{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking","slug":"baidu-ernie-4.5-21b-a3b-thinking","name":"Baidu: ERNIE 4.5 21B A3B Thinking","type":"model","url":"https://openrouter.ai/models/baidu~ernie-4.5-21b-a3b-thinking","page_url":"https://unfragile.ai/baidu-ernie-4.5-21b-a3b-thinking","categories":["model-training","testing-quality"],"tags":["baidu","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$7.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_0","uri":"capability://planning.reasoning.extended.reasoning.chain.of.thought.generation","name":"extended-reasoning-chain-of-thought-generation","description":"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.","intents":["I need the model to show its work step-by-step when solving a math problem so I can verify correctness","I want to understand how the model arrived at a conclusion for a logic puzzle or coding problem","I need reliable reasoning for scientific explanations where intermediate steps matter for accuracy","I want to debug why a model gave a wrong answer by inspecting its reasoning process"],"best_for":["AI researchers and engineers building reasoning-critical applications","Educational platforms requiring transparent problem-solving explanations","Teams building verification systems for LLM outputs in STEM domains","Developers creating chain-of-thought prompting systems for complex tasks"],"limitations":["Reasoning chains increase latency by 2-5x compared to direct-answer models due to intermediate token generation","Extended thinking may produce verbose or redundant reasoning steps for simple queries","A3B mechanism is proprietary and not interpretable — cannot directly inspect or modify branching heuristics","Reasoning depth is bounded by context window and token limits; very complex problems may be truncated"],"requires":["API access to Baidu ERNIE platform or OpenRouter integration","Support for streaming or full-response retrieval of thinking tokens","Sufficient context window to accommodate both reasoning and final output (typically 8K+ tokens recommended)"],"input_types":["text","natural language problem statements","code snippets with debugging requests","mathematical equations and logic puzzles"],"output_types":["text with embedded reasoning traces","structured reasoning chains (if parsed)","final answers with justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_1","uri":"capability://planning.reasoning.mathematical.problem.solving.with.symbolic.reasoning","name":"mathematical-problem-solving-with-symbolic-reasoning","description":"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.","intents":["I need to solve complex math problems with correct intermediate steps and final answers","I want to verify mathematical derivations or check proof correctness","I need to generate math homework solutions with explanations for educational purposes","I want to use the model as a symbolic math assistant for research or engineering calculations"],"best_for":["Educational technology platforms and tutoring systems","STEM researchers and engineers needing mathematical verification","Content creators building math problem databases with solutions","Students and educators seeking step-by-step mathematical explanations"],"limitations":["May produce correct intermediate steps but incorrect final answers for very complex multi-step problems (typical LLM arithmetic brittleness)","Cannot perform arbitrary-precision symbolic computation like Mathematica or SymPy; relies on learned approximations","Struggles with novel mathematical notations or non-standard problem formulations outside training distribution","No guarantee of proof correctness for formal mathematics; suitable for verification aid, not formal verification"],"requires":["Clear, well-formatted mathematical problem statements (LaTeX or plain text)","API access to ERNIE-4.5-21B model via Baidu or OpenRouter","Optional: external symbolic math library (SymPy, Mathematica) for validation"],"input_types":["text","mathematical equations (LaTeX or ASCII notation)","word problems with numerical context","proof sketches or derivation fragments"],"output_types":["text with step-by-step solutions","mathematical expressions and equations","numerical answers with confidence indicators"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_2","uri":"capability://text.generation.language.scientific.explanation.and.knowledge.synthesis","name":"scientific-explanation-and-knowledge-synthesis","description":"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.","intents":["I need to explain a scientific concept to a non-expert audience without losing accuracy","I want to understand the mechanisms behind a physical or chemical process","I need to generate scientifically sound educational content for a course or textbook","I want to synthesize knowledge across multiple scientific domains to understand interdisciplinary phenomena"],"best_for":["Science educators and curriculum developers","Science communicators and technical writers","Researchers needing literature synthesis and concept explanation","Educational platforms and online learning systems"],"limitations":["Knowledge cutoff limits currency; recent scientific discoveries or updated theories may not be reflected","Cannot access real-time scientific databases or perform literature searches; relies on training data","May conflate or oversimplify competing scientific theories or areas of active research debate","No ability to cite specific papers or provide primary source references; suitable for explanation, not academic citation"],"requires":["API access to ERNIE-4.5-21B via Baidu or OpenRouter","Clear scientific topic or phenomenon to explain","Optional: target audience level (K-12, undergraduate, graduate, expert) for appropriate explanation depth"],"input_types":["text","scientific concepts or phenomena names","questions about mechanisms or processes","requests for multi-domain synthesis"],"output_types":["text explanations at multiple