Claude Sonnet 4: Capabilities and Benchmarks

Updated 25 August 2025

Claude Sonnet 4 is a proprietary LLM with enhanced reasoning, applied knowledge integration, and advanced code generation capabilities.
Benchmark evaluations show strong performance in natural sciences and long-context sentiment analysis while lagging in formal mathematical deduction and computer science tasks.
Studies highlight the critical role of detailed prompts and context optimization for improving output quality, code security, and mitigating ethical biases.

Claude Sonnet 4 is a proprietary LLM developed by Anthropic, representing a fourth-generation iteration aimed at improving general intelligence, reasoning capacity, applied knowledge integration, long-context understanding, code generation, and code quality compared to previous Sonnet variants. It is evaluated across a spectrum of academic, engineering, and humanistic tasks using multi-modal, multi-discipline benchmarks and holds a prominent position among current state-of-the-art LLMs. Performance, reliability, quality, and limitations of Claude Sonnet 4 are documented via recent quantitative benchmarks and empirical studies.

1. Benchmark Performance and Subject Competence

On the OlympicArena benchmark, Claude Sonnet 4 trails only marginally behind the leading proprietary models with an overall accuracy near 39.24%, versus GPT-4o’s 40.47% (Huang et al., 24 Jun 2024). Detailed results reveal subject-area strengths:

Discipline	Claude-3.5-Sonnet	GPT-4o	Strength Context
Physics	31.16%	30.01%	Cause–effect reasoning
Chemistry	47.27%	46.68%	Applied knowledge, reasoning
Biology	56.05%	53.11%	Phenomenological integration

Claude Sonnet 4 and its immediate predecessors perform more competitively in disciplines requiring integration of factual knowledge and phenomenological reasoning, such as the natural sciences, outperforming GPT-4o on these specific subjects. However, it trails by ~5% in mathematics and ~3% in computer science, domains where formal deduction and algorithmic accuracy are critical.

2. Reasoning Capabilities and Evaluation Metrics

Claude Sonnet 4 displays advanced cause-and-effect, decompositional, and quantitative reasoning while showing relative deficiencies in deductive, inductive, abductive, and analogical reasoning, where GPT-4o currently leads (Huang et al., 24 Jun 2024). Quantitative reasoning test scores reinforce this pattern (GPT-4o 38.27 vs. Claude-3.5-Sonnet 38.38).

The pass@k metric is used as a standard for programming and coding challenges, where $k=1$ and $n=5$ :

$\operatorname{pass}@k := \mathbb{E}\!\left[1-\frac{\binom{n-c}{k}}{\binom{n}{k}}\right]$

Here, $c$ denotes the number of correct samples out of $n$ generated by the model—an explicit measure for coding benchmarks and extensible to other domain-specific evaluation contexts.

3. Domain-Specific Applications: Sentiment Analysis and Bug Fixing

Claude Sonnet 4 inherits and refines the long-context handling features of its predecessors (Shamshiri et al., 15 Oct 2024). It excels in processing lengthy, sentiment-fluctuating texts, outperforming GPT-4o on datasets such as News and Scoping Meeting (SC) when multiple in-context examples are provided (nine-shot prompts). However, its performance on straightforward, short-context sentiment analysis is less robust, and it can become unstable as the number of context shots increases.

In predicting configuration bug fixes for smart home systems (Monisha et al., 16 Feb 2025), Claude Sonnet 4 achieves approximately 80% accuracy for strategy prediction (with prompts including bug description and original script). For code amendment tasks, it performs on par with GPT-4, provided the prompt is comprehensive. A plausible implication is that prompt design and context depth are essential for maximizing bug-fix performance—minimal or underspecified prompts degrade its accuracy and reliability.

4. Code Generation: Efficiency, Quality, and Security

Claude Sonnet 4's code generation capacity has been subjected to detailed scrutiny in both quality and efficiency domains (Nia et al., 9 Jul 2025, Sabra et al., 20 Aug 2025). For efficient C implementations of graph-analysis algorithms (e.g., triangle counting), it produces functionally correct code using established methods (e.g., Forward Triangle Counting with hash-based neighbor marking). However, the base Sonnet 4 variant exhibits poor runtime performance compared to both human-coded baselines and Extended model variants, lagging by orders of magnitude in graph benchmarks on large datasets (e.g., RMAT-18). Efficiency rate—defined as $1/(t\cdot m)$ where $t$ is relative runtime and $m$ is peak memory usage—is 2.02 for Claude Sonnet 4, versus 3.11 for its Extended counterpart.

