{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-perplexity-sonar-deep-research","slug":"perplexity-sonar-deep-research","name":"Perplexity: Sonar Deep Research","type":"model","url":"https://openrouter.ai/models/perplexity~sonar-deep-research","page_url":"https://unfragile.ai/perplexity-sonar-deep-research","categories":["rag-knowledge"],"tags":["perplexity","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.00e-6 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-perplexity-sonar-deep-research__cap_0","uri":"capability://search.retrieval.autonomous.multi.step.web.search.with.refinement","name":"autonomous-multi-step-web-search-with-refinement","description":"Executes iterative web searches across multiple steps, autonomously deciding which sources to retrieve, read, and evaluate based on intermediate findings. The model refines its search strategy dynamically—reformulating queries, prioritizing high-relevance sources, and abandoning unproductive paths—without requiring explicit user guidance between steps. This is implemented via an internal planning loop that treats web search as a first-class reasoning primitive rather than a post-hoc lookup mechanism.","intents":["I need comprehensive research on a complex topic without manually chaining multiple searches","I want the model to automatically find and evaluate conflicting viewpoints across sources","I need the model to identify gaps in its knowledge and search for missing information autonomously"],"best_for":["researchers and analysts conducting deep dives into multi-faceted topics","teams building AI-powered research assistants that need autonomous information gathering","builders prototyping fact-checking or due-diligence workflows"],"limitations":["Search refinement strategy is opaque—no visibility into why specific queries were chosen or rejected","Latency scales with search depth; complex topics may require 30-60 seconds of multi-step retrieval","No explicit control over search scope, date ranges, or source prioritization—all handled internally","May over-search on ambiguous queries, consuming API quota without proportional value"],"requires":["API key for Perplexity (via OpenRouter or direct)","Network connectivity for real-time web search","Sufficient API quota/credits for multi-step search operations"],"input_types":["text (natural language research queries)"],"output_types":["text (synthesized research summary with citations)","structured citations with source URLs and relevance metadata"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_1","uri":"capability://text.generation.language.source.synthesis.with.conflict.resolution","name":"source-synthesis-with-conflict-resolution","description":"Aggregates information from multiple retrieved sources, identifies contradictions or conflicting claims, and synthesizes a coherent narrative that acknowledges uncertainty and divergent viewpoints. The model evaluates source credibility implicitly (based on domain authority signals, citation patterns, and consistency with other sources) and weights claims accordingly. This synthesis happens during generation, not as a post-processing step, allowing the model to reason about source reliability while composing its response.","intents":["I need a balanced summary of a controversial or disputed topic with multiple perspectives represented","I want to understand where expert consensus exists and where legitimate disagreement persists","I need the model to flag unreliable or contradictory sources rather than averaging conflicting claims"],"best_for":["journalists and researchers covering contentious or evolving topics","policy analysts and decision-makers needing nuanced understanding of trade-offs","teams building fact-checking or verification tools"],"limitations":["Source credibility evaluation is implicit and not explainable—no access to the model's reasoning about why one source was weighted higher than another","May struggle with highly specialized domains where authority signals are weak or non-standard","Synthesis quality degrades when sources are sparse or when consensus is genuinely absent","No explicit mechanism to flag when synthesis is speculative vs. well-supported by sources"],"requires":["Multiple relevant sources returned from web search (typically 5+ for robust synthesis)","API key for Perplexity","Topics with sufficient public discourse and documented sources"],"input_types":["text (research query with implicit expectation of multiple viewpoints)"],"output_types":["text (synthesized narrative with implicit source weighting)","structured citations with source URLs"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_2","uri":"capability://search.retrieval.real.time.web.search.grounded.generation","name":"real-time-web-search-grounded-generation","description":"Generates responses grounded in real-time web search results rather than relying solely on training data. The model retrieves current information from the web, integrates it into its reasoning context, and generates answers that reflect up-to-date facts, recent events, and current data. This is implemented via a search-augmented generation pipeline where web results are fetched, ranked, and injected into the model's context window before generation, ensuring factuality for time-sensitive queries.","intents":["I need current information about recent events, market data, or breaking news","I want answers about topics that have changed significantly since the model's training cutoff","I need to verify claims against real-time data sources"],"best_for":["teams building real-time information systems or news aggregators","financial analysts and traders needing current market data","customer support