{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1","slug":"nex-agi-deepseek-v3.1-nex-n1","name":"Nex AGI: DeepSeek V3.1 Nex N1","type":"model","url":"https://openrouter.ai/models/nex-agi~deepseek-v3.1-nex-n1","page_url":"https://unfragile.ai/nex-agi-deepseek-v3.1-nex-n1","categories":["ai-agents"],"tags":["nex-agi","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$1.35e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_0","uri":"capability://tool.use.integration.multi.turn.agentic.reasoning.with.tool.orchestration","name":"multi-turn agentic reasoning with tool orchestration","description":"Executes extended reasoning chains across multiple turns with native support for function calling and tool invocation. The model maintains conversation context across turns while dynamically selecting and invoking external tools based on task requirements, using a schema-based function registry pattern that supports structured tool definitions and return value integration back into the reasoning loop.","intents":["Build autonomous agents that can break down complex tasks into subtasks and invoke APIs without human intervention between steps","Create chatbots that can call external services (databases, APIs, calculators) and incorporate results into responses","Develop task automation workflows where the model decides which tools to use based on real-time context"],"best_for":["AI engineers building autonomous agent systems","Teams developing LLM-powered automation platforms","Developers creating multi-step workflow orchestrators"],"limitations":["Tool invocation latency depends on external service response times — model cannot parallelize tool calls natively","Requires explicit tool schema definitions; poorly-defined schemas lead to tool selection errors","Context window constraints limit the number of previous tool invocations that can be referenced in reasoning"],"requires":["API access to Nex-N1 via OpenRouter or compatible endpoint","Tool/function definitions in JSON schema format (OpenAI function calling format or equivalent)","Client-side orchestration layer to handle tool execution and result injection"],"input_types":["text (natural language instructions)","structured tool schemas (JSON)","conversation history with tool calls and results"],"output_types":["text (reasoning and responses)","structured tool calls (function name + parameters)","final actionable outputs after tool execution"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_1","uri":"capability://text.generation.language.long.context.reasoning.with.extended.token.windows","name":"long-context reasoning with extended token windows","description":"Processes extended input sequences with a large context window, enabling the model to maintain coherence and reference information across lengthy documents, code repositories, or conversation histories. The architecture uses efficient attention mechanisms and position interpolation to handle context lengths that exceed typical LLM baselines while maintaining reasoning quality across the full span.","intents":["Analyze entire codebases or documentation sets in a single request without chunking","Maintain coherent multi-turn conversations with deep historical context","Process long-form documents and generate summaries or analyses referencing specific sections"],"best_for":["Developers working with large monorepos or complex codebases","Researchers analyzing lengthy documents or datasets","Teams building conversational systems requiring deep context retention"],"limitations":["Inference latency increases with context length — longer contexts require proportionally more compute","Token pricing scales linearly with input length, making very large contexts expensive at scale","Attention quality may degrade at extreme context lengths (>100k tokens) depending on implementation"],"requires":["API access to Nex-N1 model via OpenRouter","Client capable of formatting and transmitting large payloads (>1MB for typical long-context requests)","Sufficient API rate limits and quota for extended processing"],"input_types":["text (documents, code, conversation history)","structured data (JSON, YAML, markdown)"],"output_types":["text (analysis, summaries, responses)","code (refactored or generated based on full codebase context)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Generates syntactically correct and semantically meaningful code across 40+ programming languages using learned patterns from diverse codebases. The model understands language-specific idioms, frameworks, and best practices, generating completions that respect context from surrounding code and can produce entire functions, classes, or modules based on natural language specifications or partial implementations.","intents":["Complete code snippets or functions based on context and docstrings","Generate boilerplate code for common patterns (API endpoints, database queries, UI components)","Translate or port code between different programming languages"],"best_for":["Full-stack developers seeking faster code authoring","Teams standardizing on multiple languages who need consistent code generation","Developers learning new languages or frameworks"],"limitations":["Generated