{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-235b-a22b","slug":"qwen-qwen3-235b-a22b","name":"Qwen: Qwen3 235B A22B","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-235b-a22b","page_url":"https://unfragile.ai/qwen-qwen3-235b-a22b","categories":["chatbots-assistants"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$4.55e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-235b-a22b__cap_0","uri":"capability://text.generation.language.mixture.of.experts.language.generation.with.dynamic.parameter.activation","name":"mixture-of-experts language generation with dynamic parameter activation","description":"Qwen3-235B-A22B implements a sparse mixture-of-experts (MoE) architecture that selectively activates 22B parameters per forward pass from a total 235B parameter pool. This routing mechanism uses learned gating functions to dynamically select expert subnetworks based on input tokens, reducing computational cost while maintaining model capacity. The architecture enables efficient inference by computing only active expert pathways rather than the full dense network.","intents":["Deploy a high-capacity language model with reduced inference latency and memory footprint compared to dense 235B models","Build applications requiring long-context reasoning without proportional increases in compute cost per token","Scale inference across distributed systems by leveraging sparse activation patterns for better hardware utilization"],"best_for":["Teams building production LLM applications with strict latency/cost constraints","Researchers evaluating sparse model architectures vs dense alternatives","Organizations deploying multi-turn conversational agents at scale"],"limitations":["MoE routing adds ~5-15ms latency overhead per forward pass due to gating computation and expert selection","Load balancing across experts can be uneven, causing GPU/TPU utilization imbalance in distributed inference","Fine-tuning MoE models requires careful handling of expert dropout and load-balancing losses to prevent expert collapse"],"requires":["API access via OpenRouter or compatible inference provider","Minimum context window support of 32K tokens (typical for Qwen3 variants)","Inference infrastructure supporting batch processing for optimal MoE efficiency"],"input_types":["text (natural language prompts)","code snippets (for code understanding/generation tasks)","structured prompts with system instructions"],"output_types":["text (natural language responses)","code (programming language outputs)","structured reasoning traces (in thinking mode)"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_1","uri":"capability://planning.reasoning.dual.mode.reasoning.with.explicit.thinking.to.response.pipeline","name":"dual-mode reasoning with explicit thinking-to-response pipeline","description":"Qwen3-235B-A22B implements a two-stage inference pipeline where a 'thinking' mode generates internal reasoning traces (chain-of-thought) before producing final responses. This mode uses a separate token stream for scratchpad computation, allowing the model to decompose complex problems (math, logic, code analysis) into explicit reasoning steps before committing to outputs. The thinking tokens are generated but not exposed to users by default, enabling transparent reasoning without cluttering response text.","intents":["Solve complex math problems by generating step-by-step reasoning before final answers","Debug code by explicitly tracing execution logic and identifying error sources","Improve reasoning accuracy on multi-hop logical problems by forcing intermediate decomposition"],"best_for":["Developers building math tutoring or homework assistance systems","Teams creating code analysis and debugging tools requiring explainability","Researchers studying chain-of-thought effectiveness in sparse models"],"limitations":["Thinking mode increases time-to-first-token (TTFT) by 2-4x due to reasoning generation before response output","Thinking tokens consume context window budget, reducing effective context for very long documents (e.g., 100K+ token contexts)","Thinking mode cannot be selectively applied per-sentence; it's a model-level toggle affecting entire response generation","Quality of reasoning depends on model's ability to self-correct; poor initial reasoning traces may propagate to final answers"],"requires":["API parameter support for 'thinking_mode' or equivalent toggle (provider-dependent)","Sufficient context window allocation (minimum 32K tokens recommended for complex reasoning)","Inference provider that exposes thinking token counts for cost/latency monitoring"],"input_types":["text (natural language problem statements)","code (for code analysis and debugging)","mathematical expressions and equations","logical reasoning prompts"],"output_types":["text (final response after reasoning)","reasoning traces (thinking tokens, if exposed by provider)","code with explanations (for debugging tasks)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_2","uri":"capability://memory.knowledge.long.context.semantic.understanding.with.32k.token.windows","name":"long-context semantic understanding with 32k+ token windows","description":"Qwen3-235B-A22B supports extended context windows (32K tokens minimum, potentially up to 128K or higher depending on provider configuration) using position interpolation or similar techniques to extend the base training context. This enables the model to maintain semantic coherence across long documents, multi-turn conversations, and large code repositories without losing information from earlier context. The sparse MoE architecture helps manage memory overhead of long contexts by activating only relevant expert pathways.","intents":["Analyze entire source code files or repositories