Baidu: ERNIE 4.5 21B A3B Thinking

Generates multi-step reasoning chains with explicit intermediate thinking steps before producing final answers, using an internal A3B (Adaptive Attention-Based Branching) mechanism that dynamically allocates compute across reasoning depth vs. breadth. The model explicitly models uncertainty and explores multiple solution paths before converging, enabling transparent reasoning traces for verification and debugging of complex logical problems.

Solves mathematical problems including algebra, calculus, geometry, and number theory by combining neural pattern matching with symbolic reasoning capabilities. The model leverages training on mathematical notation, formal proofs, and step-by-step derivations to handle both computational accuracy and conceptual understanding, with particular strength in multi-step problems requiring intermediate symbolic manipulation.

Generates scientifically accurate explanations across physics, chemistry, biology, and earth sciences by synthesizing knowledge from scientific literature and domain-specific training data. The model produces explanations at multiple abstraction levels (conceptual, mechanistic, mathematical) and can contextualize scientific concepts within broader frameworks, making complex phenomena accessible while maintaining technical precision.

Generates code across multiple programming languages (Python, JavaScript, Java, C++, etc.) with explicit reasoning about algorithmic correctness, complexity analysis, and edge cases. The model combines pattern matching from training on open-source repositories with reasoning capabilities to produce not just syntactically correct code but also algorithmically sound implementations, with ability to explain design choices and potential pitfalls.

Answers complex, multi-faceted questions requiring synthesis of knowledge across domains, handling ambiguity, nuance, and context-dependent reasoning. The model produces answers that acknowledge uncertainty, present multiple perspectives on contested topics, and provide reasoning for conclusions, operating at expert-level depth across academic, professional, and technical domains.

Generates diverse text content (essays, articles, creative writing, summaries, paraphrases) with fine-grained control over style, tone, and format. The model supports conditional generation based on style parameters (formal/informal, technical/accessible, concise/detailed) and can maintain consistency across long-form content through attention mechanisms that track narrative coherence and thematic continuity.

Translates text between multiple languages while preserving meaning, context, and nuance, with support for idiomatic expressions and cultural adaptation. The model can also perform cross-lingual reasoning tasks (answering questions in one language about content in another) by maintaining semantic equivalence across language boundaries through multilingual token embeddings and language-agnostic reasoning paths.

Extracts structured information (entities, relationships, attributes) from unstructured text and converts it into machine-readable formats (JSON, tables, knowledge graphs). The model uses reasoning to disambiguate entities, resolve coreferences, and infer implicit relationships, producing structured outputs suitable for downstream processing, database insertion, or knowledge base construction.

Achieves top-tier performance on standardized academic benchmarks (MMLU, GSM8K, MATH, HumanEval, etc.) through combination of broad knowledge, reasoning capability, and domain-specific training. The model is evaluated against expert-level performance standards and demonstrates consistent accuracy across diverse academic domains including mathematics, science, coding, and humanities.