Author: Omnitools

AI hubs are evolving from niche tools into broader model entry points. For many users, their appeal is direct: lower prices, more models, unified interfaces, and integration with development tools such as Claude Code, Codex, and Cursor.

But this is precisely where the problem with intermediary stations lies. Users may think they've simply switched to a cheaper API address, but in reality, they might be handing over hints, code, business documents, customer information, call logs, and even the entire development context of the project.

Omnitools believes that discussions about AI intermediary platforms should not stop at "whether they can be used" or "which one is the cheapest." The more important questions are: Where does the demand for these intermediary platforms come from? Do users really need them? If they must be used, how can the risks be controlled?

I. Market Demand Behind Transit Stations

One obvious conclusion is that transit stations are popular because the demand is real.

First, there's the price advantage. Leading large-scale model APIs from overseas aren't cheap. OpenAI's pricing page shows that GPT-5.5 input costs $5 per million tokens, and output costs $30 per million tokens; Anthropic's pricing page shows that Claude Sonnet 4.7 input costs $5 per million tokens, and output costs $25 per million tokens. For ordinary chat, these costs are negligible, but for long text processing, code generation, multi-turn agent tasks, and automated workflows, the cost of invocation quickly becomes perceptible.

The main selling point of the transit station is its API access at a price far lower than the official price. For example, 1 RMB can buy 1 USD worth of tokens, a discount of only about 15% of the official price. This represents a real cost saving for users with high demand.

Secondly, there's the access barrier. As access restrictions for US models to users in mainland China become increasingly stringent, even ignoring price advantages, many users face extremely high authentication barriers to use the official API or packages at the original price. Furthermore, in practical applications, users who want to use Claude, GPT, Gemini, and domestic models simultaneously must switch between multiple platforms. The intermediary platform compresses these complexities into a single entry point, like a "convergent socket" in the AI ​​model world. Users no longer care which line is connected behind the scenes, only whether there's stable power.

Thirdly, the development tools are driving this trend. In the past, models were primarily used for question-and-answer and writing; now, tools like Claude Code, Codex, and Cursor are integrating models into local development workflows. Model invocation is no longer just a chat, but could be a code review, a project refactoring, or an automatic fix. Furthermore, the rise of the "lobster farming" craze has increased the demand for these tokens. The more demanding the tokens, the easier it is for users to find cheaper, higher-value, and more unified integration methods.

Therefore, the booming business of transit stations is driven by real demand, and is not just another fad.

II. Do you really need a transit station?

However, not everyone needs to use a transit station.

If you only occasionally ask questions, translate texts, summarize publicly available information, or write a simple piece of copy, you often don't need a middleman. Models and tools like ChatGPT, Gemini, and Antigravity all have free quotas. If you can't manage the authentication and account setup, you can also choose from many large model aggregators, some of which also offer free quotas for daily use.

For light users, instead of handing over data to unknown intermediaries just for the sake of "cheapness," it's better to use up the free quota of official and legitimate tools first. Free quotas may change, and specific limits should be checked on the official pages of each platform, but this principle remains the same: there's no need to rush to use intermediaries for low-frequency needs.

For heavy programming users, it's not always necessary to delegate all tasks to expensive models or intermediaries. A more prudent approach is to use models in a layered manner: use a stronger, larger model for requirement breakdown, technical roadmap, architecture design, and code review; then use a cheaper, domestically developed model for more specific feature development and daily operation. Furthermore, as domestic models continue to catch up, many are now comparable to top-tier US models in handling daily development, and their prices may be significantly lower than intermediaries. For example, Kimi K2.6's output price is $4 per million tokens, only 13% of ChatGpt 5.5, a price also lower than many intermediary platforms.

Of course, this approach isn't perfect, but it's more in line with cost structure. Complex tasks require strong directional judgment and a robust framework, while the actual implementation can be broken down into multiple low-risk, low-cost smaller tasks. For individual developers and small teams, breaking down the task into smaller parts first and then deciding which stages require high-end models is usually more rational than directly purchasing large transfer fees.

Only when users have continuous, high-frequency, multi-model calling needs—such as long-term use of AI programming tools, processing large amounts of public data, performing model comparisons, and building internal automated processes—and the official quota is clearly insufficient, should the transfer station become an alternative. Even then, it should be a "selected tool," not the default entry point.

III. How to select and use a transit station?

If the assessment confirms the need for a transit station, the next question is no longer "whether to use it," but rather "how to use it without causing problems." Below is a complete operational process from assessment to daily use.

Step 1: Verify authenticity before recharging.

After obtaining a transfer station address, don't rush to recharge. Do three things first:

Verify model authenticity. Use the same prompt to call both the intermediary platform's API and the official API, comparing output quality, response format, and token usage for consistency. Some intermediary platforms might use older models to impersonate newer ones or inject extra system prompts into the output. A simple testing method is to have the model self-report its version information and then cross-compare it with the official behavior. While this cannot completely prevent counterfeiting, it can filter out platforms that are clearly suspicious.