without chunking or summarization","Conduct multi-turn conversations with 50+ exchanges while maintaining conversation history","Summarize or extract insights from long documents (research papers, legal contracts, technical specifications)"],"best_for":["Teams building code analysis and refactoring tools requiring full-file context","Customer support systems needing to maintain long conversation histories","Document analysis platforms processing research papers, technical documentation, or legal texts"],"limitations":["Inference latency scales linearly with context length; 128K token contexts may add 3-5x latency vs 4K contexts","Long contexts increase memory requirements even with MoE sparsity; distributed inference may be necessary for 100K+ tokens","Attention mechanisms may struggle with very long-range dependencies (>50K tokens) despite architectural improvements","Cost per request scales with context length; long-context queries may be 10-20x more expensive than short queries"],"requires":["Inference provider supporting extended context windows (OpenRouter, Together AI, or similar)","Client-side context management to avoid exceeding maximum window size","Sufficient API quota/budget for long-context requests (typically 5-10x cost of short queries)"],"input_types":["text (long documents, code files, conversation histories)","code (entire source files or multi-file repositories)","structured data (JSON, CSV, markdown with long content)"],"output_types":["text (analysis, summaries, responses)","code (refactored code, generated functions)","structured data (extracted information, annotations)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.with.cross.lingual.reasoning","name":"multilingual text generation with cross-lingual reasoning","description":"Qwen3-235B-A22B is trained on multilingual corpora and can generate coherent text in 30+ languages including English, Chinese, Spanish, French, German, Japanese, and others. The model maintains semantic understanding across languages and can perform cross-lingual tasks (e.g., translate while reasoning, answer questions in a different language than the prompt). The sparse MoE architecture includes language-specific expert pathways that activate based on detected input language, optimizing inference for each language.","intents":["Build chatbots and customer support systems serving global audiences in multiple languages","Translate technical documentation while preserving code snippets and formatting","Answer questions in one language while referencing source material in another language"],"best_for":["Teams building global SaaS products requiring multilingual support","Translation and localization services needing to preserve technical accuracy","International research teams analyzing documents in multiple languages"],"limitations":["Performance varies significantly across languages; English and Chinese are strongest, while low-resource languages (e.g., Icelandic, Swahili) may have degraded quality","Cross-lingual reasoning may introduce translation artifacts or semantic drift when switching between distant language pairs","Language detection is implicit; ambiguous inputs (code-heavy prompts, mixed-language text) may route to incorrect expert pathways","No explicit language tagging in API; users must rely on model's auto-detection, which may fail for code-heavy or technical content"],"requires":["API access via OpenRouter or compatible provider","UTF-8 encoding support for non-Latin scripts (Chinese, Arabic, Cyrillic, etc.)","No explicit language specification parameter (relies on model auto-detection)"],"input_types":["text in 30+ languages","code (language-agnostic)","mixed-language prompts"],"output_types":["text in requested language","code (with comments in target language)","translations"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_4","uri":"capability://code.generation.editing.code.generation.and.analysis.with.syntax.aware.completions","name":"code generation and analysis with syntax-aware completions","description":"Qwen3-235B-A22B generates syntactically correct code across 20+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) using language-specific training data and expert pathways. The model understands code structure, APIs, and common patterns, enabling it to complete functions, generate unit tests, refactor code, and explain implementation details. The thinking mode can be leveraged for complex algorithmic problems to generate step-by-step solutions before code output.","intents":["Auto-complete code functions and generate boilerplate based on function signatures and docstrings","Generate unit tests and test cases for existing code","Refactor legacy code while preserving functionality and improving readability","Explain code logic and identify potential bugs through reasoning mode"],"best_for":["Developers using IDE plugins or web-based editors for code completion","Teams automating code review and refactoring workflows","Educators building coding tutoring systems with explanation capabilities"],"limitations":["Code generation quality degrades for domain-specific languages (DSLs) and less common languages (Haskell, Lisp, Cobol)","Generated code may have subtle bugs or inefficiencies; always requires human review before production use","No built-in access to external APIs or libraries; generated code may reference non-existent or outdated library functions","Context window limits prevent analyzing very large codebases (>50K lines) without chunking"],"requires":["API access via OpenRouter or compatible provider","Code context (file content, function signatures, docstrings) as input","Language specification or inference from file extension/content"],"input_types":["code (partial functions, class