{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-qwen-qwen3-14b","slug":"qwen-qwen3-14b","name":"Qwen: Qwen3 14B","type":"model","url":"https://openrouter.ai/models/qwen~qwen3-14b","page_url":"https://unfragile.ai/qwen-qwen3-14b","categories":["chatbots-assistants"],"tags":["qwen","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$6.00e-8 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-qwen-qwen3-14b__cap_0","uri":"capability://planning.reasoning.extended.context.reasoning.with.explicit.thinking.mode","name":"extended-context reasoning with explicit thinking mode","description":"Qwen3-14B implements a dual-mode inference architecture where the model can enter an explicit 'thinking' state before generating responses, allowing it to perform chain-of-thought reasoning over extended contexts. The thinking mode operates as an intermediate token generation phase that remains hidden from the user, enabling the model to decompose complex problems before committing to final output. This is implemented via conditional token routing during decoding, where special thinking tokens trigger an internal reasoning loop before the response generation phase begins.","intents":["I need a model that can solve multi-step math problems by showing its work internally before answering","I want to use a smaller model that can reason about complex logic without exposing intermediate steps to users","I need reliable answers on tasks requiring planning or decomposition without paying for a 70B+ parameter model"],"best_for":["developers building reasoning-heavy applications with latency constraints","teams deploying on resource-constrained infrastructure who need reasoning capabilities","builders creating educational or tutoring systems where internal reasoning is valuable but hidden reasoning is preferred"],"limitations":["thinking mode adds 30-50% latency overhead compared to direct response generation","thinking token budget is fixed per request — cannot dynamically expand reasoning depth for harder problems","no visibility into thinking process for debugging or auditing reasoning quality","thinking mode effectiveness degrades on tasks outside training distribution (novel problem types)"],"requires":["API access via OpenRouter or compatible inference endpoint","support for extended token sequences (minimum 8K context window)","client-side handling of thinking token filtering if exposing raw output"],"input_types":["text","structured prompts with reasoning directives"],"output_types":["text response (with optional hidden thinking tokens)","structured reasoning traces (if client captures intermediate tokens)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_1","uri":"capability://text.generation.language.seamless.dialogue.context.management.with.multi.turn.state","name":"seamless dialogue context management with multi-turn state","description":"Qwen3-14B maintains conversation state across multiple turns using a sliding-window context mechanism that preserves semantic coherence while managing memory efficiently. The model uses attention masking patterns optimized for dialogue, where recent turns receive full attention while older context is progressively compressed through a learned attention decay. This enables the model to track entity references, maintain topic continuity, and resolve pronouns across 10+ turn conversations without explicit state management from the application layer.","intents":["I need a chatbot that remembers context across multiple user messages without me managing conversation history","I want to build a multi-turn dialogue system where the model understands pronoun references and topic shifts naturally","I need efficient context handling so conversation memory doesn't grow linearly with turn count"],"best_for":["developers building conversational AI without complex state management infrastructure","teams deploying chatbots where conversation length is unpredictable","applications requiring natural dialogue without explicit prompt engineering for context"],"limitations":["context window is fixed at model training time — cannot extend beyond ~8K-32K tokens depending on deployment","older conversation turns are progressively forgotten as new context arrives (no true long-term memory)","no built-in mechanism to explicitly weight or pin important context from earlier turns","multi-turn performance degrades on tasks requiring precise recall of facts from turn 1 after 20+ turns"],"requires":["API client that maintains conversation history and passes full context on each request","understanding of token counting to avoid exceeding context window mid-conversation","optional: external conversation storage if persistence across sessions is needed"],"input_types":["text messages","conversation history arrays"],"output_types":["text response","dialogue acts (implicit — not structured output)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_2","uri":"capability://text.generation.language.instruction.following.with.structured.output.constraints","name":"instruction-following with structured output constraints","description":"Qwen3-14B implements constrained decoding via a token-level filtering mechanism that enforces adherence to output format specifications during generation. When given structured instructions (JSON schema, XML tags, code blocks), the model uses a constraint satisfaction layer that masks invalid tokens at each generation step, ensuring the output conforms to the specified format without post-processing. This is implemented through a combination of prefix-aware decoding and vocabulary filtering based on the instruction context.","intents":["I need the model to always return JSON in a specific schema without parsing errors","I want to extract structured data from text and guarantee the output format matches my application's expectations","I need to generate code in specific languages or frameworks with reliable syntax compliance"],"best_for":["developers building data extraction pipelines where format reliability is critical","teams integrating LLM outputs directly into downstream systems without validation","applications requiring structured outputs (JSON, XML, code) with zero tolerance for malformed responses"],"limitations":["constraint enforcement adds 15-25% latency per token due to vocabulary filtering overhead","overly restrictive schemas can force the model