{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-235b-a22b-2507","slug":"qwen-qwen3-235b-a22b-2507","name":"Qwen: Qwen3 235B A22B Instruct 2507","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-235b-a22b-2507","page_url":"https://unfragile.ai/qwen-qwen3-235b-a22b-2507","categories":["text-writing"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$7.10e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_0","uri":"capability://text.generation.language.multilingual.instruction.following.text.generation","name":"multilingual instruction-following text generation","description":"Generates coherent, contextually-appropriate text responses across 100+ languages using a mixture-of-experts (MoE) architecture where only 22B of 235B total parameters activate per forward pass. The model is instruction-tuned via supervised fine-tuning on diverse task examples, enabling it to follow complex multi-step directives, answer questions, and adapt tone/style based on user intent without explicit task-specific prompting.","intents":["I need to generate natural language responses in languages beyond English for a global chatbot","I want a model that can follow detailed instructions without task-specific prompt engineering","I need efficient inference that doesn't require loading all 235B parameters into memory"],"best_for":["teams building multilingual conversational AI systems","developers deploying inference-constrained applications requiring high throughput","organizations needing general-purpose instruction-following without domain-specific fine-tuning"],"limitations":["MoE routing decisions add ~50-100ms latency overhead compared to dense models of equivalent active parameter count","Multilingual capability may show performance variance across low-resource languages (Swahili, Tagalog) vs high-resource languages (English, Mandarin)","Instruction-tuning quality depends on training data distribution; performance degrades on out-of-distribution task types not seen during SFT","No built-in few-shot learning optimization; in-context learning performance may be lower than larger dense models"],"requires":["OpenRouter API key or compatible LLM provider endpoint","HTTP client library (curl, Python requests, JavaScript fetch)","Support for streaming or non-streaming API calls depending on use case"],"input_types":["text (natural language instructions, questions, prompts)","code snippets (for code explanation or generation tasks)","structured prompts with role definitions (system prompts)"],"output_types":["text (natural language responses)","code (when instructed to generate code)","structured text (JSON, markdown, CSV when explicitly requested)"],"categories":["text-generation-language","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_1","uri":"capability://text.generation.language.context.aware.conversational.state.management","name":"context-aware conversational state management","description":"Maintains coherent multi-turn conversation context by processing full conversation history within the model's context window (typically 128K tokens), using transformer self-attention to weight relevant prior messages and maintain consistency across dialogue turns. The instruction-tuned architecture enables the model to track conversation state, reference previous statements, and adapt responses based on established context without explicit state management code.","intents":["I need a chatbot that remembers earlier parts of the conversation and references them naturally","I want to build a multi-turn dialogue system where the model understands conversation flow and context dependencies","I need the model to maintain consistent character/persona across multiple exchanges in a conversation"],"best_for":["developers building customer support chatbots requiring conversation continuity","teams creating interactive tutoring systems with multi-turn explanations","conversational AI applications where context coherence is critical to user experience"],"limitations":["Context window size (typically 128K tokens) limits conversation history; older messages may be forgotten or deprioritized in very long conversations","Attention mechanism computational cost scales quadratically with context length, causing latency degradation for maximum-length contexts","No explicit conversation state persistence; context is lost between separate API calls unless explicitly re-provided in subsequent requests","Model may conflate details across multiple conversation threads if similar entities/topics appear in history"],"requires":["OpenRouter API key with support for multi-turn message format (OpenAI-compatible messages array)","Client-side conversation history management to accumulate and pass prior messages","Understanding of token counting to stay within context window limits"],"input_types":["text (user messages in current turn)","conversation history (array of prior user/assistant message pairs)"],"output_types":["text (assistant response contextually grounded in conversation history)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_2","uri":"capability://code.generation.editing.code.generation.and.explanation.with.multi.language.support","name":"code generation and explanation with multi-language support","description":"Generates syntactically correct code across 50+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) and explains existing code through instruction-tuned patterns learned from code-heavy training data. The model uses transformer attention to understand code structure, variable scope, and language-specific idioms, enabling both generation from natural language specifications and explanation of complex code logic.","intents":["I need to generate boilerplate code or complete code snippets from natural language descriptions","I want the model to explain what a code snippet does in plain English","I need code generation across