The Complete Guide to Using Claude in China: In-Depth Comparison of 4 Solutions and How to Choose

For users in China, smoothly accessing Claude (Anthropic's flagship large language model) has always been an unavoidable challenge. Overseas credit cards, a clean network environment, account suspension risks, payment channels — layer upon layer of barriers have deterred many AI enthusiasts. Based on a comprehensive research review by Bilibili creator "AI Navigator," this article systematically breaks down the four mainstream solutions currently available — from official subscriptions to relay platforms to API aggregation — aiming to help users with different needs find the path best suited to them.

Background: Anthropic and the Claude Model Family Founded in 2021 by former OpenAI VP of Research Dario Amodei and Head of Safety Daniela Amodei, Anthropic's core mission is to build "interpretable and trustworthy" AI systems. The Claude series is built around the Constitutional AI training framework, which constrains model behavior through built-in value principles rather than relying solely on Reinforcement Learning from Human Feedback (RLHF). Constitutional AI (CAI), introduced by Anthropic in 2022, is an alignment training method whose core idea is to have the model internalize a set of explicit rules (the "constitution") during training. Training proceeds in two stages: in the first stage, the model critiques and revises its own outputs according to the constitutional principles, generating new responses more aligned with these values; in the second stage, AI feedback (RLAIF) replaces part of the human feedback, with the model itself scoring candidate responses against the constitutional clauses, then optimizing the policy model via reinforcement learning. The advantages of this approach are stronger interpretability (rules are explicitly stated) and better scalability (reduced dependence on human labeling), and it is the underlying reason why Claude models can often provide clear justifications when refusing harmful requests. The current Claude 3.x series is divided into three capability tiers: the lightweight and fast Haiku, the balanced workhorse Sonnet, and the flagship reasoning model Opus — each corresponding to a different cost-performance trade-off.

Solution 1: Official Account Subscription — Best Experience, Highest Barrier

Direct Official Access: The Ceiling of Intelligence

When it comes purely to model capability and feature completeness, direct official access is undoubtedly the optimal choice. Core capabilities like Claude Code, Artifacts, and remote mobile control can only be fully obtained through the native app and web interface.

Claude Code is a command-line-level AI coding agent from Anthropic. Unlike ordinary code-completion tools, it can autonomously read and write files in the local file system, execute terminal commands, and perform multi-step debugging — essentially an Autonomous Agent capable of "operating a computer." Its underlying architecture uses a Tool Use / Function Calling mechanism, allowing the model to run a ReAct (Reasoning + Acting) loop of "perceive → plan → act → observe" within a single task, breaking down complex programming tasks into multiple sub-goals to be completed one by one.

Worth understanding in depth: the ReAct framework was jointly proposed by Google and Princeton researchers in 2022. Its core idea is to interleave "Thought" and "Action" into sequences — the model first outputs a piece of reasoning analysis, then executes a tool call, observes the result, continues reasoning, and repeats this loop until the task is complete. The "tools" Claude Code calls include file read/write APIs, terminal command executors, code-running sandboxes, and more. The key difference from traditional IDE plugins (like GitHub Copilot) is this: the latter performs single-shot completions, while the former executes continuous closed-loop autonomous tasks, capable of self-correction and cross-file refactoring. Understanding this architecture helps predict Claude Code's behavioral boundaries: the more clearly defined the task and the more atomic the tool calls, the higher the quality of the agent's output.

Artifacts is Claude's official "executable content sandbox" feature, allowing users to preview and run Claude-generated HTML/CSS/JavaScript code, SVG graphics, React components, and more directly within the conversation interface — completing the full loop from generation to verification without leaving the web page. Both of these features depend deeply on Anthropic's native platform architecture, and API calls alone cannot fully replicate them — this is the fundamental reason why direct official access crushes all relay solutions in terms of feature completeness.

But this path also has the highest barrier of all solutions. The first hurdle is payment. The official site runs through the Stripe channel — Stripe is currently one of the most mainstream online payment infrastructures in the world, used as a subscription billing channel by nearly all Silicon Valley AI companies including Anthropic, OpenAI, and GitHub. Stripe's fraud-prevention system, Radar, uses machine learning models to evaluate hundreds of risk dimensions in milliseconds at the moment each transaction occurs.