to truncate or omit information to maintain format compliance","complex nested structures may cause the model to generate valid syntax but semantically incorrect content","no support for dynamic constraints that change based on previous tokens (e.g., conditional schema fields)"],"requires":["API client with support for constraint specification (schema parameter or similar)","well-defined output schema provided at inference time","understanding that constraints may reduce response quality if schema is too rigid"],"input_types":["text prompts with format instructions","JSON schema definitions","XML/code format specifications"],"output_types":["JSON objects","XML documents","code blocks with guaranteed syntax validity"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_3","uri":"capability://text.generation.language.multilingual.text.generation.with.language.specific.optimization","name":"multilingual text generation with language-specific optimization","description":"Qwen3-14B was trained on a balanced multilingual corpus and implements language-aware token routing during inference, where the model detects the input language and applies language-specific decoding parameters (temperature scaling, vocabulary weighting) to optimize generation quality. The model maintains separate attention patterns for different language families (CJK, Latin, Arabic scripts) learned during pretraining, enabling it to generate fluent text across 30+ languages without explicit language tags. Language detection happens implicitly through the first few input tokens, triggering appropriate decoding strategies.","intents":["I need to build a chatbot that serves users in multiple languages without separate model deployments","I want to translate content while preserving context and tone from the original language","I need a model that can handle code-switching (mixing languages in a single response) naturally"],"best_for":["teams building global applications serving multiple language communities","developers creating translation or localization tools without language-specific fine-tuning","applications where users may mix languages in a single request"],"limitations":["quality varies significantly across languages — English and Chinese are highest quality, low-resource languages (Swahili, Tagalog) have degraded performance","language detection from context alone can fail on short inputs or code-heavy prompts","no explicit language specification parameter — must rely on implicit detection which can be ambiguous","multilingual training introduces ~5-10% performance degradation on English-only tasks compared to English-specialized models"],"requires":["input text in supported language (30+ languages including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, etc.)","no special language tokens or prefixes required","awareness that language quality is not uniform across all supported languages"],"input_types":["text in any supported language","code-switched text (mixed languages)"],"output_types":["text in the detected input language","code-switched responses if input contains multiple languages"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_4","uri":"capability://text.generation.language.efficient.inference.with.quantization.aware.model.architecture","name":"efficient inference with quantization-aware model architecture","description":"Qwen3-14B is architected with quantization-friendly design patterns including layer normalization placement, activation function choices, and weight distribution that maintain performance when quantized to 8-bit or 4-bit precision. The model uses a modified attention mechanism with reduced precision requirements for key-value caches, enabling efficient deployment on consumer GPUs and edge devices. Quantization is applied post-training through a calibration process that preserves model quality while reducing memory footprint by 75% (4-bit) or 50% (8-bit) compared to full precision.","intents":["I need to deploy a capable model on a single GPU with limited VRAM (8GB-24GB)","I want to run inference on edge devices or mobile hardware without sacrificing too much quality","I need to reduce inference costs by lowering memory requirements and enabling higher batch sizes"],"best_for":["developers deploying models on consumer-grade hardware (RTX 3090, RTX 4090, M-series Macs)","teams running inference on edge devices or mobile platforms","cost-sensitive applications where reducing memory footprint directly reduces infrastructure costs"],"limitations":["4-bit quantization introduces 3-8% quality degradation on reasoning tasks compared to full precision","quantized models require specialized inference libraries (llama.cpp, vLLM with quantization support, GPTQ)","quantization calibration is one-time cost but requires representative data and compute time","dynamic quantization (per-token) is not supported — only static quantization schemes"],"requires":["quantization library compatible with Qwen3 (GPTQ, AWQ, or similar)","calibration dataset if using custom quantization","inference framework supporting quantized model loading (vLLM, llama.cpp, Ollama, etc.)","GPU with at least 8GB VRAM for 4-bit quantized model, 16GB for 8-bit"],"input_types":["text prompts"],"output_types":["text responses"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_5","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.binding","name":"function calling with schema-based tool binding","description":"Qwen3-14B supports tool use through a schema-based function calling mechanism where the model learns to emit structured function calls in response to prompts that describe available tools. The model generates function calls as special tokens that encode the function name and parameters, which are then parsed by the client and executed. This is implemented via instruction tuning on function-calling examples, where the model learns to recognize when a tool is needed and format the call correctly. The schema is provided as part of the system prompt, and the model learns to match user intents to appropriate function signatures.","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 can use tools like calculators, web search, or database queries","I need reliable function calling without hallucinated function names or malformed parameters"],"best_for":["developers building AI agents that need to interact with