multiple programming languages without language-specific models"],"best_for":["developers using AI-assisted coding in IDEs or standalone tools","technical documentation teams automating code example generation","educational platforms providing code explanation and tutoring"],"limitations":["Generated code may contain logical errors, security vulnerabilities, or inefficient patterns; always requires human review before production use","Performance varies significantly by language; well-represented languages (Python, JavaScript) generate higher-quality code than niche languages","No real-time compilation/execution feedback; model cannot verify generated code correctness without external tooling","Context window limits prevent generating very large codebases (>50K tokens); multi-file projects require chunking or separate requests"],"requires":["OpenRouter API key","Code editor or IDE integration, or custom client application","External linter/compiler for code validation (not provided by model)"],"input_types":["text (natural language code specifications)","code snippets (for explanation or refactoring tasks)","structured prompts with language specification"],"output_types":["code (generated or refactored code in specified language)","text (code explanations, documentation)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.generation","name":"structured data extraction and json generation","description":"Extracts structured information from unstructured text and generates valid JSON/YAML/CSV output by leveraging instruction-tuning on structured output examples and transformer attention patterns that understand schema constraints. The model can parse natural language into structured formats, validate against implicit schemas, and generate machine-readable output without requiring external parsing libraries or schema validation frameworks.","intents":["I need to extract key information from documents and convert it to JSON for downstream processing","I want to generate structured API responses or configuration files from natural language specifications","I need to parse user input into structured form for database insertion or API calls"],"best_for":["data engineering teams automating ETL pipeline data extraction steps","API developers generating structured responses from unstructured user input","teams building form-filling or data collection systems"],"limitations":["No schema validation; generated JSON may be syntactically valid but semantically incorrect or missing required fields","Complex nested structures (deeply nested objects, arrays of objects) may have formatting errors or incomplete data","Model cannot enforce type constraints (e.g., ensuring a field is always an integer); post-processing validation required","Performance degrades with very large schemas (>100 fields) or complex extraction rules"],"requires":["OpenRouter API key","JSON schema definition or example for the model to learn output format","Post-processing validation library (jsonschema, Pydantic, etc.) to verify output correctness"],"input_types":["text (unstructured documents, user input)","structured prompts with schema examples or format specifications"],"output_types":["JSON (structured data)","YAML (configuration files)","CSV (tabular data)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_4","uri":"capability://planning.reasoning.reasoning.and.multi.step.problem.decomposition","name":"reasoning and multi-step problem decomposition","description":"Decomposes complex problems into intermediate reasoning steps using chain-of-thought patterns learned during instruction-tuning, enabling the model to show work, justify conclusions, and handle multi-step logical reasoning. The transformer architecture processes the full reasoning chain in context, allowing later steps to reference earlier reasoning and build on intermediate conclusions without explicit planning or state management.","intents":["I need the model to explain its reasoning step-by-step for complex questions","I want to solve multi-step math problems or logic puzzles with intermediate verification","I need the model to break down complex tasks into subtasks and solve them sequentially"],"best_for":["educational applications requiring transparent reasoning for student learning","technical support systems explaining complex troubleshooting steps","research or analysis tools where reasoning transparency is critical"],"limitations":["Chain-of-thought reasoning increases token generation by 2-5x, raising latency and API costs proportionally","Reasoning quality depends on training data; the model may show plausible-sounding but incorrect intermediate steps","No external verification of reasoning steps; incorrect logic early in the chain propagates to final answer","Very complex problems (>10 reasoning steps) may exceed context window or show reasoning degradation"],"requires":["OpenRouter API key","Prompting strategy that explicitly requests step-by-step reasoning (e.g., 'Think step by step')","Acceptance of higher token usage and latency compared to direct answer generation"],"input_types":["text (complex questions, problems, or tasks)","structured prompts requesting explicit reasoning"],"output_types":["text (reasoning steps followed by final answer)","structured reasoning (numbered steps, intermediate conclusions)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_5","uri":"capability://tool.use.integration.function.calling.and.tool.integration.via.schema.based.routing","name":"function calling and tool integration via schema-based routing","description":"Integrates with external tools and APIs by accepting structured function schemas and generating function calls in JSON format, enabling the model to decide when to invoke tools, what parameters to pass, and how to incorporate tool results into responses. The instruction-tuned architecture understands function signatures