code may contain logical errors or security vulnerabilities — requires human review before production use","Performance characteristics of generated code are not guaranteed; may produce inefficient algorithms","Limited understanding of project-specific conventions unless explicitly provided in context"],"requires":["API access to Nex-N1 via OpenRouter","Code context (surrounding code, function signatures, or comments) for best results","IDE or editor integration for practical workflow (optional but recommended)"],"input_types":["text (natural language descriptions, docstrings)","code (partial implementations, function signatures, context)"],"output_types":["code (complete functions, classes, modules, or snippets)","text (explanations of generated code)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_3","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.reasoning","name":"structured data extraction and schema-based reasoning","description":"Extracts and structures information from unstructured text into defined schemas (JSON, XML, or custom formats) using constrained decoding or schema-aware generation patterns. The model understands schema requirements and generates outputs that conform to specified structures, enabling reliable downstream processing and integration with structured data pipelines.","intents":["Extract entities and relationships from documents into structured JSON formats","Parse natural language inputs into API request payloads with guaranteed schema compliance","Convert unstructured data (PDFs, emails, chat logs) into database-ready records"],"best_for":["Data engineering teams building ETL pipelines","Developers integrating LLMs into structured data workflows","Teams automating document processing and data entry"],"limitations":["Extraction accuracy depends on schema clarity and input quality — ambiguous schemas lead to inconsistent outputs","Complex nested schemas may exceed token budgets or reasoning capacity","No guarantee of 100% schema compliance without constrained decoding (which adds latency)"],"requires":["API access to Nex-N1 via OpenRouter","Well-defined schema (JSON Schema, Pydantic models, or equivalent)","Unstructured text input with sufficient information to populate schema fields"],"input_types":["text (documents, emails, chat logs, web content)","schema definitions (JSON Schema, Pydantic, OpenAI function schemas)"],"output_types":["structured data (JSON, XML, or custom formats)","validation results (schema compliance reports)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_4","uri":"capability://planning.reasoning.real.world.task.decomposition.and.planning","name":"real-world task decomposition and planning","description":"Breaks down complex, open-ended user requests into executable subtasks with clear dependencies and success criteria. The model generates task plans that account for real-world constraints (API rate limits, tool availability, data dependencies) and produces actionable steps that can be executed sequentially or in parallel by downstream agents or automation systems.","intents":["Convert high-level business requirements into technical task plans","Generate step-by-step workflows for complex multi-tool operations","Create project plans with dependencies and resource requirements"],"best_for":["AI engineers building multi-agent systems","Product managers translating requirements into technical specifications","Teams automating complex business processes"],"limitations":["Plans may not account for all edge cases or failure modes without explicit constraint specification","Task decomposition quality depends on clarity of initial request","No built-in validation that generated plans are actually executable without external verification"],"requires":["API access to Nex-N1 via OpenRouter","Clear problem statement or user request","Optional: list of available tools/resources and constraints"],"input_types":["text (natural language problem statements, requirements)","structured context (available tools, constraints, success criteria)"],"output_types":["structured task plans (JSON with steps, dependencies, estimated effort)","text (explanations and rationale for plan structure)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_5","uri":"capability://text.generation.language.conversational.context.management.with.turn.level.reasoning","name":"conversational context management with turn-level reasoning","description":"Maintains and reasons over multi-turn conversation histories with explicit awareness of context evolution, speaker roles, and information dependencies across turns. The model tracks what has been established, what remains ambiguous, and what new information each turn introduces, enabling coherent responses that reference prior context without redundancy and adapt reasoning based on conversation flow.","intents":["Build chatbots that maintain consistent personality and knowledge across long conversations","Create dialogue systems that reference earlier statements and build on prior context","Develop conversational agents that clarify ambiguities based on conversation history"],"best_for":["Teams building customer support chatbots","Developers creating