Understanding Stripe Radar's fraud logic from a technical perspective helps diagnose failures: Radar has a built-in IP reputation database that precisely distinguishes residential broadband IPs, commercial data-center IPs (such as AWS or Cloudflare exit nodes), and known VPN exit IPs — the latter two are flagged as high risk. The "consistency score" between the billing address and the IP's geographic location is another core dimension; when the two differ by thousands of kilometers, the fraud probability score rises significantly. The card BIN (the first 6-8 digits of the card number, which encode the issuing bank, card tier, and issuing country) and its historical fraud-rate performance in Stripe's data also directly affect approval rates. Additionally, contradictory device-fingerprint signals — such as a browser language set to Chinese and a timezone of UTC+8 but an IP showing a US node — will greatly increase the risk score.

Notably, Device Fingerprinting is one of Stripe Radar's core identification mechanisms. By collecting dozens of dimensions — browser version, operating system, screen resolution, installed font list, Canvas rendering characteristics, WebGL hardware information, and more — it generates a near-unique identifier for each device without relying on cookies. When the same device fingerprint attempts multiple cards from different sources in a short period, or when a fingerprint has a history of disputed transactions (chargebacks), Radar directly adds the device to a high-risk list. This means that even if you switch IPs and payment cards, if your device environment isn't reset accordingly, the failure rate remains high. Even more critically, Anthropic can configure additional custom blocking rules in its Stripe backend, directly blocking specific IP ranges or BIN ranges — a form of proactive platform-level compliance control. This is the fundamental reason why failures still occur even with high-quality proxies. The second hurdle is the network environment: without a sufficiently clean IP, international payments are equally hard to complete. The combination of the two makes the probability of "failing all at once" the highest among all solutions. Therefore, for ordinary users, directly charging at the official site is not recommended.

Claude Proxy Subscription Platforms: A Compromise Through Compliant Channels

If you still want to retain the full experience of a genuine official account and are only stuck on payment and network issues, then a proxy subscription platform is a viable option. One concept must be clarified here: a proxy subscription is not a relay. A proxy subscription simply helps you activate membership on your own Claude account; after activation, you still return to the official Claude site to use it.

According to the review, many users in China use WildCard (Wild AI). It operates through compliant international banking channels and supports WeChat Pay and Alipay. Interestingly, while most platforms in China only support Plus proxy subscriptions, WildCard supports both Plus and Pro subscriptions — because only this way can a single large-amount payment be completed.

More importantly, it offers clear account-suspension refund terms. Compared to individual top-up services on Xianyu (where sellers "take no responsibility after activation"), its after-sales guarantee is much clearer. The whole process is also simple: go to the official site, choose a plan and duration, submit your account details, scan the WeChat QR code to pay, and refresh the official site to see the membership activated — the entire process takes under 5 minutes.

Solution 2: Conversational Model Relay — A Convenient Choice for Multi-Model Switching

PoE and the International Payment Barrier

The biggest advantage of conversational model relay platforms is "one-stop" access: users only need to subscribe once to use all mainstream models — Claude, GPT, Gemini, and more — on the same platform. This suits users who frequently switch between models but only need basic features like chat and Projects.

PoE is an AI aggregation platform under Quora. Its advantages are stability and a rich model selection. But it uses a credit system, and high-end models like Opus have very high consumption multipliers, burning through tokens extremely fast.

It's worth explaining: a token is the basic unit of measurement large language models use to process text. Tokens are produced by splitting text using algorithms like Byte Pair Encoding (BPE) or SentencePiece. The BPE algorithm counts co-occurrence frequencies at the character level, iteratively merging high-frequency byte pairs to ultimately form a fixed-size vocabulary (typically 100,000-200,000 tokens). Token efficiency varies significantly across languages: because vocabulary design prioritizes English, whole English words are often encoded as a single token — roughly 4 characters equals about 1 token on average. Chinese typically corresponds to 1 token per 1-1.5 characters, but rare characters, neologisms, or specialized terms may be split into multiple byte-level tokens, causing actual consumption to far exceed expectations — for the same content, a Chinese conversation typically consumes 1.2-1.5 times as many tokens as its English equivalent.

