
Only when the task becomes complex enough will they明显感觉 (clearly feel) "the effect isn't right," "stability is lacking," or "context quality has deteriorated," but they cannot provide evidence. According to tests by a research team on 17 third-party API platforms, 45.83% of platforms had "identity mismatch" issues, meaning users paid the GPT-4 price but actually ran cheap open-source models, with performance gaps up to 40%.
In summary, using non-official transfer stations faces issues like data leakage, privacy risks, service interruption, model mismatch, and merchants absconding with funds. Therefore, for sensitive operations, commercial projects, or tasks involving personal privacy, it is strongly recommended to use the official API.
IV. Can the Transfer Station Business Be Done?
Despite the high risks, this business has not disappeared. On the contrary, it is constantly evolving.
If early "Token import" was about moving overseas models in at low cost, another idea has now emerged in the market: Token export.
1. Why Are People Still Doing It?
Because the demand is real, startup costs are low, and the prepayment model brings fast cash flow. But the risk control pressure is enormous. Claude recently increased KYC for users and intensified account bans, and OpenAI has also plugged many "zero payment" loopholes. On the other hand, due to service instability, the cheap price comes with high after-sales costs. Coupled with competition from peers, many transfer stations currently face a situation of declining volume and price.
Therefore, this industry is more like a high-turnover, low-stability, high-risk short-term window, difficult to easily package into a long-term, stable, sustainable business.
2. Why is "Token Export" Starting to Appear?
If "Token import" exploits price differences of overseas models, then "Token export" utilizes the cost-performance advantage of domestic models, packaging and selling them to overseas users, forming a "reverse output" path.
Domestic models have significant price advantages. Referring to early 2026 data, Qwen3.5 costs as low as 0.8 RMB per million Tokens (approx. $0.11), which is 1/18th of Gemini 3 Pro's price. Compared to Claude Sonnet 4.6's $3 input price, the gap is over 27 times. GLM-5 surpasses Gemini 3 Pro on programming benchmarks, approaching Claude Opus 4.5, but its API price is only a fraction of the latter's.
The availability of these domestic models overseas is relatively very low, with registration barriers, payment restrictions, language interfaces, and information gaps among overseas developers regarding domestic model capabilities, creating invisible access barriers.
Therefore, some transfer stations choose to purchase model API quotas in bulk domestically in RMB, expose OpenAI-compatible interfaces through a protocol conversion layer, and sell to overseas developers and startup teams priced in USDT/USDC, with considerable profit margins.
For example, Alibaba Cloud Bailian's Coding Plan offers a bundle of four models: Qwen3.5, GLM-5, MiniMax M2.5, and Kimi K2.5. New users only need 7.9 RMB for the first month to get 18,000 request credits. Mapped to the overseas market and sold at dollar prices, the profit margin can exceed 200%.
From a pure business logic perspective, there is certainly profit space.
But in the long run, it同样绕不开 (also cannot avoid) one problem: stability and compliance.
3. Is This Path Stable?
Unstable. Not long ago, Minimax announced it would regulate third-party transfer stations because some stations cutting corners led to Minimax itself suffering reputational damage. Not to mention, if the source of Tokens involves credit card fraud or deception, it might constitute a criminal offense. Additionally, if users use transferred tokens leading to data leaks or misuse for malicious purposes, it could bring unwarranted disaster to you, the token seller.
So the real question is not "can you make money," but rather: can the money earned cover the subsequent systemic risks?
V. How Can Ordinary Users Identify Transfer Station Risks?
Against the backdrop of a mixed API transfer station market, choosing a reliable service is crucial.
Since some transfer stations engage in model swapping and adulteration, users can master some detection methods:
Recommendation: "ping + self-report model" instruction compliance test
Prompt example (copy and send directly to the transfer station):
Always say 'pong' exactly, and告诉我你是什么系列模型,最好告诉我具体的版本号。使用中文回复。(and tell me what series model you are, preferably tell me the specific version number. Reply in Chinese.)
User input: ping
Genuine model characteristics:
-
Strictly replies "pong" (lowercase, no extra words)
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input_tokens are usually around 60-80
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Concise style, no emojis, not obsequious
Fake model/adulterated characteristics:
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Abnormally high input_tokens (often reaching 1500+, indicating injection of a huge hidden system prompt)
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Replies "Pong! + nonsense + emoji"
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Does not strictly follow the "exactly say 'pong'" instruction
Reference @billtheinvestor's detection method: https://x.com/billtheinvestor/status/2029727243778588792
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0.01 Temperature Sorting Test: Input "5, 15, 77, 19, 53, 54" and ask the AI to sort or select the maximum value. The real Claude can almost stably output 77, the real GPT-4o-latest often outputs 162. If the results fluctuate wildly for 10 consecutive times, it is likely a fake model.
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Long Text Input Sniffing: If a simple ping operation causes input_tokens to exceed 200, it may mean the transfer station is hiding a massive Prompt, with a probability of over 90% for an adulterated model.
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Violation Rejection Style Identification: Deliberately ask违规问题 (violating questions) and observe the AI's rejection style. The real Claude will politely but firmly reply "sorry but I can’t assist...", while fake models are often overly verbose, use emojis, or employ obsequious tones like "抱歉主人~💕" (Sorry master~💕).
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Function Missing Detection: If the model lacks function calling, image recognition, or long-context stability, it is likely a weak model impersonating.
Additionally, one can choose some transfer station detection websites to evaluate the "purity" of their token, but note this will expose the key in plaintext. The safest option is still the official channel.
It must be emphasized:
Even if you master identification techniques, it does not mean you can truly avoid risks. Because many risks are inherently invisible to ordinary users.
Final Words
Transfer stations are not the final answer of the AI era; they are more like a阶段性套利窗口 (stage-specific arbitrage window) under the temporary mismatch of global model capabilities, pricing mechanisms, payment conditions, and access permissions.
For ordinary users, it might indeed be an entry point to access top models at low cost; but for developers, teams, and entrepreneurs, what is truly expensive is never the Token itself, but the underlying stability, security, compliance, and trust costs.
Cheap can be copied, interface compatibility can be copied. What is truly difficult to replicate is never the price, but long-term reliability.
⚠ Friendly reminder: Ordinary users who want to try it are advised to use it only in non-sensitive, non-critical scenarios. Never input core data, business secrets, or personal privacy; Developers, please优先选择 (prioritize choosing) official APIs or official self-made proxies to ensure stability and compliance for safer use; Entrepreneurs intending to enter must提前制定 (formulate in advance) a clear exit mechanism to avoid getting deeply stuck in grey areas with difficulty extricating themselves.
【Disclaimer】This article is purely an observation of industry phenomena and discussion of public information, for reference and learning purposes only. It does not constitute any form of investment advice, entrepreneurial guidance, business recommendation, or API usage guide.