conversational AI assistants","Researchers studying dialogue systems and context management"],"limitations":["Context window limits the number of prior turns that can be referenced; older turns may be summarized or dropped","Model may hallucinate or misremember details from earlier turns if context is very long","No built-in mechanism to explicitly flag contradictions between current and prior statements"],"requires":["API access to Nex-N1 via OpenRouter","Conversation history formatted as turn-by-turn exchanges","Clear speaker/role identification for each turn (optional but recommended)"],"input_types":["text (conversation history, current user message)","metadata (speaker roles, timestamps, turn IDs)"],"output_types":["text (contextually appropriate responses)","structured context summaries (key facts, open questions)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_6","uri":"capability://text.generation.language.instruction.following.with.nuanced.constraint.handling","name":"instruction-following with nuanced constraint handling","description":"Interprets complex, multi-part instructions with explicit constraints, edge cases, and conditional logic, generating outputs that respect all specified requirements. The model parses instruction hierarchies, identifies conflicting constraints, and produces outputs that balance competing requirements while explaining trade-offs when perfect compliance is impossible.","intents":["Execute detailed specifications with multiple constraints and edge cases","Generate outputs that respect style guides, format requirements, and content policies","Handle conditional logic in instructions (if X then do Y, else do Z)"],"best_for":["Teams with strict output requirements (compliance, legal, technical standards)","Developers building systems that must follow complex specifications","Organizations automating content generation with detailed guidelines"],"limitations":["Very complex constraint sets (>10 interdependent constraints) may exceed reasoning capacity","Model may misinterpret edge cases or prioritize constraints incorrectly without explicit weighting","No built-in validation that outputs actually satisfy all constraints"],"requires":["API access to Nex-N1 via OpenRouter","Clear, well-structured instructions with explicit constraints","Examples or reference outputs (optional but improves compliance)"],"input_types":["text (detailed instructions, constraints, requirements)","examples (reference outputs demonstrating expected behavior)"],"output_types":["text (outputs respecting specified constraints)","metadata (constraint compliance report, trade-off explanations)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_7","uri":"capability://text.generation.language.knowledge.synthesis.and.comparative.reasoning","name":"knowledge synthesis and comparative reasoning","description":"Synthesizes information from multiple sources or perspectives to generate balanced, nuanced analyses that acknowledge trade-offs, competing viewpoints, and uncertainty. The model compares alternatives, identifies strengths and weaknesses of different approaches, and produces outputs that integrate multiple viewpoints rather than selecting a single perspective.","intents":["Generate comparative analyses of competing technologies, approaches, or solutions","Synthesize research findings from multiple sources into coherent summaries","Create balanced arguments that acknowledge multiple perspectives on complex topics"],"best_for":["Researchers and analysts synthesizing literature","Teams evaluating multiple technical solutions","Content creators producing balanced, nuanced writing"],"limitations":["Synthesis quality depends on quality and diversity of input sources","Model may exhibit bias toward certain perspectives if training data is skewed","No built-in fact-checking; synthesized information may contain inaccuracies"],"requires":["API access to Nex-N1 via OpenRouter","Multiple source texts or perspectives to synthesize","Clear synthesis criteria or comparison dimensions"],"input_types":["text (source documents, research papers, competing viewpoints)","structured comparison criteria (dimensions to compare, evaluation metrics)"],"output_types":["text (synthesized analyses, comparative summaries)","structured comparisons (tables, matrices comparing alternatives)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_8","uri":"capability://text.generation.language.error.recovery.and.clarification.seeking.in.ambiguous.contexts","name":"error recovery and clarification-seeking in ambiguous contexts","description":"Detects ambiguities, contradictions, or insufficient information in user requests and generates clarifying questions or proposes alternative interpretations rather than making unsupported assumptions. The model explicitly flags what is unclear, suggests possible interpretations, and requests additional information needed to proceed confidently.","intents":["Build systems that ask clarifying questions when user intent is ambiguous","Generate error messages that help users correct problematic inputs","Create assistants that acknowledge uncertainty and request additional information"],"best_for":["Teams building user-facing AI