{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-deepseek-deepseek-v3.1-terminus","slug":"deepseek-deepseek-v3.1-terminus","name":"DeepSeek: DeepSeek V3.1 Terminus","type":"model","url":"https://openrouter.ai/models/deepseek~deepseek-v3.1-terminus","page_url":"https://unfragile.ai/deepseek-deepseek-v3.1-terminus","categories":["chatbots-assistants"],"tags":["deepseek","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$2.70e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_0","uri":"capability://text.generation.language.multi.turn.conversational.reasoning.with.language.consistency","name":"multi-turn conversational reasoning with language consistency","description":"Maintains coherent dialogue across extended conversation contexts by tracking semantic state and enforcing language consistency rules throughout multi-turn exchanges. The model uses attention mechanisms to preserve context alignment across turns while applying language-specific normalization to prevent code-switching artifacts and ensure uniform linguistic output within single conversations.","intents":["Build a chatbot that maintains consistent tone and language across 50+ turn conversations","Create an AI assistant that doesn't switch between languages mid-response","Develop a customer support agent that remembers conversation history without losing context quality"],"best_for":["Teams building multilingual chatbots requiring language purity","Developers creating long-form conversational agents for customer support","Organizations deploying AI assistants in regulated industries requiring consistent communication"],"limitations":["Context window is finite; very long conversations (>100k tokens) may experience degradation in consistency","Language consistency enforcement may reduce code-switching flexibility in genuinely multilingual scenarios","No explicit memory persistence across sessions — each conversation starts fresh without prior context"],"requires":["API access via OpenRouter or direct DeepSeek endpoint","HTTP/2 capable client library","Support for streaming or non-streaming response modes"],"input_types":["text (natural language)","code snippets (for technical discussions)","structured prompts with system instructions"],"output_types":["text (natural language response)","code blocks (syntax-highlighted)","structured reasoning traces"],"categories":["text-generation-language","conversational-ai"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_1","uri":"capability://planning.reasoning.agentic.task.decomposition.and.planning","name":"agentic task decomposition and planning","description":"Breaks down complex user requests into executable sub-tasks with explicit reasoning chains, generating structured action plans that can be consumed by external tool-calling frameworks. The model produces intermediate reasoning steps with confidence scores and dependency graphs, enabling orchestration systems to parallelize independent tasks and handle conditional branching based on sub-task outcomes.","intents":["Create an AI agent that can plan multi-step workflows without explicit prompt engineering","Build a system that decomposes research queries into parallel information-gathering tasks","Develop an autonomous agent that can handle complex business processes with conditional logic"],"best_for":["Developers building agentic systems with tool-use frameworks (LangChain, LlamaIndex, AutoGPT)","Teams implementing multi-step automation workflows requiring intelligent task decomposition","Researchers prototyping autonomous agents with complex reasoning requirements"],"limitations":["Task decomposition quality degrades on highly ambiguous or under-specified requests","No built-in execution engine — requires external orchestration layer to actually run generated plans","Reasoning traces can be verbose, adding 20-40% to token consumption vs direct instruction","Dependency graph generation is best-effort; circular dependencies or deadlocks require manual intervention"],"requires":["API access with streaming enabled for real-time reasoning visibility","External tool registry or function schema definitions","JSON parsing capability in client to extract structured action plans"],"input_types":["natural language task descriptions","structured goal specifications with constraints","tool/function schemas in JSON format"],"output_types":["structured action plans (JSON with task dependencies)","reasoning traces (intermediate steps with confidence scores)","executable function calls with parameters"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_2","uri":"capability://code.generation.editing.code.generation.and.technical.problem.solving","name":"code generation and technical problem-solving","description":"Generates syntactically correct, production-ready code across 40+ programming languages using deep language-specific knowledge of idioms, libraries, and best practices. The model applies context-aware code completion by analyzing surrounding code structure, imports, and type hints to produce coherent multi-file solutions with proper error handling and documentation.","intents":["Generate boilerplate code for new projects in unfamiliar languages","Complete partial implementations with context-aware suggestions","Solve algorithmic problems with optimized solutions and complexity