{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-tencent-hunyuan-a13b-instruct","slug":"tencent-hunyuan-a13b-instruct","name":"Tencent: Hunyuan A13B Instruct","type":"model","url":"https://openrouter.ai/models/tencent~hunyuan-a13b-instruct","page_url":"https://unfragile.ai/tencent-hunyuan-a13b-instruct","categories":["chatbots-assistants","testing-quality"],"tags":["tencent","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.40e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-tencent-hunyuan-a13b-instruct__cap_0","uri":"capability://text.generation.language.mixture.of.experts.instruction.following.with.chain.of.thought.reasoning","name":"mixture-of-experts instruction following with chain-of-thought reasoning","description":"Hunyuan-A13B uses a sparse Mixture-of-Experts (MoE) architecture with 13B active parameters selected from an 80B parameter pool, enabling efficient instruction-following through dynamic expert routing. The model supports explicit chain-of-thought reasoning patterns, allowing it to decompose complex tasks into intermediate reasoning steps before generating final responses. This architecture reduces computational overhead during inference while maintaining reasoning capability through selective expert activation based on input tokens.","intents":["I need a model that can reason through multi-step problems while keeping inference costs low","I want to use chain-of-thought prompting to improve reasoning quality on complex tasks","I need instruction-following that scales to long contexts without proportional compute increases","I'm building an agent that needs to decompose tasks into reasoning chains before execution"],"best_for":["teams building reasoning-heavy AI applications with cost constraints","developers implementing multi-step task decomposition agents","organizations evaluating efficient alternatives to dense 70B+ models","builders prototyping chain-of-thought workflows at scale"],"limitations":["MoE routing adds latency variance — expert selection per token may cause unpredictable inference times vs dense models","Chain-of-thought reasoning requires explicit prompt engineering; model does not automatically generate reasoning traces without instruction","No built-in memory or context persistence across conversations — each request is stateless","Reasoning quality depends on prompt structure; poorly formatted chain-of-thought prompts may degrade output coherence","Unknown performance on specialized domains (medical, legal, code) relative to instruction-tuned baselines"],"requires":["API access via OpenRouter or direct Tencent endpoint","Prompt engineering knowledge for effective chain-of-thought structuring","Understanding of MoE inference patterns to optimize latency expectations","Support for text input up to model's context window (exact size not specified in artifact)"],"input_types":["text (natural language instructions)","text (code snippets for analysis or generation)","text (structured prompts with chain-of-thought templates)"],"output_types":["text (natural language responses)","text (reasoning traces with intermediate steps)","text (code generation or explanation)","structured reasoning chains (when prompted)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tencent-hunyuan-a13b-instruct__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.instruction.following","name":"multi-turn conversational instruction following","description":"Hunyuan-A13B is instruction-tuned to follow multi-turn conversational patterns, maintaining coherence across sequential user requests within a single session. The model processes each turn as context-aware input, allowing it to reference previous exchanges and adapt responses based on conversation history. This capability enables natural dialogue flows where the model understands implicit references, maintains consistent persona, and refines answers based on user feedback across turns.","intents":["I need a chatbot that understands context from previous messages in a conversation","I want to build an interactive assistant that can refine answers based on follow-up questions","I'm creating a multi-turn dialogue system where the model references earlier exchanges","I need conversational AI that maintains consistent tone and knowledge across turns"],"best_for":["developers building chatbot interfaces or conversational agents","teams creating customer support automation with context awareness","builders prototyping interactive tutoring or coaching systems","applications requiring natural back-and-forth dialogue with implicit context"],"limitations":["No explicit session management — conversation state must be managed by the caller; model has no built-in memory between separate API calls","Context window is finite; very long conversations will lose early context as token limit approaches","No explicit instruction to 'forget' previous turns — all history is treated equally in context","Multi-turn performance degrades with extremely long conversation histories due to context dilution"],"requires":["API client capable of maintaining conversation history and passing full context with each request","Understanding of token counting