abstraction levels","conceptual diagrams (as text descriptions)","mechanistic descriptions with causal relationships"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_3","uri":"capability://code.generation.editing.code.generation.and.debugging.with.reasoning","name":"code-generation-and-debugging-with-reasoning","description":"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.","intents":["I need to generate working code for a specific algorithm or data structure with explanation of approach","I want to debug existing code by understanding what went wrong and why","I need to optimize code for performance or readability with reasoning about trade-offs","I want to understand the algorithmic complexity and correctness of a code solution"],"best_for":["Software developers and engineers building applications","Computer science educators teaching algorithms and data structures","Technical interviewers and interview preparation platforms","Teams conducting code reviews and seeking AI-assisted analysis"],"limitations":["Generated code may contain subtle bugs or edge case failures not caught by basic testing","Cannot execute code or verify correctness against test cases; relies on reasoning alone","Performance characteristics are estimated, not measured; actual runtime may differ significantly","Limited to code patterns seen in training data; novel or domain-specific patterns may be poorly handled","No real-time access to language documentation or library APIs; may generate deprecated or incorrect API calls"],"requires":["API access to ERNIE-4.5-21B via Baidu or OpenRouter","Clear problem specification or code snippet to analyze","Target programming language specification","Optional: test cases or expected behavior for verification"],"input_types":["text","natural language problem descriptions","code snippets (any language)","pseudocode or algorithm descriptions"],"output_types":["code in specified language","text explanations of approach and complexity","debugging suggestions and fixes","performance analysis and optimization recommendations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_4","uri":"capability://text.generation.language.expert.level.question.answering.across.domains","name":"expert-level-question-answering-across-domains","description":"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.","intents":["I need expert-level answers to complex questions that require nuanced reasoning","I want to understand different perspectives on a contested or ambiguous topic","I need to synthesize knowledge across multiple domains to answer a complex question","I want answers that acknowledge uncertainty and explain reasoning transparently"],"best_for":["Researchers and academics seeking knowledge synthesis","Professionals in specialized fields needing expert consultation","Content creators and journalists researching complex topics","Students and learners seeking deep understanding of complex subjects"],"limitations":["Knowledge cutoff limits currency; recent developments or updated expert consensus may not be reflected","Cannot access real-time information or current events; suitable for timeless knowledge only","May present confident-sounding but incorrect answers in areas outside training distribution","Cannot verify claims against authoritative sources; suitable for exploration, not definitive reference","Reasoning may be opaque or difficult to verify even with thinking tokens exposed"],"requires":["API access to ERNIE-4.5-21B via Baidu or OpenRouter","Well-formed question with sufficient context","Optional: domain specification or audience level for appropriate depth"],"input_types":["text","natural language questions","complex multi-part queries","requests for perspective synthesis"],"output_types":["text answers with reasoning","structured responses with multiple perspectives","uncertainty acknowledgments and caveats"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_5","uri":"capability://text.generation.language.text.generation.and.content.creation.with.style.control","name":"text-generation-and-content-creation-with-style-control","description":"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.","intents":["I need to generate content in a specific style or tone for a target audience","I want to create long-form content (articles, essays) with consistent voice and structure","I need to paraphrase or rewrite content while maintaining meaning but changing style","I want to generate multiple variations of content with different tones for A/B testing"],"best_for":["Content creators and marketing teams","Technical writers and documentation teams","Educational content developers","Creative writers and authors seeking inspiration or assistance"],"limitations":["Generated content may lack originality or contain subtle plagiarism from training data","Long-form content coherence degrades with length; very long documents may lose thematic consistency","Style control is approximate; fine-grained stylistic requirements may not be precisely met","Cannot verify factual accuracy of generated content; suitable for creative or stylistic tasks, not factual reporting","May perpetuate biases or stereotypes present in training data"],"requires":["API access to ERNIE-4.5-21B via Baidu or OpenRouter","Clear content specification (topic, length, format)","Optional: style parameters (tone, formality, target audience)","Optional: reference content or examples for style matching"],"input_types":["text","content prompts or outlines","reference content for style matching","style parameters (tone, formality, audience)"],"output_types":["text content in specified style","long-form articles or essays","multiple variations with different styles","paraphrases and rewrites"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_6","uri":"capability://text.generation.language.multi.language.translation.and.cross.lingual.reasoning","name":"multi-language-translation-and-cross-lingual-reasoning","description":"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.","intents":["I