analysis","Generate test cases and edge-case handling code"],"best_for":["Full-stack developers working across multiple language ecosystems","Teams using DeepSeek as a code copilot alternative to GitHub Copilot","Developers prototyping solutions quickly without deep language expertise"],"limitations":["Code generation quality varies by language; less common languages (Rust, Kotlin) have lower accuracy than Python/JavaScript","No real-time IDE integration without custom plugin development","Generated code may require security review; model can produce code with subtle vulnerabilities","Context window limits prevent generating entire large codebases; best for <5000 LOC per request"],"requires":["API access to DeepSeek via OpenRouter or direct endpoint","Code formatter/linter for post-processing (optional but recommended)","Language-specific runtime for testing generated code"],"input_types":["natural language descriptions of requirements","partial code snippets with TODOs or comments","function signatures and type definitions","error messages and stack traces for debugging"],"output_types":["complete code implementations","code snippets with syntax highlighting","explanations of algorithmic approach","test cases and usage examples"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_3","uri":"capability://planning.reasoning.mathematical.reasoning.and.symbolic.computation","name":"mathematical reasoning and symbolic computation","description":"Solves mathematical problems through step-by-step symbolic reasoning, generating intermediate derivations and proofs with explicit algebraic manipulations. The model applies formal reasoning patterns to handle calculus, linear algebra, number theory, and combinatorics, producing verifiable solution paths that can be validated against symbolic math engines.","intents":["Solve calculus and linear algebra problems with detailed derivations","Generate mathematical proofs with formal reasoning steps","Explain complex mathematical concepts with worked examples","Verify mathematical correctness of solutions through symbolic reasoning"],"best_for":["Students and educators using AI for math tutoring with explanation requirements","Researchers prototyping mathematical algorithms and verifying correctness","Teams building educational platforms requiring step-by-step math solutions"],"limitations":["Symbolic computation is approximate; complex integrals or differential equations may have errors","No integration with computer algebra systems (Mathematica, Sage) — purely text-based reasoning","Numerical precision is limited to floating-point representation; exact rational arithmetic not guaranteed","Very large matrix operations or high-dimensional problems may exceed reasoning capacity"],"requires":["API access with sufficient context window (8k+ tokens recommended)","Optional: symbolic math library for validation (SymPy, Mathematica)"],"input_types":["mathematical problem statements in natural language","equations in LaTeX or plain text notation","problem constraints and boundary conditions"],"output_types":["step-by-step derivations","final numerical or symbolic answers","proof structures with logical justification"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_4","uri":"capability://data.processing.analysis.structured.data.extraction.and.schema.based.output","name":"structured data extraction and schema-based output","description":"Extracts information from unstructured text and generates structured outputs conforming to specified JSON schemas, using constraint-aware generation to ensure valid output format. The model applies schema validation during generation, preventing malformed JSON and ensuring all required fields are populated with appropriate types and values.","intents":["Extract entities and relationships from documents into structured databases","Convert natural language requirements into structured configuration files","Parse semi-structured text (emails, logs, PDFs) into normalized data models","Generate API responses that conform to OpenAPI schemas"],"best_for":["Data engineering teams building ETL pipelines with LLM-based extraction","Developers building form-filling or data collection systems","Teams automating document processing and knowledge base construction"],"limitations":["Schema compliance is best-effort; complex nested schemas may produce invalid JSON requiring post-processing","Extraction accuracy depends heavily on schema clarity and example quality","Large schemas (>50 fields) may cause token bloat and slower generation","No built-in validation against external constraints (uniqueness, referential integrity)"],"requires":["JSON schema definition for target output format","API access with JSON mode or structured output support","JSON parser in client for validation"],"input_types":["unstructured text (documents, emails, web content)","semi-structured data (CSV, logs, HTML)","JSON schema specifications"],"output_types":["valid JSON conforming to provided schema","structured data records","nested objects with typed fields"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_5","uri":"capability://text.generation.language.knowledge.synthesis.and.comparative.analysis","name":"knowledge synthesis and comparative analysis","description":"Synthesizes information across multiple domains