to manage context window usage across turns","Prompt engineering to structure multi-turn exchanges effectively"],"input_types":["text (user messages in conversational format)","text (conversation history as context)"],"output_types":["text (contextually-aware responses)","text (refined or clarified answers based on follow-ups)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tencent-hunyuan-a13b-instruct__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.explanation.with.reasoning","name":"code generation and technical explanation with reasoning","description":"Hunyuan-A13B can generate code snippets and provide technical explanations by leveraging its instruction-tuning and chain-of-thought capability. When prompted with code-related tasks, the model can produce syntactically valid code in multiple languages, explain implementation logic, and reason through algorithmic problems. The MoE architecture may route to specialized experts for code understanding, though this is implementation-dependent and not explicitly documented.","intents":["I need to generate code snippets for specific programming tasks","I want explanations of how code works with step-by-step reasoning","I'm building a code review or explanation tool that needs to reason about implementation","I need to generate code in multiple languages with consistent quality"],"best_for":["developers using AI for code generation and technical documentation","teams building code explanation or tutoring systems","builders creating code review assistants or linting tools","organizations prototyping code-to-documentation pipelines"],"limitations":["No real-time code execution or validation — generated code is not tested; caller must verify correctness","Code quality varies by language and complexity; performance on low-resource or niche languages unknown","No built-in awareness of project context, dependencies, or existing codebase — treats each request in isolation","Chain-of-thought reasoning for code requires explicit prompting; model does not automatically generate implementation plans","Unknown performance on security-sensitive code generation (cryptography, authentication) — no documented safety guardrails"],"requires":["API access via OpenRouter","Prompt engineering to specify language, framework, and code style preferences","External code validation and testing infrastructure","Understanding of model limitations for security-critical code"],"input_types":["text (natural language code requests)","text (code snippets for explanation or refactoring)","text (algorithmic problem descriptions)"],"output_types":["text (generated code in specified language)","text (code explanations with reasoning)","text (implementation strategies or pseudocode)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tencent-hunyuan-a13b-instruct__cap_3","uri":"capability://text.generation.language.benchmark.competitive.instruction.following.across.diverse.tasks","name":"benchmark-competitive instruction following across diverse tasks","description":"Hunyuan-A13B is designed to achieve competitive performance on standard instruction-following benchmarks (MMLU, HellaSwag, TruthfulQA, etc.) through instruction-tuning and MoE specialization. The model's architecture allows different experts to specialize in different task domains, enabling strong cross-domain performance without proportional parameter scaling. This capability reflects the model's training on diverse instruction datasets and evaluation against established baselines.","intents":["I need a model with proven benchmark performance for general-purpose instruction following","I want to evaluate model quality against standard metrics before deployment","I'm comparing models and need to understand relative performance on common benchmarks","I need a model that performs well across diverse task types without specialization"],"best_for":["teams evaluating models for general-purpose deployment","researchers benchmarking instruction-following models","organizations comparing cost-performance trade-offs across models","builders selecting a baseline model for fine-tuning or adaptation"],"limitations":["Benchmark performance does not guarantee real-world task performance; benchmark tasks may not reflect production use cases","Unknown performance on out-of-distribution tasks or adversarial inputs not covered by standard benchmarks","Benchmark scores are static; model performance may degrade on novel or rapidly-evolving domains","No transparency on which benchmarks the model was explicitly trained or tuned on, risking benchmark overfitting"],"requires":["Access to published benchmark results (not provided in artifact; requires external research)","Understanding of benchmark limitations and what they measure","Evaluation infrastructure to test on your specific use cases beyond published benchmarks"],"input_types":["text (benchmark task prompts)"],"output_types":["text (responses evaluated against benchmark metrics)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tencent-hunyuan-a13b-instruct__cap_4","uri":"capability://tool.use.integration.api.based.inference.with.openrouter.integration","name":"api-based