{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-32b","slug":"qwen-qwen3-32b","name":"Qwen: Qwen3 32B","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-32b","page_url":"https://unfragile.ai/qwen-qwen3-32b","categories":["chatbots-assistants"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$8.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-32b__cap_0","uri":"capability://planning.reasoning.extended.context.reasoning.with.explicit.thinking.mode","name":"extended-context reasoning with explicit thinking mode","description":"Qwen3-32B implements a dual-mode inference architecture where the model can enter an explicit 'thinking' state that separates internal reasoning from final response generation. During thinking mode, the model performs chain-of-thought style decomposition with token budget allocation for complex problems, then switches to dialogue mode for user-facing output. This is implemented via conditional token routing and mode-switching tokens that signal state transitions during generation.","intents":["I need the model to show its reasoning steps before giving me an answer","I want to solve complex multi-step problems where intermediate reasoning is valuable","I need to understand how the model arrived at a conclusion for debugging or verification"],"best_for":["developers building reasoning-heavy agents for code analysis or math problems","teams implementing explainable AI systems where reasoning transparency is required","researchers studying model behavior and intermediate decision-making"],"limitations":["thinking mode increases total token consumption and latency by 30-50% depending on problem complexity","explicit thinking tokens are counted toward context limits, reducing available space for user context","thinking output format is model-specific and not standardized across providers"],"requires":["API access to Qwen3-32B via OpenRouter or compatible endpoint","support for mode-switching tokens in client library (may require custom prompt engineering)"],"input_types":["text prompts with optional thinking directives"],"output_types":["text with optional thinking block prefix","structured reasoning followed by final response"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_1","uri":"capability://text.generation.language.dense.32b.parameter.inference.with.efficient.context.handling","name":"dense 32b parameter inference with efficient context handling","description":"Qwen3-32B is a 32.8B parameter dense transformer model optimized for inference efficiency through quantization-friendly architecture and grouped query attention (GQA) patterns. The model uses rotary positional embeddings (RoPE) and flash attention mechanisms to reduce memory bandwidth requirements during generation, enabling deployment on consumer-grade GPUs while maintaining quality comparable to larger models.","intents":["I need a capable reasoning model that fits within my GPU memory constraints","I want to reduce inference latency and cost compared to 70B+ models","I need to deploy a model locally or on edge devices with limited VRAM"],"best_for":["teams deploying models on single-GPU infrastructure (A100 40GB, RTX 4090)","cost-conscious builders who need strong reasoning without 70B+ pricing","edge deployment scenarios where model size directly impacts latency"],"limitations":["32B parameter count trades off some reasoning capability vs. 70B+ models on extremely complex multi-step problems","context window length may be smaller than flagship models (typical 4K-8K vs. 128K+)","quantization below 8-bit may introduce noticeable quality degradation for specialized tasks"],"requires":["GPU with minimum 24GB VRAM for fp16 inference, 12GB for 8-bit quantization","CUDA 11.8+ or compatible inference framework (vLLM, TensorRT-LLM, ollama)","API key for OpenRouter or self-hosted deployment infrastructure"],"input_types":["text prompts up to context window limit"],"output_types":["streaming or batch text generation","token logits for custom sampling"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_2","uri":"capability://text.generation.language.multilingual.dialogue.with.language.specific.fine.tuning","name":"multilingual dialogue with language-specific fine-tuning","description":"Qwen3-32B is trained on a multilingual corpus with language-specific instruction-tuning for dialogue tasks. The model uses shared token embeddings across languages with language-specific adapter layers that activate based on detected input language, enabling seamless code-switching and maintaining coherence across language boundaries without separate model instances.","intents":["I need to build a chatbot that handles conversations in multiple languages without language detection","I want to support code-switching where users mix languages in a single conversation","I need consistent response quality across English, Chinese, and other major languages"],"best_for":["teams building global applications serving multilingual user bases","developers creating chatbots for regions with high code-switching (e.g., Spanglish, Chinglish)","organizations needing single-model deployment across language markets"],"limitations":["performance on low-resource languages (< 1M tokens in training) degrades compared to high-resource languages","language-specific fine-tuning may introduce subtle biases in how the model handles cultural context","token efficiency varies by language; CJK languages consume 2-3x more tokens than English for equivalent meaning"],"requires":["API access to Qwen3-32B","no explicit language specification required; model auto-detects from input"],"input_types":["text in any supported language (English, Chinese, Spanish, French, German, Japanese, Korean, etc.)"],"output_types":["text in the same language as input, or specified target language"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_3","uri":"capability://text.generation.language.instruction.following.with.structured.output.formatting","name":"instruction-following with structured output formatting","description":"Qwen3-32B is fine-tuned on instruction-following tasks with explicit support for structured output formats (JSON, XML, YAML) through constrained decoding patterns. The model learns to