to answer complex questions requiring cross-domain reasoning, generating comparative analyses that highlight trade-offs and relationships between concepts. The model produces structured comparisons with explicit reasoning about similarities, differences, and contextual applicability of different approaches or solutions.","intents":["Compare technical architectures (microservices vs monolith) with trade-off analysis","Synthesize research findings across multiple papers into coherent summaries","Generate decision matrices for technology or vendor selection","Explain how concepts from one domain apply to another (e.g., biological systems to software design)"],"best_for":["Technical architects and decision-makers evaluating multiple solutions","Researchers conducting literature reviews and synthesis","Teams making technology selection decisions with complex trade-offs","Educators explaining interdisciplinary concepts"],"limitations":["Knowledge cutoff limits currency of comparisons; recent developments may be missed","Comparative analysis quality depends on training data coverage of both domains","No real-time fact-checking; claims require external validation","Bias toward well-documented solutions; niche or emerging approaches may be underrepresented"],"requires":["API access with sufficient context for multi-domain reasoning","Optional: external knowledge sources for fact-checking"],"input_types":["natural language questions about comparisons","lists of items/concepts to compare","criteria or dimensions for analysis"],"output_types":["comparative analysis with structured trade-offs","decision matrices","synthesis summaries","reasoning explanations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_6","uri":"capability://code.generation.editing.debugging.and.error.diagnosis.with.contextual.suggestions","name":"debugging and error diagnosis with contextual suggestions","description":"Analyzes error messages, stack traces, and code context to diagnose root causes and generate targeted fixes with explanations of why errors occur. The model applies pattern matching against common error categories while analyzing surrounding code to identify context-specific issues that generic error messages don't capture.","intents":["Debug runtime errors by analyzing stack traces and code context","Diagnose configuration issues in complex systems (Docker, Kubernetes, databases)","Identify performance bottlenecks from profiling output and logs","Suggest fixes for compiler/linter errors with explanations"],"best_for":["Developers debugging production issues with limited context","Teams building internal debugging tools or error analysis systems","DevOps engineers diagnosing infrastructure and deployment issues"],"limitations":["Diagnosis accuracy depends on error message quality and code context provided","May miss issues requiring runtime state inspection or distributed tracing","No access to actual running systems; suggestions are based on static analysis","Complex multi-service debugging scenarios may exceed reasoning capacity"],"requires":["API access with context window sufficient for code + error context","Error messages, stack traces, or logs in text format"],"input_types":["error messages and stack traces","code snippets showing error context","log files and diagnostic output","configuration files"],"output_types":["root cause analysis","suggested fixes with code examples","explanations of why errors occur","debugging steps and validation approaches"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_7","uri":"capability://text.generation.language.creative.writing.and.content.generation.with.style.control","name":"creative writing and content generation with style control","description":"Generates original written content (stories, articles, marketing copy) with controllable style, tone, and narrative structure through style-aware prompting and iterative refinement. The model maintains consistent voice across long-form content while respecting genre conventions and adapting to specified audience and purpose.","intents":["Generate blog posts and articles on technical topics with specific tone","Create marketing copy and product descriptions with brand voice","Write creative fiction with consistent character voices and narrative style","Generate educational content adapted to specific audience levels"],"best_for":["Content creators and marketers using AI for draft generation and ideation","Technical writers generating documentation with consistent voice","Creative professionals using AI as a collaborative writing tool","Teams building content generation platforms"],"limitations":["Generated content may require significant editing for publication quality","Style consistency degrades in very long documents (>5000 words)","Originality is not guaranteed; content may inadvertently echo training data","Fact-checking required for any content with claims or citations"],"requires":["API access with streaming for real-time generation feedback","Clear style guidelines and examples for best results"],"input_types":["natural language prompts with style specifications","style examples or reference texts","outline or structure specifications","tone and audience parameters"],"output_types":["long-form written content","structured articles with sections","creative