{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen-2.5-72b-instruct","slug":"qwen-qwen-2.5-72b-instruct","name":"Qwen2.5 72B Instruct","type":"model","url":"https://openrouter.ai/models/qwen~qwen-2.5-72b-instruct","page_url":"https://unfragile.ai/qwen-qwen-2.5-72b-instruct","categories":["llm-apis"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$3.60e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_0","uri":"capability://text.generation.language.multi.turn.instruction.following.conversation","name":"multi-turn instruction-following conversation","description":"Processes sequential user messages with full conversation history context, maintaining coherent dialogue state across turns. Uses transformer-based attention mechanisms to weight relevant prior exchanges and apply instruction-following patterns learned during supervised fine-tuning on diverse conversational datasets. Supports system prompts to establish role, tone, and behavioral constraints that persist across the conversation thread.","intents":["Build a chatbot that remembers context across multiple user messages without manual state management","Create an interactive assistant that follows complex multi-step instructions across conversation turns","Implement a conversational interface where system prompts define agent behavior and constraints"],"best_for":["Teams building conversational AI products via API without infrastructure overhead","Developers prototyping chatbots and virtual assistants with minimal setup","Applications requiring stateless API calls with implicit conversation memory"],"limitations":["Context window limited to ~32K tokens; conversations exceeding this require external summarization or truncation","No persistent memory across separate API sessions — each conversation starts fresh unless explicitly managed by client","Latency increases with conversation length due to full-history re-processing on each turn"],"requires":["API key for OpenRouter or direct Qwen2.5 endpoint access","HTTP client capable of streaming or polling responses","Client-side conversation history management if multi-turn state is needed"],"input_types":["text (user messages)","text (system prompts)","text (conversation history as formatted strings)"],"output_types":["text (natural language responses)","text (streaming tokens for real-time display)"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Generates syntactically valid code snippets, functions, and complete programs across 40+ programming languages by leveraging transformer attention patterns trained on vast code corpora. Understands language-specific idioms, library conventions, and best practices; can complete partial code, generate from docstrings, and suggest refactorings. Works via prompt engineering — no language-specific AST parsing or compilation on the model side, relying instead on learned patterns of valid syntax and semantics.","intents":["Generate boilerplate code and function stubs from natural language descriptions","Complete partial code implementations with context-aware suggestions","Translate code between programming languages while preserving logic","Generate test cases and documentation from existing code"],"best_for":["Individual developers and small teams using code generation in IDEs or editors via API","Teams building code-centric applications (documentation generators, code migration tools)","Rapid prototyping scenarios where code quality is acceptable if semantically sound"],"limitations":["No real-time syntax validation — generated code may contain subtle bugs or language-specific errors requiring human review","Limited to learned patterns; novel or domain-specific libraries may produce hallucinated or incorrect API calls","Performance degrades on very long code contexts (>8K tokens) due to attention complexity","No built-in refactoring awareness — cannot reliably perform large-scale codebase transformations"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Optional: linter or type-checker to validate generated code before execution"],"input_types":["text (natural language descriptions)","code (partial implementations for completion)","code (full functions for refactoring or translation)"],"output_types":["code (generated functions, classes, or scripts)","text (explanations of generated code)"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_2","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.problem.solving","name":"mathematical reasoning and symbolic problem-solving","description":"Solves mathematical problems including algebra, calculus, statistics, and logic puzzles through chain-of-thought reasoning patterns learned during training. Processes equations and symbolic notation as text, breaking problems into intermediate steps and applying mathematical rules. Does not use external symbolic math engines; reasoning is purely learned from training data, making it probabilistic rather than deterministic for complex proofs.","intents":["Solve word problems and mathematical equations with step-by-step explanations","Generate mathematical proofs and derivations for educational content","Validate mathematical reasoning in student work or research papers","Create math tutoring systems that explain problem-solving approaches"],"best_for":["Educational technology platforms requiring math tutoring and problem explanation","Research assistants needing symbolic reasoning for literature review and hypothesis validation","Content creators building math-heavy educational materials"],"limitations":["No symbolic computation — cannot guarantee correctness for complex proofs or high-precision calculations","May hallucinate intermediate steps or