and can map natural language requests to appropriate function calls without requiring explicit function-calling API support.","intents":["I need the model to decide when to call external APIs and generate properly-formatted function calls","I want to build an agent that uses tools like calculators, search engines, or databases","I need the model to integrate with my custom business logic functions"],"best_for":["developers building AI agents with tool-use capabilities","teams creating autonomous systems that interact with external APIs","applications requiring real-time data integration (weather, stock prices, database queries)"],"limitations":["No native function-calling API support (unlike OpenAI/Anthropic); requires manual JSON parsing and function invocation","Model may generate malformed JSON or incorrect function parameters; requires validation before execution","No built-in error handling or retry logic; failed function calls require explicit re-prompting with error messages","Context window limits prevent very long tool-use chains (>20 function calls) without context truncation"],"requires":["OpenRouter API key","Custom client code to parse generated JSON and invoke functions","Function schema definitions in JSON format","Error handling and validation logic for generated function calls"],"input_types":["text (natural language requests)","structured prompts with function schemas","tool results (for multi-turn tool use)"],"output_types":["JSON (function calls with parameters)","text (natural language responses incorporating tool results)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_6","uri":"capability://safety.moderation.content.moderation.and.safety.aware.response.generation","name":"content moderation and safety-aware response generation","description":"Filters harmful content and generates responses that avoid unsafe outputs through instruction-tuning on safety examples and alignment techniques. The model learns to recognize potentially harmful requests, decline appropriately, and suggest safe alternatives without requiring external content moderation APIs. Safety constraints are embedded in the model weights through supervised fine-tuning rather than post-hoc filtering.","intents":["I need the model to refuse harmful requests (violence, illegal content, hate speech) without external filtering","I want to ensure generated content complies with safety guidelines without additional moderation overhead","I need the model to explain why certain requests are unsafe"],"best_for":["public-facing applications requiring built-in safety without external moderation services","organizations with strict content policies requiring transparent safety decisions","applications serving diverse user bases with varying safety requirements"],"limitations":["Safety training may be overly conservative, refusing benign requests (e.g., historical violence discussion, medical information)","Adversarial prompts may bypass safety training; jailbreak techniques can elicit unsafe outputs","Safety behavior may vary across languages; non-English safety training may be weaker","No configurable safety levels; cannot adjust strictness for different use cases"],"requires":["OpenRouter API key","Acceptance of model's built-in safety decisions (no external override)","User communication strategy for explaining declined requests"],"input_types":["text (user requests of any nature)"],"output_types":["text (safe responses or refusals with explanations)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_7","uri":"capability://text.generation.language.knowledge.synthesis.and.summarization.from.long.documents","name":"knowledge synthesis and summarization from long documents","description":"Synthesizes information from long documents (up to 128K tokens) by processing full text in context and generating concise summaries, extracting key points, or answering questions about document content. The transformer attention mechanism identifies relevant passages and integrates information across the entire document without requiring external chunking or retrieval systems.","intents":["I need to summarize long documents (research papers, legal contracts, reports) into key points","I want to ask questions about document content and get answers grounded in the full text","I need to extract specific information from long documents without manual reading"],"best_for":["legal/compliance teams processing contracts and regulatory documents","research teams analyzing academic papers and literature reviews","customer support teams handling long customer communications or documentation"],"limitations":["Summarization quality depends on document structure; poorly-formatted or very dense documents may produce incomplete summaries","Attention mechanism may miss important details in very long documents (>100K tokens) due to attention distribution","No external knowledge integration; summaries are limited to document content without additional context","Latency increases significantly with document length; 128K token documents may take 10-30 seconds to process"],"requires":["OpenRouter API key","Document in text format (PDF/images require OCR preprocessing)","Token counting to ensure document fits within 128K context window"],"input_types":["text (long documents)","structured prompts with summarization instructions or questions"],"output_types":["text (summaries, key points, answers grounded in document)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_8","uri":"capability://text.generation.language.creative.writing.and.style.adaptation","name":"creative writing and style adaptation","description":"Generates creative content (stories, poetry, dialogue) and adapts writing style to match specified tones or genres through instruction-tuning on diverse writing examples. The model learns stylistic patterns, narrative structures, and genre conventions, enabling