Claude Opus 4.5 Tutorial: Real-World Coding Tests + How to Access from China Without a VPN

Why Claude Opus 4.5 Is Called the Most Powerful Coding Model

Anthropic recently released its latest flagship model, Claude Opus 4.5. Before its arrival, the previous flagship Claude Sonnet 4.5 was already going toe-to-toe with Google Gemini 2.5 Pro and OpenAI GPT-5.1 in the coding domain, with many developers considering it their undisputed "No.1 for programming."

Anthropic's Claude model family uses a clearly tiered naming system: Haiku is positioned as lightweight and fast, Sonnet offers a balance of performance and cost, and Opus is the flagship that spares no expense in pursuit of peak performance. This naming convention based on musical and poetic forms hints at the progression of capabilities from concise to grand. The version number for each model generation (e.g., 4.5) represents a major iterative upgrade on the same architectural foundation. Notably, Anthropic was co-founded in 2021 by siblings Dario Amodei and Daniela Amodei, former OpenAI Research VP and VP of Operations respectively, with a core mission of building safe, controllable AI systems. Anthropic employs a Constitutional AI training methodology, where the model critiques and corrects itself based on a set of predefined behavioral principles, thereby reducing harmful outputs. The Claude model family started from version 1.0 and has evolved through Claude 2, Claude 3, and now the 4.5 generation, with each iteration delivering significant improvements in reasoning capability, context window length, and instruction-following precision.

Claude Opus 4.5 widens the gap even further—with notable improvements in both coding and writing capabilities, solidifying its position at the top of the AI coding landscape. It's currently one of the primary models powering the AI code editor Cursor, which speaks volumes about how the industry regards it. Cursor is an AI-native code editor deeply built on top of VS Code, developed by Anysphere. It deeply integrates large language models into the coding workflow, supporting code completion (Tab), inline editing (Cmd+K), multi-file conversations (Chat), and fully automated coding agents (Agent). Cursor's core competitive advantage lies in its context awareness—it automatically indexes entire code repositories, enabling AI models to understand a project's complete architecture and dependency relationships, resulting in more accurate code generation. Claude Opus 4.5 serving as one of Cursor's primary models means it has reached production-grade standards in code comprehension depth, context window utilization efficiency, and instruction-following precision.

From a broader industry perspective, the AI coding assistant space currently features a three-way competition: GitHub Copilot, built on OpenAI's Codex models, is deeply integrated into VS Code and JetBrains IDEs with the largest user base; Google's Gemini Code Assist leverages the Gemini model series, offering advantages in code comprehension and multimodal capabilities; while Cursor differentiates itself by combining top-tier models like Claude with repository-level context awareness for superior code generation accuracy in complex projects. Additionally, Amazon CodeWhisperer, Sourcegraph Cody, and other products occupy their own niches in specific scenarios. Claude Opus 4.5 standing out in this fiercely competitive landscape further validates its technical prowess.

How to Use Claude Opus 4.5 in China (Two Detailed Solutions)

The question most Chinese developers care about is: How exactly can I use Claude Opus 4.5? Here are two viable approaches.

Option 1: Direct Access via Claude's Official Website

The most straightforward approach is to visit Claude's official website (claude.ai) and click the "Try Claude" button to get started. However, the barriers are significant:

• Requires a Google account for registration
• Requires a VPN/proxy setup
• Subscription costs $20 per month (approximately ¥145 RMB)

What's worse, the official site poses a high risk of account bans for users in China—you could pay for a subscription only to have it vanish without warning. This path isn't recommended for users in China.

Option 2: Access via Third-Party Platforms Without a VPN (Recommended)

There are currently several third-party platforms that provide stable access by connecting to multiple Claude Pro accounts through load balancing technology. Load Balancing is a core technique in distributed systems that distributes user requests across multiple backend service nodes to prevent single-point overload. In this context, these platforms typically maintain a pool of Claude Pro accounts and use load balancing algorithms (such as round-robin, least connections, or weighted distribution) to intelligently route user conversation requests to different accounts, thereby bypassing single-account rate limits while improving overall service availability and response speed.

