{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-upstage-solar-pro-3","slug":"upstage-solar-pro-3","name":"Upstage: Solar Pro 3","type":"model","url":"https://openrouter.ai/models/upstage~solar-pro-3","page_url":"https://unfragile.ai/upstage-solar-pro-3","categories":["chatbots-assistants"],"tags":["upstage","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.50e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-upstage-solar-pro-3__cap_0","uri":"capability://text.generation.language.mixture.of.experts.language.generation.with.selective.token.routing","name":"mixture-of-experts language generation with selective token routing","description":"Solar Pro 3 implements a Mixture-of-Experts (MoE) architecture with 102B total parameters but only activates 12B parameters per forward pass through learned gating mechanisms that route tokens to specialized expert subnetworks. This selective activation pattern reduces computational cost while maintaining model capacity, using sparse expert selection rather than dense transformer layers for each token position.","intents":["I need a language model that can handle complex reasoning tasks without the computational overhead of a fully dense 102B parameter model","I want to deploy a high-performance LLM with lower inference latency and reduced memory footprint compared to dense alternatives","I need to balance model capability with cost-efficiency for high-volume API inference workloads"],"best_for":["teams building cost-sensitive LLM applications requiring high throughput","developers optimizing inference latency for real-time conversational AI","enterprises managing large-scale API inference budgets"],"limitations":["MoE architecture may exhibit load balancing issues where certain experts become over-utilized, reducing effective model diversity","Sparse activation patterns can introduce non-deterministic performance variance across different input distributions","Fine-tuning MoE models requires careful handling of expert routing to avoid expert collapse"],"requires":["API key for OpenRouter or direct Upstage API access","HTTP/REST client capability or SDK integration","Support for streaming or batch inference depending on use case"],"input_types":["text (natural language prompts)","code snippets","structured prompts with system instructions"],"output_types":["text (streaming or batch)","structured JSON responses","code generation output"],"categories":["text-generation-language","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_1","uri":"capability://text.generation.language.multi.turn.conversational.context.management.with.extended.context.windows","name":"multi-turn conversational context management with extended context windows","description":"Solar Pro 3 maintains conversation state across multiple turns by accepting full conversation history in each API request, with support for extended context windows that allow retention of longer dialogue histories and document context. The model processes the entire conversation context through its MoE routing mechanism, enabling coherent multi-turn interactions without explicit memory management.","intents":["I need to build a chatbot that maintains conversation coherence across 10+ turns without losing context","I want to provide long-form documents or conversation histories as context for follow-up questions","I need to implement a customer support agent that remembers previous interactions within a single session"],"best_for":["developers building conversational AI applications with stateless API architectures","teams implementing document-aware Q&A systems requiring full context retention","customer service platforms needing multi-turn dialogue without external state stores"],"limitations":["Each API call must include full conversation history, increasing payload size and latency for long conversations","No built-in session persistence — conversation state must be managed by the client application","Context window size limits the maximum conversation length that can be maintained in a single session"],"requires":["API client capable of managing conversation history arrays","HTTP POST requests with JSON payloads containing message arrays","Client-side logic to format messages with 'user' and 'assistant' roles"],"input_types":["text (user messages)","system prompts (instructions for model behavior)","conversation history (array of previous turns)"],"output_types":["text (assistant response)","streaming text chunks","structured JSON with metadata"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving.with.multi.language.support","name":"code generation and technical problem-solving with multi-language support","description":"Solar Pro 3 generates syntactically correct code across multiple programming languages (Python, JavaScript, Java, C++, SQL, etc.) by leveraging its 102B parameter capacity trained on diverse code corpora. The MoE architecture routes code-generation tokens to specialized experts trained on language-specific patterns, enabling context-aware completions that respect language idioms and frameworks.","intents":["I need to generate boilerplate code or function implementations from natural language descriptions","I want to get code explanations and refactoring suggestions for existing code snippets","I need to debug code by asking the model to identify issues and suggest fixes"],"best_for":["developers using AI-assisted coding in IDEs or web-based editors","teams automating code generation for repetitive tasks or scaffolding","technical writers and educators creating code examples"],"limitations":["Generated code may contain logical errors or security vulnerabilities — requires human review before production use","Performance on domain-specific or proprietary frameworks may be lower than models fine-tuned on those specific domains","No built-in code execution or validation — generated code must be tested separately"],"requires":["API access to Solar Pro 3 via OpenRouter or Upstage","Ability to format code snippets in prompts with proper syntax highlighting or markdown","Optional: IDE integration or custom client for