apply incorrect mathematical rules, especially for non-standard problems","Limited to problems expressible in natural language or standard mathematical notation; specialized domains (topology, abstract algebra) may fail","Performance degrades on problems requiring >10 intermediate reasoning steps"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Optional: external symbolic math library (SymPy, Mathematica) for verification"],"input_types":["text (mathematical problems in natural language)","text (equations in LaTeX or plain text notation)"],"output_types":["text (step-by-step solutions)","text (mathematical explanations and proofs)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_3","uri":"capability://memory.knowledge.knowledge.grounded.text.generation.with.learned.facts","name":"knowledge-grounded text generation with learned facts","description":"Generates factual text responses by retrieving and synthesizing information from its training data (knowledge cutoff approximately early 2024). Uses attention mechanisms to activate relevant knowledge patterns when processing queries, then generates coherent text that incorporates those facts. Does not perform real-time web search or access external knowledge bases; all knowledge is static and embedded in model weights.","intents":["Answer factual questions about history, science, culture, and current events up to training cutoff","Generate summaries of topics using learned domain knowledge","Create content that requires factual accuracy within training data scope","Provide explanations of concepts and phenomena"],"best_for":["Question-answering systems for domains with stable, well-documented knowledge","Content generation platforms where training-data-era facts are sufficient","Educational tools explaining established concepts and historical information"],"limitations":["Knowledge cutoff prevents awareness of events after early 2024; will hallucinate or refuse recent queries","No real-time fact-checking — may confidently state incorrect information if training data contained errors or contradictions","Cannot access proprietary, internal, or specialized knowledge bases without fine-tuning","Factual accuracy varies by domain; technical and niche topics more prone to errors than common knowledge"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Optional: fact-checking service or knowledge base for verification"],"input_types":["text (factual questions)","text (topic prompts for content generation)"],"output_types":["text (factual answers and explanations)","text (generated content with embedded facts)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_4","uri":"capability://text.generation.language.instruction.conditioned.text.transformation.and.style.adaptation","name":"instruction-conditioned text transformation and style adaptation","description":"Transforms input text according to explicit instructions (summarize, expand, translate, change tone, rewrite for audience) by learning instruction-following patterns during supervised fine-tuning. Processes the instruction as part of the prompt context and applies learned transformation rules without task-specific training. Supports arbitrary instruction variations, making it flexible for custom transformation pipelines.","intents":["Summarize long documents to specific lengths or detail levels","Translate text between languages with context preservation","Rewrite content for different audiences (technical to non-technical, formal to casual)","Adapt tone and style (professional, creative, academic, etc.)"],"best_for":["Content platforms requiring flexible text transformation without model retraining","Localization and translation workflows where instruction-based control is preferred","Writing assistance tools with user-customizable transformation rules","Batch processing pipelines for document preparation and content adaptation"],"limitations":["Instruction interpretation is probabilistic; complex or ambiguous instructions may produce inconsistent results","No guarantee of output length or format compliance — summaries may exceed requested length, translations may lose nuance","Sensitive to instruction phrasing; minor wording changes can significantly alter output","Limited to text-based transformations; cannot handle structured data or code-specific transformations reliably"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Well-crafted prompt templates for consistent instruction formatting"],"input_types":["text (source content to transform)","text (transformation instructions)"],"output_types":["text (transformed content)","text (multiple variants if requested)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.from.unstructured.text","name":"structured data extraction from unstructured text","description":"Extracts structured information (entities, relationships, key-value pairs, JSON) from unstructured text by learning extraction patterns during training. Processes natural language descriptions of desired output format and generates structured responses (JSON, CSV, key-value pairs) without external parsing libraries. Relies on prompt engineering to specify schema and extraction rules; no built-in schema validation or type enforcement.","intents":["Extract named entities (people, organizations, locations) from documents","Parse semi-structured text (resumes, invoices, contracts) into JSON","Generate structured summaries with specific fields from long documents","Convert unstructured data into database-ready formats"],"best_for":["Data preparation pipelines where manual annotation is infeasible","Document processing systems handling diverse formats (PDFs, emails, web content)","Knowledge