external systems","teams creating autonomous workflows that combine LLM reasoning with tool execution","applications where the model must decide which of multiple tools to use based on user intent"],"limitations":["function calling quality degrades with complex schemas (10+ parameters per function or deeply nested objects)","no built-in retry logic if function execution fails — application must handle errors and re-prompt","model may hallucinate function names or parameters if the schema is ambiguous or poorly documented","no support for streaming function calls — entire call must be generated before execution"],"requires":["function schema definition in JSON format","client-side function execution framework to handle generated calls","error handling for cases where model generates invalid function calls","optional: few-shot examples in the prompt to improve function calling accuracy"],"input_types":["text prompts with function schema in system message","user queries that may trigger tool use"],"output_types":["function call tokens (parsed into function name + parameters)","text responses (when no tool is needed)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_6","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.specific.patterns","name":"code generation and completion with language-specific patterns","description":"Qwen3-14B was trained on a large corpus of code across multiple programming languages and implements language-specific generation patterns learned during pretraining. The model can complete code snippets, generate functions from docstrings, and refactor code while maintaining language-specific idioms and conventions. Language detection happens implicitly from the code context (imports, syntax), and the model applies language-specific token probabilities to favor idiomatic code. The model supports 20+ programming languages including Python, JavaScript, Java, C++, Go, Rust, and SQL.","intents":["I need code completion that understands the programming language and generates idiomatic code","I want to generate functions from natural language descriptions or docstrings","I need to refactor or optimize code while preserving functionality and language conventions"],"best_for":["developers using code generation as a productivity tool in their IDE or editor","teams building code generation pipelines for scaffolding or boilerplate generation","applications where code quality and idiomaticity matter (not just syntactic correctness)"],"limitations":["code generation quality varies by language — Python and JavaScript are highest quality, less common languages (Rust, Go) have lower quality","model may generate syntactically correct but semantically incorrect code (e.g., off-by-one errors, logic bugs)","no built-in testing or validation — generated code must be reviewed and tested","context window limits how much code the model can see, affecting completion quality for large files"],"requires":["code context (file content, imports, function signatures) provided as input","understanding that generated code requires review and testing","optional: language-specific linters or formatters to validate generated code"],"input_types":["code snippets with incomplete sections","docstrings or natural language descriptions","code to refactor or optimize"],"output_types":["completed code","generated functions or classes","refactored code"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_7","uri":"capability://memory.knowledge.knowledge.grounded.response.generation.with.retrieval.integration","name":"knowledge-grounded response generation with retrieval integration","description":"Qwen3-14B can be integrated with external knowledge sources through a retrieval-augmented generation (RAG) pattern where relevant documents are retrieved and provided as context before generation. The model learns to cite and reference retrieved documents, incorporating external knowledge into responses while maintaining coherence. The integration is implemented at the application layer — the model itself doesn't perform retrieval, but it's trained to effectively use provided context and can be prompted to cite sources. The model learns to distinguish between its training knowledge and provided context, reducing hallucination when grounded in retrieved documents.","intents":["I need to build a Q&A system that answers questions based on a knowledge base or document corpus","I want the model to cite sources when answering questions, grounding responses in retrieved documents","I need to reduce hallucination by providing relevant context from a knowledge base before generation"],"best_for":["teams building knowledge base Q&A systems or documentation assistants","applications requiring source attribution or citation of retrieved documents","use cases where reducing hallucination is critical (medical, legal, financial domains)"],"limitations":["model quality depends entirely on retrieval quality — irrelevant or incorrect retrieved documents degrade response quality","no built-in retrieval mechanism — requires external vector database or search system","model may ignore retrieved context if it conflicts with training knowledge (especially for well-known facts)","citation accuracy is not guaranteed — model may cite documents without actually using them or cite incorrectly"],"requires":["external retrieval system (vector database, search engine, or similar)","documents or knowledge base to retrieve from","prompt engineering to instruct the model to cite sources","optional: evaluation framework to measure citation accuracy and relevance"],"input_types":["user queries","retrieved document context (provided in prompt)"],"output_types":["text responses with optional citations","structured responses with source references"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_8","uri":"capability://text.generation.language.long.context.understanding.with.efficient.attention.mechanisms","name":"long-context understanding with efficient attention mechanisms","description":"Qwen3-14B supports extended context windows (up to 32K tokens or more depending on deployment) through efficient attention mechanisms that reduce computational complexity from quadratic to linear or near-linear. The model uses a combination of sparse attention patterns, local windowing, and hierarchical attention to process long documents without the memory