definitions, test stubs)","natural language descriptions (docstrings, comments, requirements)","code snippets with syntax errors (for debugging)"],"output_types":["code (completed functions, generated tests, refactored code)","explanations (code logic, design patterns, potential issues)","reasoning traces (in thinking mode, for complex algorithms)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_5","uri":"capability://data.processing.analysis.structured.data.extraction.and.json.schema.compliance","name":"structured data extraction and json schema compliance","description":"Qwen3-235B-A22B can extract structured information from unstructured text and generate outputs conforming to specified JSON schemas or structured formats. The model understands schema constraints and generates valid JSON, CSV, or other structured outputs without requiring external parsing or validation layers. This capability leverages the model's reasoning abilities to map natural language content to structured representations while respecting type constraints and required fields.","intents":["Extract entities, relationships, and attributes from documents and convert to structured JSON","Generate API responses conforming to OpenAPI schemas without manual formatting","Parse semi-structured data (logs, emails, chat transcripts) into queryable databases"],"best_for":["Data engineering teams building ETL pipelines with LLM-powered extraction","API developers generating structured responses from unstructured sources","Teams building knowledge graphs or semantic databases from text"],"limitations":["Schema compliance is not guaranteed; complex nested schemas may produce invalid JSON requiring post-processing validation","Large schemas (>50 fields) may exceed token budgets or cause the model to truncate outputs","No built-in schema versioning or backward compatibility; schema changes require prompt engineering adjustments","Extraction accuracy depends on schema clarity and input text quality; ambiguous schemas produce inconsistent outputs"],"requires":["JSON schema or structured format specification in prompt","Input text containing information to extract","Post-processing validation to ensure schema compliance (recommended)"],"input_types":["unstructured text (documents, articles, logs)","semi-structured data (CSV, markdown tables)","natural language descriptions with schema requirements"],"output_types":["JSON (structured objects conforming to schema)","CSV (tabular data)","XML or other structured formats"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_6","uri":"capability://text.generation.language.multi.turn.conversation.with.stateless.context.management","name":"multi-turn conversation with stateless context management","description":"Qwen3-235B-A22B maintains coherent multi-turn conversations by processing the full conversation history (all previous messages) in each forward pass, without requiring external state management or session storage. The model tracks context, user preferences, and conversation flow across 50+ turns while managing token budgets through intelligent context windowing. This stateless design simplifies deployment but requires clients to manage conversation history and pass it with each request.","intents":["Build chatbot applications with natural multi-turn conversations without backend session storage","Create conversational agents that adapt to user preferences and context over time","Implement customer support systems where conversation history is maintained client-side"],"best_for":["Teams building stateless conversational APIs (e.g., serverless functions, edge deployments)","Applications where conversation history is stored client-side (web apps, mobile apps)","Systems requiring conversation portability (users can export and resume conversations)"],"limitations":["Stateless design requires clients to manage and transmit full conversation history with each request, increasing bandwidth and latency","Long conversations (100+ turns) may exceed context windows, requiring client-side summarization or history truncation","No built-in conversation persistence; clients must implement database storage if conversations need to survive application restarts","Context window constraints mean very long conversations require explicit history management (e.g., summarizing old turns)"],"requires":["Client-side conversation history management (array of messages with roles)","API support for message arrays in request format (e.g., OpenAI-compatible format)","Context window tracking to avoid exceeding limits"],"input_types":["conversation history (array of user/assistant messages)","current user message","optional system prompt or instructions"],"output_types":["assistant response (text)","token usage metadata (for cost tracking)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_7","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Qwen3-235B-A22B demonstrates strong mathematical reasoning capabilities, including solving algebra, calculus, geometry, and discrete math problems. The thinking mode is particularly effective for math, allowing the model to generate step-by-step solutions with intermediate calculations before final answers. The model can work with symbolic expressions, equations, and mathematical notation, though it does not perform symbolic computation (e.g., cannot simplify complex expressions symbolically like Mathematica).","intents":["Solve math homework problems with step-by-step explanations","Generate mathematical proofs and derivations for educational purposes","Verify mathematical correctness of solutions and identify errors in reasoning"],"best_for":["Educational platforms providing math tutoring and homework help","Researchers validating mathematical reasoning in LLMs","Teams