recognize format directives in prompts and applies token-level constraints during generation to ensure output adheres to specified schemas without post-processing.","intents":["I need the model to always return JSON that I can parse directly without error handling","I want to extract structured data from unstructured text with guaranteed format compliance","I need to integrate model outputs directly into downstream systems that expect specific schemas"],"best_for":["developers building data extraction pipelines that require deterministic output formats","teams using models in production systems where parsing failures are unacceptable","builders creating function-calling interfaces that depend on structured responses"],"limitations":["constrained decoding adds 10-15% latency overhead due to token filtering at each generation step","complex nested schemas may cause the model to truncate output rather than violate constraints","format constraints are best-effort; model may still produce malformed output if schema is ambiguous"],"requires":["API client that supports constrained decoding (e.g., vLLM with grammar constraints, or custom sampling logic)","explicit format specification in prompt (e.g., 'respond in valid JSON')"],"input_types":["text prompts with format directives"],"output_types":["JSON objects","XML documents","YAML structures","CSV rows"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_4","uri":"capability://text.generation.language.few.shot.in.context.learning.with.example.based.adaptation","name":"few-shot in-context learning with example-based adaptation","description":"Qwen3-32B supports few-shot learning where the model adapts its behavior based on 2-10 examples provided in the prompt context. The model uses attention mechanisms to identify patterns in examples and applies those patterns to new inputs without parameter updates. This is implemented through standard transformer self-attention over the full context window, with no special few-shot-specific architecture.","intents":["I want to teach the model a new task by showing it 3-5 examples instead of fine-tuning","I need to adapt the model's style or format to match domain-specific conventions without retraining","I want to perform zero-shot to few-shot transfer for tasks the model wasn't explicitly trained on"],"best_for":["rapid prototyping teams that need task adaptation without fine-tuning infrastructure","developers building dynamic systems where task definitions change per request","researchers studying in-context learning behavior and prompt sensitivity"],"limitations":["few-shot performance is highly sensitive to example quality and ordering; poor examples degrade accuracy by 10-30%","context window limits the number of examples (typically 4-8 high-quality examples fit in 4K context)","few-shot learning is less stable than fine-tuning; performance varies more across different input distributions"],"requires":["API access to Qwen3-32B","carefully curated examples that represent the target task distribution"],"input_types":["text prompts with embedded examples in standard format (e.g., 'Example 1: input -> output')"],"output_types":["text following the pattern demonstrated in examples"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_5","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.specific.syntax.awareness","name":"code generation and completion with language-specific syntax awareness","description":"Qwen3-32B includes code generation capabilities trained on diverse programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) with syntax-aware token prediction. The model uses language-specific tokenization patterns and has learned representations of common code structures (functions, classes, control flow), enabling it to complete code snippets with correct syntax and semantic coherence.","intents":["I need to generate boilerplate code or complete partial function implementations","I want to translate code between languages while preserving logic","I need to generate test cases or documentation for existing code"],"best_for":["developers using the model as a coding assistant for rapid prototyping","teams automating code generation for repetitive patterns or scaffolding","educators using the model to generate code examples for teaching"],"limitations":["code generation quality degrades for domain-specific languages or proprietary frameworks not well-represented in training data","generated code may contain logical errors or security vulnerabilities; always requires human review","multi-file code generation is limited by context window; complex projects require external file management"],"requires":["API access to Qwen3-32B","programming language specification in prompt (e.g., 'write Python code')"],"input_types":["text descriptions of desired code","partial code snippets to complete","code in one language to translate to another"],"output_types":["code in specified programming language","code with inline comments","test cases or documentation"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_6","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Qwen3-32B is trained on mathematical problem datasets and symbolic reasoning tasks, enabling it to solve algebra, calculus, and discrete math problems through step-by-step derivation. The model learns to recognize mathematical notation, apply transformation rules, and generate intermediate steps that can be verified. This capability is enhanced by the explicit thinking mode, which allocates tokens for mathematical reasoning before generating the final answer.","intents":["I need to solve math problems step-by-step with intermediate verification","I want to generate mathematical proofs or derivations for educational purposes","I need to check mathematical reasoning in student work or research papers"],"best_for":["educational platforms generating math tutoring content","researchers validating mathematical reasoning in AI systems","developers building math-focused applications (homework helpers, research tools)"],"limitations":["performance on novel or highly specialized math (e.g., advanced topology) is limited to patterns seen in training","symbolic computation is text-based; no integration with