systems requiring up-to-date product or policy information"],"limitations":["Search latency (typically 5-15 seconds per query) makes this unsuitable for sub-second response requirements","Web search results can be noisy or contain misinformation; model may amplify unreliable sources","No explicit control over search freshness—results may be hours old depending on indexing lag","Costs scale with search volume; each query triggers web retrieval, increasing API expenses"],"requires":["API key for Perplexity with web search enabled","Network connectivity for real-time search","Tolerance for 5-30 second latency per query"],"input_types":["text (natural language query)"],"output_types":["text (response grounded in current web data)","citations with URLs and timestamps"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_3","uri":"capability://planning.reasoning.iterative.query.refinement.with.feedback.loops","name":"iterative-query-refinement-with-feedback-loops","description":"Refines search and reasoning strategies based on intermediate results, automatically reformulating queries when initial searches yield insufficient or irrelevant results. The model evaluates whether retrieved information answers the original question, identifies gaps, and adjusts its approach—changing keywords, broadening/narrowing scope, or pivoting to related topics. This feedback loop is internal to the model's reasoning process, not exposed to the user, enabling adaptive exploration without explicit user intervention.","intents":["I want the model to keep searching until it finds truly relevant information, not just the first results","I need the model to recognize when initial search results are off-topic and try different angles","I want comprehensive coverage of a topic without manually re-querying multiple times"],"best_for":["researchers exploring unfamiliar domains where initial queries may be poorly formulated","teams building exploratory search interfaces that benefit from adaptive query strategies","use cases where search precision matters more than latency"],"limitations":["Refinement strategy is non-deterministic and opaque—same query may follow different search paths on different runs","No user control over refinement depth; model may stop refining prematurely or over-refine on edge cases","Latency is unpredictable; complex topics requiring many refinement cycles may take 60+ seconds","May waste API quota on unproductive refinement attempts if initial query is fundamentally ambiguous"],"requires":["API key for Perplexity","Sufficient API quota to support multiple search attempts per query","Tolerance for variable latency"],"input_types":["text (research query, may be vague or poorly formulated)"],"output_types":["text (synthesized response after refinement iterations)","citations from final search iteration"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_4","uri":"capability://text.generation.language.citation.grounded.response.generation","name":"citation-grounded-response-generation","description":"Generates responses with explicit citations to source URLs, enabling users to verify claims and trace reasoning back to original sources. Citations are embedded in the response text or provided as structured metadata, linking specific claims to the web sources that support them. This is implemented by maintaining a mapping between generated text and retrieved sources during generation, ensuring citations are accurate and traceable.","intents":["I need to verify the factual basis of the model's claims by checking original sources","I want to cite sources in my own work based on the model's research","I need transparency about where information came from"],"best_for":["academic researchers and students requiring proper attribution","journalists and content creators building on the model's research","compliance-heavy domains (legal, medical, financial) where source traceability is mandatory"],"limitations":["Citations may be inaccurate if the model misattributes claims to sources or conflates information from multiple sources","No guarantee that cited sources actually support the claim—model may cite a source that merely mentions related concepts","Citation format is not standardized; may not be compatible with citation management tools (Zotero, Mendeley, etc.)","Broken links or paywalled sources may make citations unusable despite being technically correct"],"requires":["API key for Perplexity","Ability to parse and follow URLs in responses"],"input_types":["text (research query)"],"output_types":["text with embedded citations","structured citation metadata (URL, source title, access date)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_5","uri":"capability://text.generation.language.long.form.research.synthesis.with.structured.output","name":"long-form-research-synthesis-with-structured-output","description":"Generates comprehensive, multi-paragraph research summaries that synthesize information across dozens of sources into coherent narratives with clear structure (introduction, key findings, trade-offs, limitations). The model organizes information hierarchically, prioritizes important findings, and provides context for how different pieces of information relate. Output can be formatted as structured sections (e.g., JSON with 'summary', 'key_findings', 'limitations', 'sources') or as flowing prose with implicit organization.","intents":["I need a comprehensive research report on a complex topic, not just a quick answer","I want information organized by theme or importance, not just chronologically or by source","I need structured output that I can programmatically parse and integrate into my own systems"],"best_for":["researchers