Test latency and stability. Make 20-50 consecutive calls and observe for frequent timeouts, random errors, or fluctuations in response quality. The relay station has an extra layer of connection compared to a direct connection; if basic stability is inadequate, more problems will arise during subsequent use.

Check the documentation quality. A well-run intermediary platform will typically provide complete API documentation, access instructions compatible with OpenAI format, and a clear list of models and pricing. Be wary of platforms that have cobbled-together documentation or vague model lists.

Step 2: Isolate configurations and do not mix them.

Once the platform is confirmed to be basically usable, the next step is technical isolation. Many users skip this step, but it determines the scope of the damage in case of problems.

Use a separate API Key. Do not directly enter the Key you obtained from the official platform into the intermediary station, nor share the same Key across multiple intermediary stations. Generate a separate Key for each intermediary station, so that if one platform encounters a problem, it can be immediately invalidated without affecting other services.

Manage your API keys using environment variables. In your local development environment, store the API key in a .env file or system environment variable; do not hardcode it into your code. For example, when configuring the API Base URL and Key in Cursor settings, ensure these configurations are not committed to the Git repository. If using command-line tools such as Claude Code or Codex, check your shell configuration file to ensure the key does not appear in the version control history.

Set usage limits. Most legitimate token transfer services support setting monthly token limits or spending caps. The first thing to do after topping up is to set these limits. This is not only for cost control but also a safety net; if your key is accidentally leaked, the usage limit can limit losses.

Step 3: Establish data classification habits

Once the technical configuration is complete, the most crucial aspect of daily use is to perform rapid data classification and assessment for each call. While it's not necessary to write a security report for every call, it's essential to cultivate a habit of reflexively checking these aspects.

Before sending, ask yourself this question: If this content appears on a public forum tomorrow, can I accept it?

If the answer is "yes," such as summarizing publicly available information, general translation, technical discussions of open-source projects, or analysis of public documents, then the transfer station can be used directly.

If the answer is "not very likely, but the loss is manageable," such as internal meeting minutes, draft business documents, customer communication templates, or code snippets, then perform an anonymization process before sending them. Specifically, replace names with role codes ("Customer A," "Colleague B"), replace specific amounts with percentages or ranges, replace internal serial numbers with placeholders, and remove database connection addresses, internal API endpoints, and undisclosed business logic descriptions. This process doesn't take long, usually only a minute or two, but it can reduce the risk from "potentially something bad might happen" to "basically manageable."

If the answer is "absolutely not," such as private keys, mnemonic phrases, production environment keys, database passwords, unpublished financial data, customer privacy information, or complete private code repositories, then do not hand them over to any intermediary, no matter how secure it claims to be.

Step 4: AI programming tools should be treated separately.

This point deserves special emphasis because AI programming tools have a much wider data exposure than ordinary conversations.

When you connect a transfer station to tools such as Cursor, Claude Code, and Cline, the model obtains not only the prompts you actively input, but also: the content of currently open files, project directory structure, terminal output history, dependency configuration files (such as package.json and requirements.txt), Git commit history, and file paths and environment variable names in error messages.

This means that what seems like a simple "help me fix this bug" request may actually send far more data to the relay station than you expect.

Operational Recommendations: When using a transit station in AI programming tools, prioritize handling independent code tasks unrelated to core business logic. If you must handle code involving private repositories or production environments, there are two relatively safe approaches: First, only paste anonymized code snippets instead of letting the tool read the entire project directly; second, switch the development of sensitive projects back to the official API or local model, and only use the transit station for non-sensitive projects. Neither approach is perfect, but both are far better than indiscriminately handing over the entire development context to a third-party proxy.

Step 5: Continue monitoring and prepare to exit.

Using a transit station is not a one-time decision, but a continuous evaluation process.

Regularly check your billing records. Confirm that token consumption matches your actual usage. If your usage hasn't increased significantly over a period of time, but the billing speed has accelerated, it may be due to platform adjustments to billing rules or abnormal calls to your key.

Pay attention to platform announcements and community feedback. The operational status of a transit point may change at any time; adjustments to upstream channels, changes in quota policies, and sudden service outages are all possible. If you rely on a particular transit point as your primary access method, you should have at least one backup plan. It is recommended to register on 2-3 platforms simultaneously, maintain a minimum top-up amount, and avoid concentrating all calls on a single channel.

Ensure portability. When configuring the transfer station, use the standard interface in an OpenAI-compatible format. This way, switching platforms usually only requires modifying the Base URL and API Key, without changing the code logic. If your project is deeply tied to a transfer station's private interface or special functions, migration costs will increase significantly, which is a risk that needs to be considered in advance.

Ultimately, a transit station is a tool, not a belief. Its value lies in solving real access needs at a controllable cost, but this "controllability" needs to be defined and maintained by yourself. Through verification, isolation, classification, special handling, and continuous monitoring, you can keep the initiative in your own hands.