streaming code output"],"input_types":["natural language descriptions of desired code","existing code snippets for refactoring or explanation","technical specifications or pseudocode"],"output_types":["code in target programming language","code explanations and documentation","refactored or optimized code variants"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_3","uri":"capability://text.generation.language.instruction.following.and.task.decomposition.with.system.prompts","name":"instruction-following and task decomposition with system prompts","description":"Solar Pro 3 accepts system prompts that define behavioral constraints and task-specific instructions, then follows those instructions consistently across multiple turns. The model decomposes complex tasks into subtasks by analyzing the system prompt and user request, routing different reasoning steps through appropriate expert pathways in its MoE architecture.","intents":["I need to create a specialized AI assistant with consistent personality and behavior constraints","I want the model to break down complex problems into manageable steps before solving them","I need to enforce specific output formats or response styles for downstream processing"],"best_for":["developers building domain-specific chatbots with custom behavior rules","teams creating AI agents that must follow strict operational guidelines","applications requiring consistent output formatting for parsing or integration"],"limitations":["System prompt effectiveness depends on prompt engineering quality — poorly written instructions may be ignored or misinterpreted","No guaranteed enforcement of constraints — model may violate system prompts under certain conditions","Task decomposition quality varies based on task complexity and prompt clarity"],"requires":["API client supporting 'system' role in message arrays","Careful prompt engineering to define clear, unambiguous instructions","Testing and validation of system prompt behavior across diverse inputs"],"input_types":["system prompts (behavioral instructions)","user queries or tasks","optional: examples of desired behavior"],"output_types":["text responses following system prompt constraints","structured output matching specified formats","step-by-step reasoning or task decomposition"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_4","uri":"capability://text.generation.language.semantic.understanding.and.reasoning.for.knowledge.intensive.tasks","name":"semantic understanding and reasoning for knowledge-intensive tasks","description":"Solar Pro 3 performs semantic analysis and reasoning by processing input text through its 102B parameter capacity, with MoE routing directing reasoning-heavy tokens to expert subnetworks trained on logical inference and knowledge synthesis. The model can answer questions requiring multi-step reasoning, identify semantic relationships, and synthesize information across multiple concepts.","intents":["I need to answer complex questions that require understanding relationships between multiple concepts","I want to extract key insights or summaries from long-form text documents","I need to perform semantic similarity matching or categorization tasks"],"best_for":["knowledge workers building question-answering systems over document collections","teams implementing semantic search or recommendation systems","researchers analyzing text for patterns and relationships"],"limitations":["Reasoning capability is bounded by training data — may struggle with novel or highly specialized domains","No access to real-time information — knowledge cutoff limits ability to answer current events questions","Semantic understanding is probabilistic — may produce plausible-sounding but incorrect inferences"],"requires":["API access to Solar Pro 3","Well-formed natural language queries or documents","Optional: external knowledge bases or documents for context (via prompt inclusion)"],"input_types":["natural language questions","long-form text documents","structured queries with context"],"output_types":["natural language answers","summaries and key insights","semantic classifications or categories"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_5","uri":"capability://text.generation.language.streaming.response.generation.with.real.time.token.output","name":"streaming response generation with real-time token output","description":"Solar Pro 3 supports streaming inference through OpenRouter's API, returning tokens incrementally as they are generated rather than waiting for the complete response. This enables real-time display of model output in user interfaces, reducing perceived latency and allowing users to see reasoning progress as it unfolds.","intents":["I need to display model responses in real-time as they're generated for better UX","I want to implement a chat interface that shows streaming responses like ChatGPT","I need to reduce perceived latency for long-form text generation tasks"],"best_for":["web and mobile application developers building conversational UIs","teams implementing real-time AI assistants with streaming output","applications where user experience depends on immediate visual feedback"],"limitations":["Streaming adds complexity to client-side implementation — requires handling partial responses and error recovery","Token streaming may increase total API calls or connection overhead compared to batch requests","Some integrations or proxies may not support streaming, requiring fallback to batch mode"],"requires":["HTTP client with streaming support (fetch API, axios, requests library with stream=True, etc.)","Server-Sent Events (SSE) or chunked transfer encoding support","Client-side logic to accumulate and display partial responses"],"input_types":["text prompts","conversation history","system instructions"],"output_types":["streaming text chunks (newline-delimited)","individual tokens","metadata about generation (stop reason, token count)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_6","uri":"capability://tool.use.integration.api.based.inference.with.configurable.sampling.parameters","name":"api-based inference