Claude 3: Advanced LLM Suite by Anthropic
- Claude 3 is a family of large language models by Anthropic, offering variants with distinct trade-offs in accuracy, processing speed, and multimodal capabilities.
- They demonstrate competitive performance on text-based examinations, though challenges arise in visual reasoning and language-specific tasks.
- Key improvements are needed in multimodal integration and domain-specific data augmentation to enhance safety, consistency, and real-world applicability.
Claude 3 is a family of LLMs developed by Anthropic, characterized by competitive performance across multiple reasoning, comprehension, and application benchmarks, including multilingual and multimodal tasks. The Claude 3 suite comprises variants such as Claude‑3.5‑Sonnet, Claude‑3‑Opus, Claude‑3‑Sonnet, and Claude‑3‑Haiku, each offering distinct trade-offs in terms of accuracy, processing efficiency, and capability profiles. Claude 3 models have been deployed and evaluated in high-stakes settings, most recently across medical, educational, engineering, and economic domains, where they have demonstrated strengths rivaling those of human experts but also revealed persistent limitations—especially in language-specific and visual reasoning tasks.
1. Performance on Multilingual Medical Reasoning Tasks
The evaluation of Claude 3 variants in the Brazilian medical residency exam (HCFMUSP) reveals high accuracy in text-only questions, with Claude‑3.5‑Sonnet and Claude‑3‑Opus scoring approximately 70–73%, exceeding or matching the peak human candidate distribution (65–70%) (Truyts et al., 26 Jul 2025). Performance degrades when models encounter multimodal questions (those requiring image interpretation, including radiological images), where accuracy falls to 69.57% for Claude‑3.5‑Sonnet, 63.59% for Claude‑3‑Opus, 54.70% for Claude‑3‑Sonnet, and 44.44% for Claude‑3‑Haiku.
| Model | Text-only Accuracy (%) | Multimodal Accuracy (%) | Avg. Processing Time (s) |
|---|---|---|---|
| Claude‑3.5‑Sonnet | ~70.3 | 69.6 | 13.0 |
| Claude‑3‑Opus | ~70.5 | 63.6 | 24.7 |
| Claude‑3‑Sonnet | 72.9 | 54.7 | – |
| Claude‑3‑Haiku | – | 44.4 | 4.1–5.5 |
Processing time analysis shows substantial variance: Claude‑3‑Haiku is the fastest (~4.1–5.5s/question), whereas Claude‑3‑Opus is slower (up to ~24.7s/question), a point important for clinical throughput and scale.
2. Coherence and Safety of Explanations
Claude 3 models generally deliver explanations that are concordant and internally consistent with the selected answers when those answers are correct (Truyts et al., 26 Jul 2025). Correct responses were associated with high inter-observer agreement among clinical evaluators, measured via Gwet’s AC1 statistic. However, when incorrect answers were generated, explanations often exhibited hallucinations or reasoning errors, reducing agreement and potentially leading to unsafe conclusions. This reflects a limitation in assurance for high-risk decision support, suggesting the need for further advances in factual grounding and error detection.
3. Multimodal Reasoning Limitations
Accuracy drops markedly when Claude 3 models are required to process visual inputs, especially in radiological imaging. Even top-performing variants such as Claude‑3.5‑Sonnet lose several percentage points in multimodal tasks, and variants such as Claude‑3‑Haiku show substantial drops (Truyts et al., 26 Jul 2025). This decreased performance is consistent with broader findings in the literature regarding VLM (Vision-LLM) integration challenges, and pinpoints multimodal reasoning—including image processing, contextual fusion, and visual-data augmentation—as a key area for future research.
4. Impact of Linguistic Disparities
Claude 3 models trained primarily on English corpora experience measurable performance gaps when operating in Portuguese-language medical contexts (Truyts et al., 26 Jul 2025). While text-based question accuracy matches or exceeds that of English and Spanish tasks, performance attenuation is observed in multimodal settings and for questions requiring nuanced language understanding, an outcome likely resulting from less well-represented Portuguese medical content in training data. This underscores the necessity of targeted fine-tuning and dataset augmentation for non-English medical domains.
5. Technical Evaluation and Statistical Methodology
Accuracy was defined as the proportion of correct answers over the total questions, shown as
Repeated-measures ANOVA and confidence interval estimation were employed to compare performance across models and conditions. These statistical procedures validated that differences in accuracy among Claude 3 variants and against human baselines were both numerically and statistically significant (Truyts et al., 26 Jul 2025).
6. Recommendations for Model Improvement
Future development for Claude 3 and successor models should prioritize several technical targets:
- Multimodal Integration: Enhanced image reasoning is required, especially for radiological questions.
- Language-Specific Tuning: Augmenting training data with diverse, high-quality Portuguese clinical sources is critical for equitable performance across linguistic contexts.
- Explainability and Safety: Development of more robust factuality and safety monitoring mechanisms will be necessary to ensure that explanations for both correct and incorrect answers maintain coherence and clinical validity.
- Task-Specific Fine-Tuning: Techniques such as Retrieval-Augmented Generation (RAG) are recommended for improved contextualization and accuracy in domain-specific applications.
7. Significance and Trajectory
Claude 3 models have demonstrated competitive or superior performance to human candidates in Brazilian Portuguese medical exams, primarily on text-only questioning. Nevertheless, notable challenges persist in multimodal reasoning, explanation safety, and language adaptation. Addressing these issues through targeted fine-tuning, data augmentation, and methodological innovations will be crucial to advancing the utility, reliability, and fairness of LLMs in global clinical and research settings (Truyts et al., 26 Jul 2025).