systems requiring high reliability","Developers creating systems where incorrect assumptions are costly","Organizations prioritizing user experience and error handling"],"limitations":["Over-asking for clarification can frustrate users and reduce efficiency","Model may miss genuine ambiguities or flag false positives","Clarifying questions may not always lead to resolution if user cannot provide needed information"],"requires":["API access to Nex-N1 via OpenRouter","Ambiguous or incomplete user requests","Optional: context about what information is available vs. missing"],"input_types":["text (user requests, problem statements)","context (available information, constraints)"],"output_types":["text (clarifying questions, alternative interpretations)","structured ambiguity reports (flagged issues, suggested resolutions)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-nex-agi-deepseek-v3.1-nex-n1__cap_9","uri":"capability://text.generation.language.domain.specific.reasoning.with.technical.depth","name":"domain-specific reasoning with technical depth","description":"Applies specialized knowledge and reasoning patterns to technical domains (software engineering, mathematics, science, finance) with understanding of domain-specific conventions, terminology, and best practices. The model generates outputs that reflect domain expertise and can reason about complex technical problems using domain-appropriate approaches.","intents":["Solve technical problems in specialized domains (algorithms, mathematics, system design)","Generate domain-appropriate code or technical documentation","Provide expert-level analysis of domain-specific problems"],"best_for":["Technical teams in specialized domains (fintech, scientific computing, systems engineering)","Developers seeking expert-level technical reasoning","Researchers and engineers working on complex technical problems"],"limitations":["Domain expertise is limited to domains covered in training data; emerging or niche domains may lack depth","Technical reasoning may contain errors in complex domains; outputs require expert review","Model may conflate similar concepts across domains or apply inappropriate patterns"],"requires":["API access to Nex-N1 via OpenRouter","Technical problem statement with sufficient context","Optional: domain-specific constraints, standards, or best practices"],"input_types":["text (technical problems, specifications, questions)","code (existing implementations, algorithms)","structured data (mathematical notation, technical diagrams)"],"output_types":["text (technical explanations, solutions)","code (domain-specific implementations)","structured analysis (proofs, derivations, technical documentation)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access to Nex-N1 via OpenRouter or compatible endpoint","Tool/function definitions in JSON schema format (OpenAI function calling format or equivalent)","Client-side orchestration layer to handle tool execution and result injection","API access to Nex-N1 model via OpenRouter","Client capable of formatting and transmitting large payloads (>1MB for typical long-context requests)","Sufficient API rate limits and quota for extended processing","API access to Nex-N1 via OpenRouter","Code context (surrounding code, function signatures, or comments) for best results","IDE or editor integration for practical workflow (optional but recommended)","Well-defined schema (JSON Schema, Pydantic models, or equivalent)"],"failure_modes":["Tool invocation latency depends on external service response times — model cannot parallelize tool calls natively","Requires explicit tool schema definitions; poorly-defined schemas lead to tool selection errors","Context window constraints limit the number of previous tool invocations that can be referenced in reasoning","Inference latency increases with context length — longer contexts require proportionally more compute","Token pricing scales linearly with input length, making very large contexts expensive at scale","Attention quality may degrade at extreme context lengths (>100k tokens) depending on implementation","Generated code may contain logical errors or security vulnerabilities — requires human review before production use","Performance characteristics of generated code are not guaranteed; may produce inefficient algorithms","Limited understanding of project-specific conventions unless explicitly provided in context","Extraction accuracy depends on schema clarity and input quality — ambiguous schemas lead to inconsistent outputs","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":"2026-05-24T12:16:24.484Z","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=nex-agi-deepseek-v3.1-nex-n1","compare_url":"https://unfragile.ai/compare?artifact=nex-agi-deepseek-v3.1-nex-n1"}},"signature":"h8yfsZcxX5iP5TZOffZLCZdvX/enNBHYKNxij/z56adKpMuP25Aoe5h8aKNv75mf/zI8luurSoX0UMoLwtSLCA==","signedAt":"2026-06-22T22:12:47.450Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nex-agi-deepseek-v3.1-nex-n1","artifact":"https://unfragile.ai/nex-agi-deepseek-v3.1-nex-n1","verify":"https://unfragile.ai/api/v1/verify?slug=nex-agi-deepseek-v3.1-nex-n1","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"}}