graph construction from unstructured sources","Rapid prototyping of data extraction workflows before building specialized models"],"limitations":["No schema validation — extracted JSON may be malformed or missing required fields","Accuracy degrades with complex schemas (>20 fields) or nested structures","Hallucination risk: model may invent plausible-sounding values for missing information","No type enforcement; numeric fields may contain text, dates may be unparseable","Performance degrades on very long documents (>4K tokens) due to attention limitations"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","JSON parser and validator for output validation","Optional: schema definition language (JSON Schema) for prompt engineering"],"input_types":["text (unstructured documents)","text (schema or format specification)"],"output_types":["structured data (JSON)","structured data (CSV rows)","structured data (key-value pairs)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_6","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Generates original creative content (stories, poetry, marketing copy, dialogue) by sampling from learned distributions of language patterns, narrative structures, and stylistic conventions. Accepts style directives (tone, genre, length, audience) as part of the prompt and applies them through attention-weighted generation. Does not use templates or retrieval; all content is generated de novo from learned patterns, making each output unique but potentially inconsistent with long-form content.","intents":["Generate marketing copy and product descriptions with brand voice","Create story outlines and narrative content for games or interactive fiction","Write poetry and creative prose in specified styles or genres","Generate dialogue for characters in scripts or games"],"best_for":["Content marketing platforms requiring rapid copy generation at scale","Game development studios generating narrative content and dialogue","Creative writing tools and platforms for authors and screenwriters","Advertising and branding agencies prototyping creative concepts"],"limitations":["Consistency degrades in long-form content (>2K tokens); narrative threads may diverge or contradict earlier sections","Style control is probabilistic; instructions may be ignored or inconsistently applied","Originality not guaranteed; may inadvertently reproduce training data or generate clichéd content","No built-in fact-checking; creative content may contain factual errors if based on learned misconceptions","Tone and voice may shift unexpectedly within a single generation"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Human review process for quality control and fact-checking"],"input_types":["text (creative prompts and style directives)","text (partial content for continuation)"],"output_types":["text (generated creative content)","text (multiple variants for A/B testing)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_7","uri":"capability://planning.reasoning.logical.reasoning.and.constraint.satisfaction","name":"logical reasoning and constraint satisfaction","description":"Solves logic puzzles, constraint satisfaction problems, and reasoning tasks by applying learned logical inference patterns. Processes problem descriptions in natural language and generates step-by-step logical deductions. Does not use formal logic engines or SAT solvers; reasoning is probabilistic and based on learned patterns, making it suitable for heuristic reasoning but not guaranteed correctness for complex logical systems.","intents":["Solve logic puzzles and riddles with explanations","Validate logical consistency of arguments and statements","Generate logical proofs and deductions for educational content","Reason about constraints and dependencies in planning problems"],"best_for":["Educational platforms teaching logic and critical thinking","Puzzle and game platforms requiring reasoning explanations","Content creators generating logic-based educational material","Systems requiring heuristic reasoning for non-critical decisions"],"limitations":["No formal verification — logical conclusions may be incorrect or incomplete","Struggles with problems requiring >5 levels of logical nesting","Cannot handle symbolic logic notation reliably; natural language descriptions required","Performance degrades on problems with many constraints or variables","May apply incorrect logical rules or miss valid deductions"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Optional: formal logic checker for verification"],"input_types":["text (logic puzzles and reasoning problems)","text (logical statements and constraints)"],"output_types":["text (step-by-step logical deductions)","text (logical conclusions and proofs)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_8","uri":"capability://text.generation.language.multi.language.support.with.cross.lingual.understanding","name":"multi-language support with cross-lingual understanding","description":"Processes and generates text in 40+ languages including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, and others. Leverages shared token embeddings and cross-lingual attention patterns learned during multilingual pre-training. Supports code-switching (mixing languages in single prompts) and can translate between language pairs without explicit translation instructions, though quality varies by language pair and domain.","intents":["Build multilingual chatbots and assistants serving global audiences","Translate content between languages with context preservation","Process and analyze multilingual documents and datasets","Support code-switching in conversational interfaces for