it to generate coherent creative content or transform existing text to match target styles without explicit style transfer algorithms.","intents":["I need to generate creative content (stories, poetry, dialogue) in specific genres","I want to adapt writing style (formal to casual, technical to narrative) for different audiences","I need to generate character dialogue or narrative descriptions for creative projects"],"best_for":["content creators and writers using AI for ideation and drafting","game developers generating NPC dialogue and narrative content","marketing teams creating engaging copy in different tones"],"limitations":["Generated creative content may be derivative or clichéd, lacking originality compared to human writers","Consistency across long narratives (>5K tokens) may degrade; characters may change personality or plot threads may become incoherent","Style adaptation may be superficial; deep stylistic changes may require multiple iterations","No explicit control over narrative structure; very long stories may lack coherent plot arcs"],"requires":["OpenRouter API key","Clear style/genre specifications in prompts","Human editorial review for quality and originality"],"input_types":["text (style specifications, genre descriptions, story prompts)","existing text (for style adaptation or continuation)"],"output_types":["text (creative content in specified style/genre)"],"categories":["text-generation-language","creative-writing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b-2507__cap_9","uri":"capability://text.generation.language.translation.and.cross.lingual.transfer","name":"translation and cross-lingual transfer","description":"Translates text between 100+ languages and performs cross-lingual tasks (answering questions in different languages, translating code comments, etc.) through multilingual training and instruction-tuning. The model learns language-agnostic representations enabling it to understand meaning in one language and express it in another without language-specific translation models.","intents":["I need to translate content between multiple languages without separate translation services","I want to answer user questions in their native language regardless of training data language","I need to translate code comments and documentation across languages"],"best_for":["global applications serving multilingual user bases","teams localizing content for international markets","developers working with multilingual codebases"],"limitations":["Translation quality varies significantly by language pair; high-resource pairs (English-Spanish) are better than low-resource pairs (English-Swahili)","Idiomatic expressions and cultural context may not translate accurately; literal translations may be produced","No domain-specific translation (legal, medical terminology may be mistranslated)","Transliteration and script handling may be imperfect for non-Latin scripts"],"requires":["OpenRouter API key","Source and target language specification in prompts","Acceptance of potential quality variance across language pairs"],"input_types":["text (in any of 100+ supported languages)"],"output_types":["text (translated to target language)"],"categories":["text-generation-language","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["OpenRouter API key or compatible LLM provider endpoint","HTTP client library (curl, Python requests, JavaScript fetch)","Support for streaming or non-streaming API calls depending on use case","OpenRouter API key with support for multi-turn message format (OpenAI-compatible messages array)","Client-side conversation history management to accumulate and pass prior messages","Understanding of token counting to stay within context window limits","OpenRouter API key","Code editor or IDE integration, or custom client application","External linter/compiler for code validation (not provided by model)","JSON schema definition or example for the model to learn output format"],"failure_modes":["MoE routing decisions add ~50-100ms latency overhead compared to dense models of equivalent active parameter count","Multilingual capability may show performance variance across low-resource languages (Swahili, Tagalog) vs high-resource languages (English, Mandarin)","Instruction-tuning quality depends on training data distribution; performance degrades on out-of-distribution task types not seen during SFT","No built-in few-shot learning optimization; in-context learning performance may be lower than larger dense models","Context window size (typically 128K tokens) limits conversation history; older messages may be forgotten or deprioritized in very long conversations","Attention mechanism computational cost scales quadratically with context length, causing latency degradation for maximum-length contexts","No explicit conversation state persistence; context is lost between separate API calls unless explicitly re-provided in subsequent requests","Model may conflate details across multiple conversation threads if similar entities/topics appear in history","Generated code may contain logical errors, security vulnerabilities, or inefficient patterns; always requires human review before production use","Performance varies significantly by language; well-represented languages (Python, JavaScript) generate higher-quality code than niche languages","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-qwen3-235b-a22b-2507","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-235b-a22b-2507"}},"signature":"9atnIiT/IADlLthHDjVrATfhKbK30dwFn0LNEAhANWgNckdh42EnxzKq/UHqsUykq+ohjp1/nzT/k9GwHstMCw==","signedAt":"2026-06-21T03:23:05.722Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-235b-a22b-2507","artifact":"https://unfragile.ai/qwen-qwen3-235b-a22b-2507","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-235b-a22b-2507","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"}}