and analysts producing detailed reports or white papers","teams building AI-powered research platforms that need structured output","builders creating knowledge bases or documentation systems"],"limitations":["Synthesis quality degrades for niche or emerging topics with sparse source material","No explicit control over output structure—organization is determined by the model's internal reasoning","Long-form generation increases latency (30-60+ seconds) and API costs","Structured output format is not guaranteed to be consistent across queries; schema may vary"],"requires":["API key for Perplexity","Sufficient API quota for long-form generation","Tolerance for 30-60+ second latency"],"input_types":["text (research topic or question)"],"output_types":["text (long-form prose synthesis)","structured JSON (sections with key findings, limitations, sources)","markdown with hierarchical organization"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_6","uri":"capability://planning.reasoning.domain.specific.reasoning.with.expert.context","name":"domain-specific-reasoning-with-expert-context","description":"Applies domain-specific reasoning patterns and expert knowledge to research queries, adapting its approach based on the topic domain (e.g., scientific research, legal analysis, financial modeling). The model implicitly recognizes domain context from the query and adjusts its search strategy, source evaluation, and synthesis approach accordingly. For example, scientific queries may prioritize peer-reviewed sources and methodology evaluation, while financial queries may emphasize recent data and regulatory context.","intents":["I need research on a specialized topic where domain expertise matters for evaluating sources","I want the model to apply expert reasoning patterns (e.g., scientific methodology critique) to my research","I need domain-appropriate synthesis that reflects how experts in the field would approach the topic"],"best_for":["domain experts (scientists, lawyers, financial analysts) using AI to accelerate research","teams building vertical-specific research tools (e.g., legal research, scientific literature review)","builders creating domain-aware knowledge systems"],"limitations":["Domain recognition is implicit and not configurable—no way to explicitly specify which domain reasoning to apply","Quality varies significantly across domains; some domains (e.g., medicine, law) may be better supported than others","No transparency into which domain-specific patterns were applied or why","May apply incorrect domain context if query is ambiguous or spans multiple domains"],"requires":["API key for Perplexity","Queries with sufficient domain context for implicit recognition"],"input_types":["text (domain-specific research query)"],"output_types":["text (domain-appropriate synthesis with expert reasoning patterns)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_7","uri":"capability://text.generation.language.uncertainty.quantification.and.confidence.signaling","name":"uncertainty-quantification-and-confidence-signaling","description":"Explicitly signals confidence levels and uncertainty in its responses, distinguishing between well-supported claims (backed by multiple sources), speculative claims (based on limited evidence), and areas where expert disagreement exists. The model may use explicit language ('likely', 'uncertain', 'experts disagree') or structured confidence metadata to communicate epistemic status. This is implemented by evaluating source agreement, source credibility, and evidence strength during synthesis.","intents":["I need to know which claims are well-supported vs. speculative or disputed","I want the model to flag areas of genuine uncertainty rather than presenting everything with false confidence","I need to make decisions based on understanding the evidence quality, not just the conclusion"],"best_for":["decision-makers in high-stakes domains (medical, legal, financial) who need to understand evidence quality","researchers and analysts who need to distinguish between consensus and frontier knowledge","teams building AI systems where transparency about uncertainty is critical"],"limitations":["Confidence signaling is implicit and not quantified—no numerical confidence scores, only linguistic hedging","No explicit mechanism to distinguish between 'uncertain because evidence is sparse' vs. 'uncertain because experts disagree'","May over-signal confidence on well-researched topics or under-signal on emerging topics with limited but high-quality sources","Confidence assessment is not calibrated—no validation that the model's confidence levels match actual accuracy"],"requires":["API key for Perplexity","User ability to interpret linguistic confidence signals"],"input_types":["text (research query)"],"output_types":["text with implicit confidence signals (hedging language, explicit uncertainty statements)","optional structured confidence metadata"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_8","uri":"capability://text.generation.language.comparative.analysis.across.multiple.perspectives","name":"comparative-analysis-across-multiple-perspectives","description":"Systematically compares and contrasts different viewpoints, approaches, or solutions to a problem, organizing information to highlight similarities, differences, and trade-offs. The model searches for and synthesizes multiple perspectives (e.g., different schools of thought, competing products, alternative methodologies), explicitly comparing them on relevant dimensions. This is implemented by treating comparative analysis as a structured reasoning task where the model identifies comparison dimensions, retrieves information for each perspective, and