Original Title: “AI ‘Transit Hub’ Monthly Income of Millions? Five Questions Uncover the Truth About Token Arbitrage!”
Original Source: Biteye

Over the past month, the phrase “Transit Hub” has frequently appeared on many people’s homepages. Some players who once exploited airdrops in the crypto space have quietly transformed into “API Transit Hub” merchants, engaging in token import and export business.

The so-called “Transit Hub” is not a new technological invention but an arbitrage model based on global AI service price differences and access barriers. Although this track faces multiple issues such as privacy, security, and compliance, it still attracts a large number of individuals and small teams to enter.

So, what exactly is an “API Transit Hub”? How does it achieve token arbitrage amid global AI price differences and access barriers, and attract many individuals and small teams?

Let’s start by dissecting its essence and operational process.

1. What is a Transit Hub?

The essence of an API Transit Hub is to build an intermediary service layer that provides foreign AI vendor API tokens at lower prices and more convenient access to domestic users, claiming to be a “global token transporter.”

Its operation process roughly includes:

· Choose overseas AI vendor models (OpenAI/Claude, etc.)

· Resource providers obtain low-cost tokens through “gray” means or technical methods

· Build a transit hub for encapsulation, billing, and distribution

· Offer to end users such as developers/companies/individuals

Functionally, it resembles an “AI transfer station”; commercially, it is more like a liquidity intermediary in a secondary token market.

The premise for this chain to exist is not technical barriers but the coexistence of several long-term differences:

· Official API pricing is relatively high

· Subscription models and API costs are mismatched

· Access and payment conditions vary across regions

· Users have strong demands for model capabilities but find official access paths not user-friendly enough

These factors stack up to give “Transit Hubs” room to survive.

2. Why do people use transit hubs?

The reason “Token Import” has become a trend is mainly driven by the high costs brought by AI role transformation and the capability gap between domestic and foreign models.

1. Good models consume a lot of tokens

With the maturity of desktop-level AI agents like Codex, Claude Code, etc., AI has truly gained “work” ability, such as assisting programming, video editing, financial trading, and office automation. These tasks heavily depend on high-performance large models, billed by tokens.

For example, Claude Code’s official price is about $5 per million tokens (about 35 RMB). Intensive use for an hour may cost dozens of dollars, and heavy developers or companies may consume over $100 daily. This cost far exceeds many people’s expectations, even higher than hiring junior programmers, making “how to use top AI at low cost” a pressing need.

2. Overseas top models have obvious advantages

Although domestic models have improved rapidly over the past year and are highly competitive in price, overseas top models still hold clear advantages in complex coding tasks, toolchain collaboration, long-chain reasoning, multimodal stability, and other scenarios.

This is why many developers, researchers, and content teams, even knowing the higher prices, still prefer to use models from OpenAI, Anthropic, Google.

Simply put, users don’t necessarily need a “Transit Hub”; they just want:

· More powerful models

· Lower prices

· Simpler access

When these three needs cannot be met simultaneously through official channels, transit hubs naturally emerge.

3. There is a cost mismatch between subscription and API models

The rise of transit hubs is also frequently discussed because: Subscription rights and API billing are not always linearly correlated.

A common practice in the market is to purchase official subscriptions, team packages, enterprise credits, or other discounted resources, then encapsulate part of the capabilities for resale to end users.

Taking OpenAI as an example, buying a Plus subscription allows access to Codex services, and via OAuth login to OpenClaw, it’s equivalent to calling the API. The $20 monthly subscription can generate about 26 million tokens, with output costs around $10-12 per million, totaling $260-312. Using subscriptions as a proxy for token usage offers high cost performance.

Based on user experiences, this approach can indeed be cheaper than direct official API calls at certain stages. But it’s important to emphasize:

· This is not an official pricing system

· It does not necessarily provide stable, equivalent API access

· And it’s not sustainable long-term

Many see only the “cheapness” and overlook that behind these discounts often lie unstable resources, gray areas, or strategic loopholes.

3. Can you use transit hubs?

Whether you can use them depends on your willingness to accept certain risks.

The profit model of transit hubs looks straightforward—buy low, sell high. But when broken down, it usually 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 the most gray layer.

Some resource providers obtain model invocation capabilities far below market prices through various means, such as:

· Exploiting enterprise support programs and cloud credits

· Bulk registering accounts for rotation

· Re-distributing via subscription rights, team accounts, or discounted resources

· More aggressively, possibly involving credit card fraud, illegal account creation, etc.

Different sources determine the upper limit of the hub’s stability. If upstream resources are based on unstable or illegal methods, end users are not getting cheap access but a temporary interface that could fail at any time.

2. Midstream: Who processes your data?

This is often the most overlooked issue.

When you call a model through a transit hub, your input prompt, context, file contents, and model output usually pass through the hub’s own servers first.

These data are highly valuable, reflecting real user intentions, industry-specific prompts, and model output quality, which can be used for evaluation or fine-tuning proprietary models. The hub may anonymize and package this data for sale to domestic large model companies, data brokers, or academic research institutions. Users pay while unknowingly contributing training data—becoming a “customer and product” at once.

Recently, @steipete, founder of OpenClaw, expressed this concern:

Additionally, transit hubs may inject scripts into request chains (e.g., secretly adding hidden System Prompts), altering model behavior, increasing token consumption, or introducing security risks. Such risks are especially critical in AI agent scenarios.

3. End-user: Are you really getting the flagship version?

This is the third common risk: Model downgrades or swaps.

When users pay, they see a high-end model name, but the actual request may not be to that version. The reason is simple—some merchants’ most direct cost-cutting method is not optimization but replacement.