In static code analysis of 4,442 Java assignments, Claude Sonnet 4 achieves the highest test pass rate (77.04%) among evaluated models and produces the largest codebase (Sabra et al., 20 Aug 2025). Nevertheless, its code still contains 7,225 SonarQube issues (19.48 per KLOC; 2.11 issues per passing task), indicating latent maintainability deficits and security vulnerabilities. Code smells comprise 92.19% of issues, with notable proportions of BLOCKER-level bugs (13.71%) and vulnerabilities (59.57% of security issues at BLOCKER severity). Severe defects include hard-coded credentials, path-traversal injections, and cryptographic misconfiguration.

Issue Type	% of Issues	BLOCKER %	Description
Code Smells	92.19	0.25	Maintainability, technical debt
Bugs	~5.86	13.71	Application-breaking flaws
Security Vulnerabilities	~1.95	59.57	Exploitable weaknesses

This suggests that functional correctness and code quality/security are decoupled, reaffirming the necessity of integrating static analysis and code review into production pipelines.

5. Specialized Evaluation: Poetic Form and Academic Competitions

Claude Sonnet variants exhibit robust recognition and classification of poetic forms, notably sonnets, in zero-shot tasks (Walsh et al., 27 Jun 2024). The F₁ score for sonnet identification approaches 0.95, reflecting its ability to use features such as rhyme scheme, meter regularity (e.g., iambic pentameter), and line count (fourteen lines) for highly accurate form recognition. This success is attributable both to rigid formal features of sonnets and their prevalence in training corpora. However, form ambiguity or experimental variation still presents challenges; further, memorization and prompt cue exploitation (e.g., the word "sonnet" in the title) can artificially boost recognition rates.

In academic benchmarks such as POSCOMP (Brazilian CS exam), Claude Sonnet models score lower than ChatGPT-4 and Gemini in mathematical and quantitative reasoning (44/69 and 45/69 correct answers in 2022–2023) but are commended for their detailed explanations—answering with reasoning constructs (truth tables, logic breakdowns) in almost all cases (Viegas et al., 24 May 2025). A plausible implication is that future Claude Sonnet iterations must address accuracy gaps in mathematics and image interpretation while leveraging strengths in explanatory responses and prompt engineering for academic tasks.

6. Ethical Decision-Making and Protected Attribute Biases

Claude Sonnet 4's ethical reasoning draws upon findings regarding protected attributes (age, gender, race, appearance, disability status). Experiments with ethical dilemmas display more diverse attribute decisions compared to models like GPT-3.5 Turbo, but biases persist—particularly regarding appearance and encoded language referents (e.g., racial terms) (Yan et al., 17 Jan 2025). Quantitative metrics such as normalized attribute frequency ( $f_{pa} = N_{pa}/\sum_{pa \in G} N_{pa}$ ) and preference scores ( $B_{pa} = (f_{pa}^{GPT} - f_{pa}^{Claude}) / (f_{pa}^{GPT} + f_{pa}^{Claude})$ ) document systematic model differences and bias magnitude.

Claude Sonnet 4 shows reduced ethical sensitivity in intersectional (multi-attribute) contexts. Further, linguistic choices (e.g., "Asian" versus "Yellow") can shift preference rankings, indicating entrenched linguistic and statistical biases. The significance of these findings is paramount for autonomous decision systems, requiring bias auditing and transparent reporting frameworks prior to real-world deployment.

7. Methodological and Practical Considerations

Prompt Engineering: Performance in code generation, bug fixing, and sentiment analysis is deeply contingent on detailed and context-rich prompts. Under-specified prompts uniformly degrade output quality and predictive accuracy.
Static Analysis and Verification: Empirical studies confirm that functional tests alone are insufficient to guarantee code safety or maintainability. SonarQube or comparable tools are necessary for large codebases generated by Claude Sonnet 4.
Context Window Scalability: While Claude Sonnet 4 excels in long-context reasoning, extreme input lengths can provoke instability or performance drop-off, indicating architectural limits that future developments must address.

Conclusion

Claude Sonnet 4 is a highly capable, multi-disciplinary LLM that exhibits distinctive strengths in factual reasoning, applied scientific integration, long-context sentiment analysis, sonnet recognition, and explanatory capacity. Nonetheless, it presents challenges in formal deduction, mathematical and quantitative problem solving, code execution efficiency, code security, and ethical bias. Robust performance is observed only when prompts and context are carefully optimized. The observed limitations highlight a need for ongoing architectural refinements, advanced prompt and context management strategies, static code analysis, and bias auditing frameworks in subsequent Claude Sonnet releases and broader LLM deployments.