with configurable sampling parameters","description":"Solar Pro 3 is accessed exclusively through OpenRouter's REST API, accepting configuration parameters like temperature, top-p, top-k, and max-tokens to control output randomness and length. The API abstracts away model deployment complexity, handling load balancing and infrastructure while exposing a simple HTTP interface for inference requests.","intents":["I need to integrate a powerful language model into my application without managing infrastructure","I want to control output randomness and creativity for different use cases (deterministic vs. creative)","I need to set response length limits to control costs and latency"],"best_for":["startups and small teams without ML infrastructure expertise","developers building rapid prototypes requiring minimal setup","applications requiring flexible model access without long-term commitments"],"limitations":["Dependency on OpenRouter's availability and uptime — no local fallback option","API latency includes network round-trip time, making it unsuitable for ultra-low-latency applications","Pricing is per-token, which can become expensive for high-volume applications","No fine-tuning or custom training available — model behavior is fixed"],"requires":["OpenRouter API key (obtained from https://openrouter.ai)","HTTP client library (curl, requests, fetch, etc.)","Network connectivity to OpenRouter's API endpoints","Understanding of JSON request/response formatting"],"input_types":["JSON request bodies with messages array","system prompts","sampling parameters (temperature, top_p, top_k, max_tokens)"],"output_types":["JSON response with generated text","token usage metadata","finish reason (stop, length, etc.)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-upstage-solar-pro-3__cap_7","uri":"capability://text.generation.language.content.generation.and.creative.writing.with.style.control","name":"content generation and creative writing with style control","description":"Solar Pro 3 generates original content across multiple genres and styles (marketing copy, creative fiction, technical documentation, etc.) by conditioning on style descriptors and examples in prompts. The model's 102B parameters provide sufficient capacity for diverse writing styles, with MoE routing allowing different experts to specialize in different genres.","intents":["I need to generate marketing copy or product descriptions at scale","I want to create creative fiction or storytelling content with consistent voice","I need to produce technical documentation or instructional content"],"best_for":["content creators and marketing teams automating content production","writers using AI as a creative tool for brainstorming and drafting","technical teams generating documentation or API descriptions"],"limitations":["Generated content may lack originality or contain clichés common in training data","Style consistency degrades over very long documents (1000+ words) without explicit style reinforcement","No built-in fact-checking — generated content may contain false or misleading information","Copyright concerns — generated content may inadvertently reproduce training data"],"requires":["API access to Solar Pro 3","Clear style descriptions or examples in prompts","Human review and editing for quality assurance"],"input_types":["style descriptors (e.g., 'professional', 'casual', 'technical')","content briefs or outlines","example content for style matching","topic or subject matter"],"output_types":["generated text in specified style","multiple variations or alternatives","structured content (headlines, body, CTA)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API key for OpenRouter or direct Upstage API access","HTTP/REST client capability or SDK integration","Support for streaming or batch inference depending on use case","API client capable of managing conversation history arrays","HTTP POST requests with JSON payloads containing message arrays","Client-side logic to format messages with 'user' and 'assistant' roles","API access to Solar Pro 3 via OpenRouter or Upstage","Ability to format code snippets in prompts with proper syntax highlighting or markdown","Optional: IDE integration or custom client for streaming code output","API client supporting 'system' role in message arrays"],"failure_modes":["MoE architecture may exhibit load balancing issues where certain experts become over-utilized, reducing effective model diversity","Sparse activation patterns can introduce non-deterministic performance variance across different input distributions","Fine-tuning MoE models requires careful handling of expert routing to avoid expert collapse","Each API call must include full conversation history, increasing payload size and latency for long conversations","No built-in session persistence — conversation state must be managed by the client application","Context window size limits the maximum conversation length that can be maintained in a single session","Generated code may contain logical errors or security vulnerabilities — requires human review before production use","Performance on domain-specific or proprietary frameworks may be lower than models fine-tuned on those specific domains","No built-in code execution or validation — generated code must be tested separately","System prompt effectiveness depends on prompt engineering quality — poorly written instructions may be ignored or misinterpreted","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"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":"2026-05-24T12:16:25.059Z","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=upstage-solar-pro-3","compare_url":"https://unfragile.ai/compare?artifact=upstage-solar-pro-3"}},"signature":"/fsf1FKDtx9dmeVEa25Nkh7kO1ecDq3gQBXIPF3EK/KlTmdXzuFoH77jezhnEtFCrlbSqy1qJZuIVu4go0K2BQ==","signedAt":"2026-06-20T09:35:36.592Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/upstage-solar-pro-3","artifact":"https://unfragile.ai/upstage-solar-pro-3","verify":"https://unfragile.ai/api/v1/verify?slug=upstage-solar-pro-3","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"}}