The advantages of these platforms are clear:

• No VPN required—direct connection from Chinese networks
• Supports the latest Claude Opus 4.5 model
• Also supports the previous generation Claude Sonnet 4.5
• Supports Extended Thinking (deep reasoning) mode
• Supports Web Search (real-time internet search) functionality
• Supports Research (in-depth exploration) functionality

Extended Thinking is a reasoning enhancement mechanism introduced by Anthropic, conceptually similar to OpenAI's Chain-of-Thought—it allows the model to engage in a longer internal reasoning process before delivering its final answer. This mechanism is particularly suited for mathematical proofs, multi-step programming logic, and complex decision analysis. During the "thinking" phase, the model self-verifies, corrects errors, and optimizes its approach, significantly improving the accuracy and completeness of the final output. The Chain-of-Thought concept was first systematically proposed by Google Brain's team in a 2022 paper, which found that guiding models to show their reasoning step by step could improve accuracy on math and logic tasks by dozens of percentage points. Extended Thinking builds on this by allowing the model to allocate more computational resources (i.e., a larger token budget) for internal reasoning—essentially a "test-time compute" strategy that trades reasoning time for output quality.

For users who don't want to deal with network environment configurations, this is currently the most hassle-free solution for using Claude in China.

Real-World Testing: How Strong Are Claude Opus 4.5's Coding and Writing Abilities?

Specs on paper mean nothing—let's look at actual tests. We evaluated Claude Opus 4.5 across three dimensions: writing, bug fixing, and data visualization.

Chinese Writing Ability: Long-Form Content Generated in One Shot Without Translation Artifacts

Test task: Ask Claude Opus 4.5 to write a summary article about its own release, requiring accessible language and approximately 2,000 Chinese characters. This mirrors the daily content production workflow of many content creators.

The results were impressive—the generated article was titled "Claude Opus 4.5 Release: AI Assistant Gets a Major Upgrade, Coding Capabilities Top Human Engineers," with clear structure, smooth language, and no obvious "translation artifacts" in the Chinese text. "Translation artifacts" refer to the phenomenon where AI models generate Chinese that carries obvious English sentence structure patterns—such as overuse of passive voice, nested long attributive clauses, and conjunctions used in un-Chinese ways. Claude Opus 4.5's improvement in Chinese writing likely stems from Anthropic increasing the proportion of high-quality Chinese corpus in training data and introducing more native Chinese annotators' preference data during the RLHF (Reinforcement Learning from Human Feedback) phase. For creators who need to quickly produce technical articles, this writing quality is ready to use out of the box.

AI Code Bug Fixing: From Diagnosis to Solution in One Step

Test task: Present Claude Opus 4.5 with a real-world development scenario—Java throws an exception when reading from MongoDB. How to fix it?

This is an extremely common problem in real development. MongoDB is currently one of the most popular NoSQL document databases, and Java applications typically interact with it through the MongoDB Java Driver or Spring Data MongoDB framework. NoSQL (Not Only SQL) is a category of data storage solutions alternative to traditional relational databases, mainly including document stores (MongoDB), key-value stores (Redis), column-family stores (Cassandra), and graph databases (Neo4j). MongoDB stores data in BSON (Binary JSON) format, supports flexible schema design, and is particularly suited for fast-iterating internet applications. Its replica set mechanism achieves high availability through primary-secondary replication, while sharded clusters provide horizontal scaling. Common read exceptions include: BSON type mismatches (e.g., the database stores a string but the Java entity class defines it as integer), missing codecs, timeout exceptions due to connection pool exhaustion, and read failures caused by improper replica set read-write separation configuration. Whether an AI can precisely identify the root cause of such issues is a key metric for evaluating the practicality of AI coding assistants.

Claude Opus 4.5's response was highly professional, presenting a three-step solution approach:

1. Precise root cause identification: Rather than giving generic advice, it directly analyzed the specific cause of the exception
2. Three different solutions: Provided differentiated fix strategies for different scenarios
3. Complete, ready-to-use code: Each solution included detailed code examples that could be copied and run immediately

From problem analysis to solution design to code implementation, this complete chain demonstrates Claude Opus 4.5's powerful practical utility in real development. Compared to previous models, the quality of generated code is noticeably higher, requiring almost no secondary modifications.