For example, a user pays for the flagship Opus 4.7, but the actual call is to a sub-flagship Sonnet 4.6 or a lightweight Haiku. Because API formats remain compatible, ordinary users find it hard to notice immediately. Only when tasks become complex do they feel “something’s off,” “stability is lacking,” or “context quality drops,” but without concrete proof.

A study tested 17 third-party API platforms, finding 45.83% had “identity mismatch” issues—users paying GPT-4 prices but actually running cheaper open-source models, with performance gaps up to 40%.

In summary, using unofficial transit hubs risks data leaks, privacy breaches, service interruptions, model mismatches, or even fraud. Therefore, for sensitive business, commercial projects, or tasks involving personal privacy, it’s strongly recommended to use official APIs.

4. Can this business be done?

Despite high risks, this business has not disappeared. On the contrary, it continues to evolve.

If early “Token import” was about bringing overseas models at low cost, now another approach has emerged: Token export.

1. Why do some still do it?

Because the demand is real, startup costs are low, and prepaid models generate quick cash flow. But risk control is huge—recently Claude increased KYC and account bans, OpenAI has blocked many “zero-cost” loopholes. On the other hand, service instability means cheapness often comes with high after-sales costs. Coupled with fierce competition, many transit hubs now face declining volume and prices.

Thus, this industry resembles a high-turnover, low-stability, high-risk short-term window, difficult to package as a long-term, steady, sustainable business.

2. Why is “Token export” reappearing?

If “Token import” exploits overseas model price differences, “Token export” leverages domestic models’ cost-effectiveness, packaging and selling them to overseas users, creating a “reverse output” path.

Domestic models have significant price advantages. For example, early 2026 data shows Qwen3.5 costs as low as 0.8 RMB per million tokens (about $0.11), which is 1/18 of Gemini 3 Pro, and over 27 times cheaper than Claude Sonnet 4.6 at $3 input price. GLM-5 surpasses Gemini 3 Pro in coding benchmarks and approaches Claude Opus 4.5, but API prices are only a fraction of the latter.

These domestic models are relatively hard to access overseas due to registration barriers, payment restrictions, language interfaces, and information gaps about capabilities among overseas developers, forming an invisible entry barrier.

Therefore, some transit hubs choose to purchase model API quotas in RMB domestically, then expose OpenAI-compatible interfaces via protocol conversion layers, selling to overseas developers and startups priced in USDT/USDC, with considerable profit margins.

For example, Alibaba Cloud’s Baolian Coding Plan offers Qwen3.5, GLM-5, MiniMax M2.5, Kimi K2.5 in a bundle. New users can get 18,000 requests for just 7.9 RMB in the first month, and when sold in overseas markets priced in USD, profit margins can exceed 200%.

From a pure business perspective, this certainly has profit potential.

But long-term, it also faces issues of stability and compliance.

3. Is this route stable?

Unstable. Recently, Minimax announced plans to regulate third-party transit hubs because some hubs cut corners, damaging Minimax’s reputation. Not to mention, if token sources involve theft or fraud, it could be criminal. Using transit tokens may lead to data leaks or malicious activities, which could also bring trouble to the seller.

So, the real question isn’t “Can I make money?” but “Can the earnings cover systemic risks?”

5. How can ordinary users identify transit hub risks?

In a market full of mixed quality, choosing reliable services is crucial.

Since some transit hubs engage in model swaps or adulteration, users can employ detection methods:

· “Ping + self-report model” command test

Always say “pong” exactly, and tell me what series of model you are, preferably the specific version number. Reply in Chinese.
User input: ping

True model features:

· Strictly reply “pong” (lowercase, no extra nonsense)

· input_tokens usually around 60-80

· Simple style, no emojis, no flattery

Fake/adulterated model features:

· input_tokens abnormally high (often 1500+), indicating injection of large hidden system prompts

· Reply “Pong! + nonsense + emoji”

· Not strictly following “exactly say ‘pong’” command

Reference detection method from @billtheinvestor:

1. 0.01 temperature sorting test: Input “5, 15, 77, 19, 53, 54” and ask AI to sort or pick the maximum. Genuine Claude can reliably output 77; genuine GPT-4o-latest often outputs 162. If results fluctuate wildly over 10 tries, it’s likely a fake model.

2. Long text input sniffing: If a simple ping causes input_tokens to exceed 200, it likely indicates large hidden prompts, with fake models over 90% probability.

3. Violation refusal style: Ask illegal questions and observe refusal style. Genuine Claude responds politely and firmly, e.g., “Sorry but I can’t assist…”, while fake models tend to be verbose, include emojis, or use flattering phrases like “Sorry master~”.

4. Function missing detection: Lack of function calls, image recognition, or stable long context suggests a weak model impersonating.

Additionally, some transit hub detection websites can evaluate token “purity,” but note this exposes your key in plaintext. The safest remains official channels.

It’s important to emphasize:

Even if you master detection skills, it doesn’t mean you can fully avoid risks, as many are invisible to ordinary users.

In conclusion

Transit hubs are not the ultimate answer in the AI era; they are more like a temporary arbitrage window caused by mismatches in global model capabilities, pricing, payment conditions, and access rights.

For ordinary users, they may indeed be an affordable entry point to top models; but for developers, teams, and entrepreneurs, the real cost isn’t the token itself but the stability, security, compliance, and trust costs behind it.

Cheap can be copied, interface compatibility can be copied. The truly hard-to-copy part is not price but long-term reliability.

Warm reminder: Ordinary users should only try in non-sensitive, non-critical scenarios, avoiding core data, trade secrets, or personal privacy. Developers should prioritize official APIs or self-made proxies to ensure stability and compliance. Entrepreneurs planning to enter should set clear exit mechanisms in advance to avoid getting trapped in gray areas.

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