This gap has a deeper underlying mechanism in practice: the vocabularies of most LLMs are trained on predominantly English corpora, giving Chinese naturally lower vocabulary coverage. When the model encounters a Chinese word not in its vocabulary, it falls back to byte-level encoding, breaking a single Chinese character into 3 UTF-8 bytes and treating each as a separate token — this is especially pronounced when inputting lengthy Chinese technical documents. Additionally, Chinese punctuation (such as 「」、，。) typically each occupies a separate token, while English punctuation is often merged into the token of an adjacent word. This means that even with the same character count, Chinese text has lower "density" at the token level, requiring more tokens to convey the same amount of information. API billing is usually measured per million tokens (MTok), split into two independently priced parts: "input tokens" (the content you send to the model, including system prompts and context history) and "output tokens" (the content the model returns). Output token prices are typically 3-5 times that of input tokens — because generation is autoregressive, decoding token by token, its computational cost is far higher than the single forward pass of prompt processing. As a flagship reasoning model, Claude Opus already costs several times more than Sonnet, so it burns through credits especially quickly on credit-based platforms. More critically, PoE still requires international payment capability and a VPN, so the usage barrier is not fully resolved.

Domestic Relay Platforms: The 2233-Type Solution

What compensates for PoE's shortcomings are domestic Claude relay platforms like 2233. They support WeChat Pay and don't require a VPN, so if you simply want to use Claude directly, these solutions have the lowest barrier to entry. Users get independent accounts and chat histories, and plans are finely divided into daily, monthly, and quarterly cards, with overall prices below one-half to one-third of official rates — making it a core choice for low-cost experience.

Moreover, these platforms use the official native interface, and their UI strives to stay consistent with the official site. Recently, they've also gradually rolled out features previously exclusive to the official web version, such as code preview, Artifacts, selection ranges, and image editing — making the overall experience quite close to the official site.

However, one must be clear-headed: it is still fundamentally a third-party relay, not your own dedicated official Claude account. Although the risk of account suspension and data loss is transferred to the relay platform, you may also face issues like information being monitored or the platform going bankrupt and disappearing. So be sure to choose long-term, stable, seriously operated platforms.

Solution 3: Coding Model Relay — The Value Choice for Developers

AnyRouter: New-User Giveaways but Higher Prices

For developers who mainly write code, token relay is a more suitable solution. The most frequently mentioned options are AnyRouter and 0011.ai.

AnyRouter focuses on user acquisition, gifting $50 worth of tokens directly after registration, with further bonuses for inviting new users. But once the free allowance is used up, its API prices are basically at official rates, and some models are even priced at old rates — for example, the Claude 3.5 series still lists input/output at $1 and $5 per million tokens, whereas Anthropic reduced prices for this series to $0.8 and $4 during its major price-cut cycle from late 2024 to 2025, making it about a quarter more expensive than official pricing. It has also frequently experienced outages recently, most likely due to too many freebie hunters causing insufficient compute supply.

0011.ai: A Beginner-Friendly Claude Code Relay

A more stable domestic option is 0011.ai, with prices as low as a quarter of the official site. It supports WeChat Pay and offers pay-as-you-go quotas or token packages. Its API Key can be used directly in Codex and Claude Code Agents, and it also provides proprietary client interfaces close to the native Codex and Claude Code — making it one of the few beginner-friendly Coding Agent relay solutions.

According to the review, in a comparison on the same game-development task, 0011.ai and native Codex showed largely consistent basic development styles and code structures, with equally good long-context performance. As a relay platform, this level of quality is quite impressive.

Solution 4: API Aggregation Platform OpenRouter — A Powerful Tool for Developers' Cross-Comparison

OpenRouter is the world's largest AI API aggregation platform. Founded in 2023 by a former Coinbase engineer, its business model is similar to a "compute broker" in cloud computing: it signs API resale agreements directly with AI labs like Anthropic, OpenAI, Google, Meta, and Mistral, and provides services through a unified OpenAI-compatible format (i.e., the /v1/chat/completions interface specification). Developers only need a single API Key to access hundreds of models.