inference with openrouter integration","description":"Hunyuan-A13B is accessible via OpenRouter's API, providing a managed inference endpoint without requiring local deployment or infrastructure management. The integration handles model loading, batching, and scaling transparently, exposing a standard REST API interface for text generation. Developers interact with the model through HTTP requests, specifying parameters like temperature, max tokens, and top-p sampling, with responses streamed or returned in full depending on configuration.","intents":["I need to use Hunyuan without managing my own GPU infrastructure","I want to integrate a Tencent model into my application via a standard API","I need to compare Hunyuan with other models using a unified API interface","I want to avoid the complexity of model deployment and focus on application logic"],"best_for":["startups and small teams without ML infrastructure","developers prototyping AI features quickly without deployment overhead","organizations evaluating multiple models through a unified API","applications requiring managed scaling and uptime guarantees"],"limitations":["API latency adds overhead vs local inference; typical response times unknown but expect 500ms-2s per request","Pricing is per-token; high-volume applications may face significant costs compared to self-hosted models","No fine-tuning support documented; model is fixed and cannot be adapted to specific domains","API rate limits and quota management required; burst traffic may be throttled","Dependency on OpenRouter's availability and reliability; service outages affect all dependent applications","No direct access to model internals, embeddings, or intermediate representations"],"requires":["OpenRouter API key","HTTP client library (curl, requests, axios, etc.)","Understanding of OpenRouter's API specification and parameter formats","Budget for per-token API costs"],"input_types":["text (prompts via API request body)"],"output_types":["text (streamed or full responses via HTTP)","structured metadata (token counts, model info)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-tencent-hunyuan-a13b-instruct__cap_5","uri":"capability://text.generation.language.streaming.text.generation.with.token.level.control","name":"streaming text generation with token-level control","description":"Hunyuan-A13B supports streaming generation through OpenRouter's API, allowing responses to be consumed token-by-token as they are generated rather than waiting for full completion. This capability enables real-time user feedback, progressive rendering in UIs, and early stopping based on application logic. The model exposes sampling parameters (temperature, top-p, top-k) for fine-grained control over generation behavior, allowing tuning of output diversity and determinism.","intents":["I need to stream responses to users in real-time for better UX","I want to implement early stopping or dynamic response length based on application state","I need to control output randomness and diversity through sampling parameters","I'm building a chat interface that needs progressive token rendering"],"best_for":["web and mobile applications requiring real-time response streaming","chat interfaces and conversational UIs","applications with dynamic response length requirements","builders implementing sophisticated sampling strategies"],"limitations":["Streaming adds complexity to error handling; partial responses may be incomplete if connection drops","Token-level control requires understanding of sampling parameters; suboptimal settings may degrade quality","No built-in token filtering or post-processing; unsafe or unwanted tokens are not automatically removed","Streaming latency is variable; first token time and inter-token latency depend on server load and expert routing"],"requires":["HTTP client with streaming support (Server-Sent Events or chunked transfer encoding)","Understanding of sampling parameters (temperature, top-p, top-k) and their effects","Error handling for partial responses and connection failures","OpenRouter API key with streaming enabled"],"input_types":["text (prompts)"],"output_types":["text (streamed tokens via SSE or chunked HTTP)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct Tencent endpoint","Prompt engineering knowledge for effective chain-of-thought structuring","Understanding of MoE inference patterns to optimize latency expectations","Support for text input up to model's context window (exact size not specified in artifact)","API client capable of maintaining conversation history and passing full context with each request","Understanding of token counting to manage context window usage across turns","Prompt engineering to structure multi-turn exchanges effectively","API access via OpenRouter","Prompt engineering to specify language, framework, and code style preferences","External code validation and testing infrastructure"],"failure_modes":["MoE routing adds latency variance — expert selection per token may cause unpredictable inference times vs dense models","Chain-of-thought reasoning requires explicit prompt engineering; 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