Data Visualization Dashboard Development: Fully Automated from Data Collection to Page Generation

This test was the most impressive. The task was to have Claude Opus 4.5 create a "BYD Auto Sales Ranking Dashboard for January-November 2025."

The entire process was fully automated:

• First used the web search function to query 10 web pages and scrape real sales data
• Then automatically generated complete dashboard code
• Provided a real-time JSX preview on the right side with instant visual results
• Included line charts, pie charts, bar charts, monthly sales rankings, and other visualization components

Some technical background is worth explaining here: JSX (JavaScript XML) is the core syntax extension of the React framework, allowing developers to write HTML-like UI structures directly in JavaScript code. Claude's Artifacts feature supports JSX real-time preview, meaning the React component code generated by the model can be instantly rendered into interactive interfaces. In the data visualization field, commonly used frontend libraries include ECharts, D3.js, Chart.js, and Recharts, which provide rich chart components like line charts, pie charts, and bar charts. D3.js is a low-level visualization library offering fine-grained control over SVG and Canvas; ECharts, open-sourced by Baidu, rapidly generates rich charts through configuration-driven approaches and is extremely popular among Chinese developers; Recharts is a declarative chart library designed specifically for the React ecosystem. When generating data dashboards, Claude typically selects these mature visualization libraries to build charts and uses CSS Grid or Flexbox layouts to arrange multiple charts in a dashboard format. Data dashboard development also typically involves responsive layout adaptation, real-time data stream integration (WebSocket or SSE), and rendering performance optimization for large datasets (such as virtual scrolling and Canvas rendering instead of SVG).

From data collection to visualization rendering, the entire process was seamless and took less than two minutes. This kind of full-stack development capability would have previously required a frontend engineer at least half a day to complete.

Which Use Cases Is Claude Opus 4.5 Best Suited For?

Based on our testing, Claude Opus 4.5's API model identifier is claude-opus-4.5-20251101, and it is currently Anthropic's most premium flagship model. Its strongest use cases include:

• Daily coding and development: Code generation, bug fixing, architecture design, code review
• Content creation and writing: Technical blogs, product documentation, marketing copy, creative writing
• Complex reasoning and analysis: Data analysis, logical reasoning, option comparison, problem diagnosis
• Full-stack application development: Frontend and backend code, data visualization, API integration, prototype development

In short, if you're a programmer or content creator, Claude Opus 4.5 can cover over 80% of your daily work needs.

Conclusion: Is Claude Opus 4.5 Worth Using?

The release of Claude Opus 4.5 has genuinely raised the ceiling of AI coding assistants yet again. From our hands-on testing, whether it's everyday bug fixing, technical article writing, or complex data visualization dashboard development, its performance is unquestionably top-tier.

For developers in China, using a third-party platform for VPN-free access is currently the most practical solution—it eliminates the hassle of network setup and account management while providing access to the latest model capabilities.

However, two reminders:

1. Mind data security: When using any third-party platform, avoid entering your company's core code or sensitive information in conversations
2. Code still requires human review: While AI-generated code quality keeps improving, it must go through testing and Code Review before being deployed to production. Code Review is a critical quality assurance step in software engineering, typically involving other developers on the team conducting line-by-line code inspection, focusing on logical correctness, security vulnerabilities, performance bottlenecks, and coding standards. Even if AI-generated code appears syntactically and functionally correct, it may still contain issues like improper boundary condition handling, resource leaks, or incompatibility with existing system architecture—making human review an indispensable step for the foreseeable future.

The security risks of AI-generated code deserve special attention. A 2023 Stanford University study showed that developers using AI coding assistants produced code with a significantly higher proportion of security vulnerabilities compared to a control group not using AI, and developers tended to be overconfident about the security of AI-generated code. Common risks include SQL injection, Cross-Site Scripting (XSS), hardcoded credentials, and insecure deserialization. When enterprises adopt AI coding tools, they typically need to complement them with SAST (Static Application Security Testing) and DAST (Dynamic Application Security Testing) tools for automated security scanning, and establish dedicated AI code review processes to ensure AI improves efficiency without introducing new security vulnerabilities.

No matter how powerful AI tools become, they ultimately serve humans. Making good use of Claude Opus 4.5's coding and reasoning capabilities is the key to truly boosting development efficiency.