Claude 4

Between 2023 and 2024, the performance gap among mainstream large language models such as GPT-4, Claude 3, and Gemini Ultra continued to narrow on benchmarks, making them difficult to distinguish on most general tasks.

Claude 3.5 Sonnet and the Claude 4 series have performed prominently on authoritative benchmarks such as SWE-bench.

Claude 4是Anthropic于2025年发布的旗舰模型

Anthropic在Claude 4系列上采用从4.0到4.8快速迭代的小步快跑策略

Anthropic在Claude 3系列到Claude 4系列的迁移中刻意维护了更高的行为一致性，不像OpenAI那样需要大幅调整提示词

Anthropic Claude 4 is stronger at long-context reasoning, suitable for document analysis and code generation, but the rate limits on lower-tier plans are extremely low and are frequently hit.

In Soroban contract scenarios, a typical integration approach is: code commit triggers the pipeline → a static analyzer (such as a Slither-type tool) does initial screening → Claude 4 extended thinking mode reads the analysis results and the contract source code → outputs risk priorities and fix suggestions → after human review, decides whether to release.

Extended thinking mode internally samples, compares and self-negates candidate hypotheses within the model, gradually converging on more reliable conclusions, similar to human scratch-paper thinking, but entirely completed autonomously by the model.

According to tests by veteran AI application practitioner André Dias Moreira Prol, in Stellar blockchain contract analysis, extended thinking mode markedly reduced false positive rates in detecting reentrancy vulnerabilities and authorization flaws.

Traditional prompts tend to give generic fix suggestions, while extended thinking traces the contract state flow and precisely locates the failure point in the authentication verification logic.

In actual projects, Claude 4 is integrated via API into the CI/CD pipeline of Soroban contracts, automatically completing the security review step before testnet deployment.

In real-world asset tokenization (RWA) scenarios, Claude 4 can simultaneously model the legal structure and the technical architecture, suggesting a contract solution that both meets CVM compliance requirements and takes into account Stellar network gas efficiency.

Rule of thumb: one should measure the "value density" of each decision to decide whether to use extended reasoning—a $50,000 asset tokenization requiring compliance checks is worth using it, while an FAQ chatbot is not.

Extended reasoning should not be used as a routine default mode, but reserved for genuinely difficult problems (such as cryptographic verification, forensic timeline reconstruction, tokenomics modeling), while routine summarization tasks should continue to use standard mode.

For most teams, a reasonable approach is to first build a query complexity classification layer, and then enable deep thinking mode for high-value decisions.

According to the author André Dias Moreira Prol's measured data, when analyzing Soroban smart contracts, enabling a 10,000 token thinking budget raised the accuracy of multi-step contract logic audits from about 71% to 89%.

A query that takes 2 seconds in standard mode may become 25 seconds in deep reasoning mode, while consuming 5 to 10 times the tokens.

In a tokenization project, the author designed a two-tier routing architecture (lightweight classifier + extended reasoning), bringing the usage rate of reasoning mode down to about 15% of total requests, with monthly reasoning costs falling nearly 40% compared to using deep mode for everything.

Claude 4's extended reasoning can be combined with tool use, and the model is able to reason about when to call functions, interpret results, reason again, and coherently chain actions over long spans.

The prototype agent the author built to monitor Stellar on-chain activity maintained coherence across a dozen steps, whereas earlier models would fail after the fifth or sixth action.