synthesizes findings into a comparison matrix or narrative.","intents":["I need to compare multiple approaches or solutions and understand their trade-offs","I want to see how different experts or schools of thought approach a problem differently","I need a structured comparison of alternatives to make an informed decision"],"best_for":["decision-makers evaluating multiple options (products, methodologies, vendors)","researchers comparing different theoretical approaches or empirical findings","teams building comparison tools or decision-support systems"],"limitations":["Comparison dimensions are implicitly chosen by the model—no explicit control over which dimensions are compared","May miss important dimensions if they're not well-represented in web sources","Comparison quality depends on source availability for each perspective; sparse coverage of minority viewpoints may skew results","No explicit weighting of comparison dimensions—all dimensions treated as equally important unless sources suggest otherwise"],"requires":["API key for Perplexity","Multiple perspectives or alternatives with sufficient public discourse"],"input_types":["text (comparative query, e.g., 'compare X and Y')"],"output_types":["text (narrative comparison with explicit trade-offs)","structured comparison matrix or table","citations for each perspective"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-perplexity-sonar-deep-research__cap_9","uri":"capability://text.generation.language.conversational.research.with.follow.up.refinement","name":"conversational-research-with-follow-up-refinement","description":"Supports multi-turn conversations where users can ask follow-up questions, request clarification, or ask the model to dive deeper into specific aspects of previous research. The model maintains context across turns, refining its search and synthesis based on conversational feedback. For example, a user might ask 'What are the latest developments in quantum computing?', then follow up with 'Tell me more about error correction approaches', and the model will search for and synthesize information specific to that subtopic while maintaining context from the initial query.","intents":["I want to iteratively explore a topic through conversation, not just get a single comprehensive answer","I need to ask follow-up questions and have the model understand context from previous turns","I want to drill down into specific aspects of a topic based on initial research"],"best_for":["exploratory research workflows where the user's information needs evolve during the conversation","teams building conversational research assistants or chatbots","interactive learning scenarios where users want to explore topics at their own pace"],"limitations":["Context window limitations may prevent the model from maintaining full conversation history on very long conversations (100+ turns)","Follow-up searches may be redundant with previous searches, wasting API quota","No explicit conversation state management—the model's understanding of context is implicit","Conversation history is not persisted across sessions unless explicitly saved by the user"],"requires":["API key for Perplexity","Support for multi-turn conversations (available via OpenRouter or Perplexity API)"],"input_types":["text (initial query and follow-up questions in conversational format)"],"output_types":["text (responses refined based on conversational context)","citations for each turn"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API key for Perplexity (via OpenRouter or direct)","Network connectivity for real-time web search","Sufficient API quota/credits for multi-step search operations","Multiple relevant sources returned from web search (typically 5+ for robust synthesis)","API key for Perplexity","Topics with sufficient public discourse and documented sources","API key for Perplexity with web search enabled","Network connectivity for real-time search","Tolerance for 5-30 second latency per query","Sufficient API quota to support multiple search attempts per query"],"failure_modes":["Search refinement strategy is opaque—no visibility into why specific queries were chosen or rejected","Latency scales with search depth; complex topics may require 30-60 seconds of multi-step retrieval","No explicit control over search scope, date ranges, or source prioritization—all handled internally","May over-search on ambiguous queries, consuming API quota without proportional value","Source credibility evaluation is implicit and not explainable—no access to the model's reasoning about why one source was weighted higher than another","May struggle with highly specialized domains where authority signals are weak or non-standard","Synthesis quality degrades when sources are sparse or when consensus is genuinely absent","No explicit mechanism to flag when synthesis is speculative vs. well-supported by sources","Search latency (typically 5-15 seconds per query) makes this unsuitable for sub-second response requirements","Web search results can be noisy or contain misinformation; model may amplify unreliable sources","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=perplexity-sonar-deep-research","compare_url":"https://unfragile.ai/compare?artifact=perplexity-sonar-deep-research"}},"signature":"ooDJEUGFvHedJoispSvj6peH7l+g2k44IY9oTNPoZI+kwxksyum4eAIZdMZ9jhJzmgE4EdSvnayAOZygB6OJAQ==","signedAt":"2026-06-19T19:10:40.303Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/perplexity-sonar-deep-research","artifact":"https://unfragile.ai/perplexity-sonar-deep-research","verify":"https://unfragile.ai/api/v1/verify?slug=perplexity-sonar-deep-research","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"}}