building math-focused applications (calculators, problem solvers)"],"limitations":["Cannot perform symbolic computation (e.g., simplify algebraic expressions, solve differential equations symbolically); requires external tools like SymPy","Accuracy degrades on very complex problems (e.g., multi-step proofs, advanced calculus) despite thinking mode","Numerical precision is limited to floating-point accuracy; cannot handle arbitrary-precision arithmetic","No access to mathematical libraries or computational tools; all reasoning is text-based"],"requires":["Mathematical problem statement in natural language or LaTeX notation","Thinking mode enabled for best results on complex problems","Optional: external symbolic computation tools (SymPy, Wolfram Alpha) for verification"],"input_types":["natural language math problems","LaTeX mathematical notation","equations and expressions"],"output_types":["step-by-step solutions (text)","final numerical or symbolic answers","reasoning traces (in thinking mode)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-235b-a22b__cap_8","uri":"capability://text.generation.language.instruction.following.with.complex.multi.step.tasks","name":"instruction-following with complex multi-step tasks","description":"Qwen3-235B-A22B demonstrates strong instruction-following capabilities, understanding and executing complex, multi-step directives with specific constraints, formatting requirements, and conditional logic. The model can parse detailed instructions, maintain state across steps, and produce outputs that precisely match specified formats or requirements. This capability is enhanced by the thinking mode, which allows the model to decompose complex instructions into sub-steps before execution.","intents":["Execute complex workflows (e.g., 'analyze this code, identify bugs, suggest fixes, and generate tests')","Follow detailed formatting requirements (e.g., 'generate a JSON response with specific fields, sorted by date')","Handle conditional instructions (e.g., 'if the input contains X, do Y; otherwise do Z')"],"best_for":["Teams building LLM-powered automation workflows with complex requirements","Applications requiring precise output formatting and structure","Systems where instruction clarity and compliance are critical (e.g., legal, medical)"],"limitations":["Very long or ambiguous instructions may be misinterpreted; instruction clarity is critical","Nested or conditional instructions (>5 levels deep) may exceed the model's ability to track state correctly","No explicit instruction validation; the model may silently fail to follow parts of complex instructions","Instruction-following quality degrades when instructions conflict or contain contradictions"],"requires":["Clear, well-structured instructions in natural language","Optional: examples or templates to clarify expected output format","Thinking mode recommended for complex multi-step instructions"],"input_types":["natural language instructions","structured prompts with step-by-step directives","examples or templates"],"output_types":["text (following specified format)","code (if code generation is part of instructions)","structured data (JSON, CSV, etc., if specified)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible inference provider","Minimum context window support of 32K tokens (typical for Qwen3 variants)","Inference infrastructure supporting batch processing for optimal MoE efficiency","API parameter support for 'thinking_mode' or equivalent toggle (provider-dependent)","Sufficient context window allocation (minimum 32K tokens recommended for complex reasoning)","Inference provider that exposes thinking token counts for cost/latency monitoring","Inference provider supporting extended context windows (OpenRouter, Together AI, or similar)","Client-side context management to avoid exceeding maximum window size","Sufficient API quota/budget for long-context requests (typically 5-10x cost of short queries)","API access via OpenRouter or compatible provider"],"failure_modes":["MoE routing adds ~5-15ms latency overhead per forward pass due to gating computation and expert selection","Load balancing across experts can be uneven, causing GPU/TPU utilization imbalance in distributed inference","Fine-tuning MoE models requires careful handling of expert dropout and load-balancing losses to prevent expert collapse","Thinking mode increases time-to-first-token (TTFT) by 2-4x due to reasoning generation before response output","Thinking tokens consume context window budget, reducing effective context for very long documents (e.g., 100K+ token contexts)","Thinking mode cannot be selectively applied per-sentence; it's a model-level toggle affecting entire response generation","Quality of reasoning depends on model's ability to self-correct; poor initial reasoning traces may propagate to final answers","Inference latency scales linearly with context length; 128K token contexts may add 3-5x latency vs 4K contexts","Long contexts increase memory requirements even with MoE sparsity; distributed inference may be necessary for 100K+ tokens","Attention mechanisms may struggle with very long-range dependencies (>50K tokens) despite architectural improvements","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"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","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-235b-a22b"}},"signature":"kxwgy8OU3kNjPaCQocFPobDlF3cYm+wfHBZawtcdHL4O8M2JqY1RglXrj97KZ1D0KqIV10rOZ5LpKzGBaKOlCA==","signedAt":"2026-06-22T20:58:08.842Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-235b-a22b","artifact":"https://unfragile.ai/qwen-qwen3-235b-a22b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-235b-a22b","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"}}