bilingual users"],"best_for":["Global platforms requiring multilingual support without separate models per language","International teams building products for diverse language markets","Localization workflows where rapid translation is needed","Research applications analyzing multilingual text corpora"],"limitations":["Translation quality varies significantly by language pair; some pairs (English-Chinese) are strong, others (English-low-resource languages) are weak","Code-switching may confuse the model; mixing languages can degrade response quality","Performance is lower for non-Latin scripts and low-resource languages","Cultural and idiomatic nuances may be lost in translation","No language detection; must specify language in prompt for optimal results"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Optional: language detection library for automatic language identification"],"input_types":["text (in any supported language)","text (multilingual prompts with code-switching)"],"output_types":["text (in requested language)","text (translations between language pairs)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen-2.5-72b-instruct__cap_9","uri":"capability://text.generation.language.role.playing.and.persona.based.response.generation","name":"role-playing and persona-based response generation","description":"Adopts specified personas, roles, or character archetypes and generates responses consistent with those personas through prompt-based conditioning. Learns to maintain character voice, knowledge domain, and behavioral patterns from system prompts and few-shot examples. Does not use separate character models; all personas are implemented through prompt engineering and learned attention patterns.","intents":["Create interactive characters for games, chatbots, or educational simulations","Generate expert responses in specific domains (doctor, lawyer, engineer) for educational content","Build customer service bots with consistent brand voice and personality","Simulate historical figures or fictional characters for entertainment or education"],"best_for":["Game studios building NPC dialogue systems and interactive characters","Educational platforms simulating expert interactions or historical scenarios","Customer service platforms requiring consistent brand personality","Entertainment applications with character-driven narratives"],"limitations":["Persona consistency degrades over long conversations; character may drift from defined traits","Complex or contradictory personas may confuse the model, leading to inconsistent responses","Knowledge boundaries of personas are not enforced; character may claim expertise outside their domain","Sensitive personas (controversial figures, harmful stereotypes) may be refused or sanitized","No persistent memory of character-specific facts across separate conversations"],"requires":["API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Well-crafted system prompts defining persona traits and knowledge boundaries"],"input_types":["text (persona descriptions and character traits)","text (few-shot examples of character responses)","text (user messages to respond to as the character)"],"output_types":["text (character-consistent responses)","text (dialogue in character voice)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API key for OpenRouter or direct Qwen2.5 endpoint access","HTTP client capable of streaming or polling responses","Client-side conversation history management if multi-turn state is needed","API key for OpenRouter or Qwen endpoint","HTTP client for API calls","Optional: linter or type-checker to validate generated code before execution","Optional: external symbolic math library (SymPy, Mathematica) for verification","Optional: fact-checking service or knowledge base for verification","Well-crafted prompt templates for consistent instruction formatting","JSON parser and validator for output validation"],"failure_modes":["Context window limited to ~32K tokens; conversations exceeding this require external summarization or truncation","No persistent memory across separate API sessions — each conversation starts fresh unless explicitly managed by client","Latency increases with conversation length due to full-history re-processing on each turn","No real-time syntax validation — generated code may contain subtle bugs or language-specific errors requiring human review","Limited to learned patterns; novel or domain-specific libraries may produce hallucinated or incorrect API calls","Performance degrades on very long code contexts (>8K tokens) due to attention complexity","No built-in refactoring awareness — cannot reliably perform large-scale codebase transformations","No symbolic computation — cannot guarantee correctness for complex proofs or high-precision calculations","May hallucinate intermediate steps or apply incorrect mathematical rules, especially for non-standard problems","Limited to problems expressible in natural language or standard mathematical notation; specialized domains (topology, abstract algebra) may fail","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.485Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwen-qwen-2.5-72b-instruct","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen-2.5-72b-instruct"}},"signature":"ASJlgBJyyrwQWHtdyJI/KObjCU73HOq1dOpvngNrFOKkeNUWBFjKeN/CS2mfSNt78AdtRnFgVczY3IF0p5mgAQ==","signedAt":"2026-06-22T04:17:34.170Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen-2.5-72b-instruct","artifact":"https://unfragile.ai/qwen-qwen-2.5-72b-instruct","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen-2.5-72b-instruct","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}