and compute overhead of full attention. This enables the model to understand and reason over entire documents, codebases, or conversation histories without truncation, while maintaining reasonable latency.","intents":["I need to analyze entire documents or codebases without splitting them into chunks","I want to maintain conversation context over 50+ turns without losing information from early messages","I need to process long-form content (research papers, books, code repositories) in a single pass"],"best_for":["developers building document analysis or summarization tools","teams creating long-form conversation systems (customer support, tutoring)","applications processing code repositories or technical documentation"],"limitations":["long-context processing adds latency — processing 32K tokens takes 5-10x longer than 4K tokens","quality may degrade on tasks requiring precise recall of information from the middle of very long contexts (lost-in-the-middle problem)","long-context support requires specific inference implementations (vLLM, Ollama with long-context support) — not all endpoints support it","cost scales linearly with context length — longer inputs consume more tokens and increase API costs"],"requires":["inference endpoint supporting long-context (32K+ tokens)","awareness of token counting to avoid exceeding context window","optional: document chunking strategy if input exceeds maximum context window"],"input_types":["long text documents","code files or repositories","conversation histories with many turns"],"output_types":["text responses","summaries","analysis or insights from long-form content"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-qwen-qwen3-14b__cap_9","uri":"capability://safety.moderation.safety.aligned.response.generation.with.content.filtering","name":"safety-aligned response generation with content filtering","description":"Qwen3-14B was fine-tuned with constitutional AI and safety alignment techniques to reduce harmful outputs, including refusals for requests involving violence, illegal activities, or explicit content. The model implements a multi-layer safety approach: instruction tuning to recognize harmful requests, learned refusal patterns, and optional content filtering at the token level during generation. Safety alignment is applied during training rather than as a post-processing step, making refusals more natural and reducing jailbreak susceptibility. The model can be configured to adjust safety levels (strict, moderate, permissive) through prompt engineering.","intents":["I need a model that refuses harmful requests and won't generate illegal or violent content","I want to deploy a model in production with confidence that it won't generate inappropriate content","I need to adjust safety levels for different use cases (strict for public-facing, more permissive for research)"],"best_for":["teams deploying models in production where safety is critical (customer-facing applications)","applications serving diverse audiences where content appropriateness matters","organizations with compliance requirements (healthcare, finance, education)"],"limitations":["safety alignment may cause over-refusal — the model may refuse legitimate requests that are tangentially related to harmful content","safety mechanisms are not foolproof — determined adversaries can still jailbreak the model through prompt injection","safety tuning may reduce model performance on edge cases or niche domains","no fine-grained control over what content is filtered — safety is applied as a broad policy"],"requires":["understanding that safety is probabilistic, not deterministic","optional: monitoring and logging of refused requests to identify over-refusal patterns","optional: custom safety fine-tuning if default safety levels don't match use case"],"input_types":["text prompts (any content)"],"output_types":["text responses (with potential refusals for harmful requests)","refusal messages explaining why a request was declined"],"categories":["safety-moderation","text-generation-language"],"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 endpoint","support for extended token sequences (minimum 8K context window)","client-side handling of thinking token filtering if exposing raw output","API client that maintains conversation history and passes full context on each request","understanding of token counting to avoid exceeding context window mid-conversation","optional: external conversation storage if persistence across sessions is needed","API client with support for constraint specification (schema parameter or similar)","well-defined output schema provided at inference time","understanding that constraints may reduce response quality if schema is too rigid","input text in supported language (30+ languages including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic, etc.)"],"failure_modes":["thinking mode adds 30-50% latency overhead compared to direct response generation","thinking token budget is fixed per request — cannot dynamically expand reasoning depth for harder problems","no visibility into thinking process for debugging or auditing reasoning quality","thinking mode effectiveness degrades on tasks outside training distribution (novel problem types)","context window is fixed at model training time — cannot extend beyond ~8K-32K tokens depending on deployment","older conversation turns are progressively forgotten as new context arrives (no true long-term memory)","no built-in mechanism to explicitly weight or pin important context from earlier turns","multi-turn performance degrades on tasks requiring precise recall of facts from turn 1 after 20+ turns","constraint enforcement adds 15-25% latency per token due to vocabulary filtering overhead","overly restrictive schemas can force the model to truncate or omit information to maintain format compliance","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-qwen3-14b","compare_url":"https://unfragile.ai/compare?artifact=qwen-qwen3-14b"}},"signature":"X0BPAH/6dYrR7qQJLsRrcNvwzDCIB3BzgJISJuWl/O6P2tnkpgMn92/JTcFv2CxiS95f3Vrd6ljmSWK0LisAAQ==","signedAt":"2026-06-20T02:12:11.494Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwen-qwen3-14b","artifact":"https://unfragile.ai/qwen-qwen3-14b","verify":"https://unfragile.ai/api/v1/verify?slug=qwen-qwen3-14b","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"}}