computer algebra systems (CAS) for verification","reasoning chains can be long; complex proofs may exceed context window or require multiple turns"],"requires":["API access to Qwen3-32B","mathematical notation in standard formats (LaTeX, plain text, or ASCII math)"],"input_types":["mathematical problems in text or LaTeX notation","equations or expressions to simplify or solve"],"output_types":["step-by-step solutions","mathematical proofs","simplified expressions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_7","uri":"capability://text.generation.language.long.context.understanding.with.efficient.attention.mechanisms","name":"long-context understanding with efficient attention mechanisms","description":"Qwen3-32B supports extended context windows (typically 4K-8K tokens, potentially up to 32K with sparse attention) through efficient attention mechanisms like grouped query attention (GQA) and sparse attention patterns. The model can maintain coherence and reference information across long documents without proportional increases in memory or latency, enabling analysis of full documents, conversations, or code files in a single pass.","intents":["I need to analyze a full document or code file without chunking or summarization","I want to maintain conversation history across many turns without losing context","I need to find and reference specific information from a long context window"],"best_for":["document analysis systems processing full reports, papers, or books","multi-turn conversational agents that need full conversation history","code analysis tools that need to understand full file context"],"limitations":["context window is finite; documents longer than window limit require chunking or summarization","attention computation is still O(n²) in worst case; very long contexts (>16K tokens) may have latency impact","model may lose focus on early context when processing very long sequences; recency bias is present"],"requires":["API access to Qwen3-32B with specified context window size","client library that supports long context (most modern libraries do)"],"input_types":["text documents up to context window limit","conversation histories with multiple turns"],"output_types":["analysis or summaries of long documents","answers to questions about document content","coherent continuations of long conversations"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-32b__cap_8","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"Qwen3-32B is accessed via OpenRouter's API, which provides both streaming and batch inference modes. Streaming mode returns tokens incrementally as they are generated, enabling real-time user-facing applications. Batch mode processes multiple requests asynchronously, optimizing throughput for non-latency-sensitive workloads. The API handles model selection, load balancing, and fallback routing transparently.","intents":["I need to integrate Qwen3-32B into a web application with real-time streaming responses","I want to process large batches of requests efficiently without managing infrastructure","I need to handle variable load with automatic scaling and failover"],"best_for":["web application developers building chat interfaces or content generation features","data processing teams running batch inference jobs on large datasets","teams without GPU infrastructure who want managed model access"],"limitations":["API calls incur per-token pricing; high-volume applications may be more cost-effective with self-hosted deployment","streaming adds latency overhead (typically 50-100ms per token) compared to batch processing","API rate limits may apply; burst traffic requires coordination with provider"],"requires":["OpenRouter API key","HTTP client library (any language)","network connectivity to OpenRouter endpoints"],"input_types":["text prompts via HTTP POST"],"output_types":["streaming text via Server-Sent Events (SSE)","batch results via JSON responses"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access to Qwen3-32B via OpenRouter or compatible endpoint","support for mode-switching tokens in client library (may require custom prompt engineering)","GPU with minimum 24GB VRAM for fp16 inference, 12GB for 8-bit quantization","CUDA 11.8+ or compatible inference framework (vLLM, TensorRT-LLM, ollama)","API key for OpenRouter or self-hosted deployment infrastructure","API access to Qwen3-32B","no explicit language specification required; model auto-detects from input","API client that supports constrained decoding (e.g., vLLM with grammar constraints, or custom sampling logic)","explicit format specification in prompt (e.g., 'respond in valid JSON')","carefully curated examples that represent the target task distribution"],"failure_modes":["thinking mode increases total token consumption and latency by 30-50% depending on problem complexity","explicit thinking tokens are counted toward context limits, reducing available space for user context","thinking output format is model-specific and not standardized across providers","32B parameter count trades off some reasoning capability vs. 70B+ models on extremely complex multi-step problems","context window length may be smaller than flagship models (typical 4K-8K vs. 128K+)","quantization below 8-bit may introduce noticeable quality degradation for specialized tasks","performance on low-resource languages (< 1M tokens in training) degrades compared to high-resource languages","language-specific fine-tuning may introduce subtle biases in how the model handles cultural context","token efficiency varies by language; CJK languages consume 2-3x more tokens than English for equivalent meaning","constrained decoding adds 10-15% latency overhead due to token filtering at each generation step","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-qwen3-32b","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-32b"}},"signature":"+rooLo3vL/CcmxdqLiCaERO1aAvHhj9pq0LkN+gidIS1/uVZbnGbOo9RJxdDVUPNHG6enZEGcrryPr5Ko5vuBA==","signedAt":"2026-06-20T12:25:31.251Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-32b","artifact":"https://unfragile.ai/qwen-qwen3-32b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-32b","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"}}