need accurate translation of technical or specialized content between languages","I want to translate content while preserving tone, style, and cultural context","I need to answer questions about content in a different language than the query","I want to identify semantic equivalence across languages for content matching"],"best_for":["International teams and organizations","Translation services and localization companies","Multilingual content platforms and global applications","Researchers working with multilingual datasets"],"limitations":["Translation quality varies significantly across language pairs; low-resource languages may have poor quality","Idiomatic expressions and cultural references may not translate accurately","Cannot handle specialized terminology outside training distribution","Cross-lingual reasoning may lose nuance or context-specific meaning","No access to real-time terminology databases or domain-specific glossaries"],"requires":["API access to ERNIE-4.5-21B via Baidu or OpenRouter","Source and target language specification","Optional: domain specification or terminology glossary for specialized content","Optional: style or tone parameters for adapted translation"],"input_types":["text","content in any supported language","language pair specification","style or tone parameters"],"output_types":["translated text","cross-lingual answers","semantic equivalence scores"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.text","name":"structured-data-extraction-from-unstructured-text","description":"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.","intents":["I need to extract key information from documents and convert it to structured format","I want to identify entities and relationships in text for knowledge graph construction","I need to parse semi-structured content (emails, forms, reports) into databases","I want to extract metadata and attributes from unstructured content at scale"],"best_for":["Data engineering teams building ETL pipelines","Knowledge graph and semantic search teams","Document processing and content management systems","Business intelligence and analytics teams"],"limitations":["Extraction accuracy depends on text clarity and structure; ambiguous or poorly formatted text may produce errors","Cannot handle domain-specific entity types without additional training or fine-tuning","Relationship extraction may miss implicit or complex relationships","No guarantee of schema compliance; output may require post-processing validation","Scaling to large document volumes requires batching and may incur significant API costs"],"requires":["API access to ERNIE-4.5-21B via Baidu or OpenRouter","Clear schema or format specification for extracted data","Unstructured text input","Optional: examples of desired extraction for few-shot prompting"],"input_types":["text","unstructured documents","schema specifications (JSON schema, table definitions)","extraction examples"],"output_types":["JSON structured data","CSV or table format","knowledge graph triples","entity and relationship lists"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-baidu-ernie-4.5-21b-a3b-thinking__cap_8","uri":"capability://planning.reasoning.academic.benchmark.performance.and.expert.evaluation","name":"academic-benchmark-performance-and-expert-evaluation","description":"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.","intents":["I need to evaluate model quality against standardized benchmarks for my application","I want to use a model with proven expert-level performance on academic tasks","I need to compare this model's capabilities against other models on standard metrics","I want to understand model performance across specific domains (math, coding, science)"],"best_for":["AI researchers and model evaluators","Teams selecting models for production applications","Benchmark developers and evaluation frameworks","Organizations requiring certified model performance"],"limitations":["Benchmark performance may not translate to real-world application performance","Benchmarks may not cover domain-specific requirements or edge cases","Performance metrics are static; model may degrade with distribution shift or domain-specific data","Benchmark results are proprietary and may not be independently verified","Performance on benchmarks does not guarantee safety, alignment, or absence of biases"],"requires":["Access to benchmark evaluation frameworks (MMLU, GSM8K, MATH, HumanEval, etc.)","API access to ERNIE-4.5-21B for evaluation","Computational resources for running full benchmark suites"],"input_types":["benchmark test cases","standardized evaluation datasets"],"output_types":["benchmark scores and metrics","performance comparisons","domain-specific performance breakdowns"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access to Baidu ERNIE platform or OpenRouter integration","Support for streaming or full-response retrieval of thinking tokens","Sufficient context window to accommodate both reasoning and final output (typically 8K+ tokens recommended)","Clear, well-formatted mathematical problem statements (LaTeX or plain text)","API access to ERNIE-4.5-21B model via Baidu or OpenRouter","Optional: external symbolic math library (SymPy, Mathematica) for validation","API access to ERNIE-4.5-21B via Baidu or OpenRouter","Clear scientific topic or phenomenon to explain","Optional: target audience level (K-12, undergraduate, graduate, expert) for appropriate explanation depth","Clear problem specification or code snippet to analyze"],"failure_modes":["Reasoning chains increase latency by 2-5x compared to direct-answer models due to intermediate token generation","Extended thinking may produce verbose or redundant reasoning steps for simple queries","A3B mechanism is proprietary and not interpretable — cannot directly inspect or modify branching heuristics","Reasoning depth is bounded by context window and token limits; 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