This design draws on the architectural approach of a "Multi-Cloud Abstraction Layer" in cloud computing. The core pain point of traditional multi-cloud management is that each vendor's API interfaces, authentication methods, and billing units are mutually incompatible, requiring substantial code rewrites to switch providers. By uniformly wrapping all models into the OpenAI Chat Completions format, OpenRouter essentially plays the role of a "protocol conversion gateway" — shielding upper-layer applications from underlying model differences, allowing developers to freely switch between Claude, GPT-4o, and Gemini 1.5 Pro without modifying any business code. This belongs to the same design paradigm as Kubernetes' abstraction of underlying container runtimes and Terraform's abstraction of cloud resources: reducing vendor lock-in risk through a standardized interface layer. From a business-model perspective, OpenRouter's core logic is bidirectional monetization through "compute arbitrage + traffic aggregation": on the supply side, it leverages economies of scale to obtain wholesale prices from AI labs, then sells them slightly above wholesale; on the demand side, it aggregates large volumes of developer traffic to form bargaining leverage. The platform also supports "model routing": developers can configure automatic primary/backup model switching strategies, so when the primary model returns an error or times out, it automatically falls back to the backup model. It also displays real-time price differences, TTFT (time to first token), and availability status of each model across different providers (such as Together AI, Fireworks AI, Groq, etc.) — a publicly available, objective source of benchmark data, very convenient for conducting model benchmarking, product testing, or configuring backup models for applications. The platform currently hosts over 300 models, covering the full ecosystem from consumer-grade open-source models (like Llama 3) to top-tier commercial flagships.

However, it mainly provides APIs rather than a complete official Claude account. Although you can connect it to Codex, Claude Code, or third-party clients, some official-site-exclusive capabilities still can't be obtained. When topping up, it adds roughly 0-10% platform service fees on top of official API prices, and the minimum handling fee for small top-ups will significantly raise the effective overall rate — so it's better suited to professional developers with high-frequency calls and large monthly consumption. On the payment side, it currently doesn't support Alipay, only international credit cards.

Additionally, when choosing a reasoning model, don't look only at the token unit price — reasoning modes (like Claude's Extended Thinking mode) are an internalized version derived from the Chain-of-Thought (CoT) prompting technique. Before giving a final answer, they first generate an invisible draft of thinking (Thinking Tokens) in an "internal reasoning space," then output the final answer after self-verification and correction.

From a technical-principles standpoint, Extended Thinking mode allows the model to perform self-critique, counterfactual verification, and multi-path exploration during the thinking stage — equivalent to dynamically increasing the model's "effective parameter utilization" at inference time. Understanding the essence of this mechanism helps anticipate its cost structure: in traditional inference (Standard Mode), the model's computation graph is a single forward pass — input tokens go through the attention computation of each Transformer layer and directly output prediction probabilities, an entirely deterministic one-way flow. Extended Thinking, by contrast, essentially opens a "scratchpad buffer" in the model's output space, allowing multiple iterative intermediate reasoning steps. Although these intermediate steps are invisible to the user, they are fully equivalent to final output tokens at the billing level. This means the "deeper" the model thinks, the more the user pays, and this consumption is highly uncertain — the same prompt may produce significantly different numbers of Thinking Tokens across different runs. This mechanism is highly effective for complex mathematical reasoning, multi-step logical planning, and long code-generation tasks, but at the cost of dramatically expanded token consumption — Thinking Tokens are billed at the same unit price as ordinary output tokens (about $3 per million tokens for Claude 3.7 Sonnet). In a task involving multi-step mathematical proofs or large-codebase refactoring, Thinking Tokens can account for up to 70%. Taking a complex programming task as an example, thinking tokens may account for 60%-70% of total consumption — meaning that even though the unit price appears the same, the actual cost of completing a full task can be 3-5 times that of standard mode. It's advisable for developers to set a max_tokens limit or enable Extended Thinking only for specific routes in production, and to monitor actual consumption in the Anthropic Console's token-usage dashboard before deciding whether to enable it across the board.

Conclusion: Four Solutions, Three Core Paths

Overall, the four Claude-in-China solutions essentially reduce to three core paths:

• You want the full experience of a genuine official account and can accept some risk: Go with Solution 1's compliant proxy subscription (like WildCard), which supports WeChat Pay and offers refunds for account suspensions.
• You don't write code and just want to open a web page and use it: Go with Solution 2's 2233-type relay, which has the lowest barrier and prices around half of the official site.
• You write code and need to use Claude Code and Codex: Go with Solution 3's 0011.ai — no command line required, one payment covers two agents, and it can even be shared.
• Developers with international payment methods who need frequent model benchmarking: Consider Solution 4's OpenRouter.

Each solution has a clear target audience and potential risks. When choosing, make a rational judgment based on your own payment capability, network environment, and use case — and be especially wary of third-party platforms' information security risks and the risk of them disappearing with your money.

Key Takeaways