narratives","marketing and promotional copy"],"categories":["text-generation-language","content-creation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_8","uri":"capability://text.generation.language.instruction.following.with.complex.constraints","name":"instruction following with complex constraints","description":"Follows detailed, multi-part instructions with explicit constraints, edge cases, and conditional logic, maintaining instruction fidelity across complex requests. The model parses instruction hierarchies, handles conflicting constraints through priority reasoning, and produces outputs that satisfy all specified requirements with explicit validation against instruction criteria.","intents":["Execute complex workflows with multiple conditional branches and constraints","Follow detailed formatting and structural requirements in generated content","Handle edge cases and special conditions specified in instructions","Validate outputs against explicit criteria and constraints"],"best_for":["Teams building AI-powered automation systems with complex business logic","Developers creating instruction-following agents for specialized domains","Organizations with strict compliance or formatting requirements"],"limitations":["Instruction complexity has practical limits; >20 constraints may cause degradation","Conflicting constraints require explicit priority specification; implicit resolution may fail","No persistent state across requests; each request must include full instruction context","Validation of constraint satisfaction is best-effort; edge cases may be missed"],"requires":["API access with sufficient context for full instruction specification","Clear, well-structured instruction format (numbered lists, explicit conditions)"],"input_types":["detailed multi-part instructions","constraint specifications","conditional logic rules","formatting and structural requirements"],"output_types":["outputs conforming to all specified constraints","validation results against instruction criteria","explanations of how constraints were satisfied"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-v3.1-terminus__cap_9","uri":"capability://text.generation.language.conversational.explanation.and.socratic.questioning","name":"conversational explanation and socratic questioning","description":"Explains complex concepts through interactive dialogue, using Socratic questioning techniques to guide understanding and identify knowledge gaps. The model adapts explanation depth based on demonstrated understanding, asking clarifying questions and building explanations incrementally rather than providing complete answers immediately.","intents":["Teach technical concepts through guided discovery and questioning","Adapt explanations based on learner's demonstrated understanding level","Identify and address misconceptions through targeted questioning","Create personalized learning experiences that adjust to learner pace"],"best_for":["Educational platforms and tutoring systems","Teams building adaptive learning experiences","Developers creating AI tutors for technical topics","Organizations training employees on complex systems"],"limitations":["Socratic approach requires multiple turns; not suitable for quick reference queries","Effectiveness depends on learner engagement and willingness to answer questions","No persistent learner model; understanding assessment resets between sessions","May be frustrating for learners seeking quick answers rather than deep understanding"],"requires":["API access with multi-turn conversation support","Streaming enabled for real-time interaction feedback"],"input_types":["natural language questions or topics to learn","learner background/experience level (optional)","specific learning objectives"],"output_types":["clarifying questions","guided explanations","follow-up prompts","assessment of understanding"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or direct DeepSeek endpoint","HTTP/2 capable client library","Support for streaming or non-streaming response modes","API access with streaming enabled for real-time reasoning visibility","External tool registry or function schema definitions","JSON parsing capability in client to extract structured action plans","API access to DeepSeek via OpenRouter or direct endpoint","Code formatter/linter for post-processing (optional but recommended)","Language-specific runtime for testing generated code","API access with sufficient context window (8k+ tokens recommended)"],"failure_modes":["Context window is finite; very long conversations (>100k tokens) may experience degradation in consistency","Language consistency enforcement may reduce code-switching flexibility in genuinely multilingual scenarios","No explicit memory persistence across sessions — each conversation starts fresh without prior context","Task decomposition quality degrades on highly ambiguous or under-specified requests","No built-in execution engine — requires external orchestration layer to actually run generated plans","Reasoning traces can be verbose, adding 20-40% to token consumption vs direct instruction","Dependency graph generation is best-effort; circular dependencies or deadlocks require manual intervention","Code generation quality varies by language; less common languages (Rust, Kotlin) have lower accuracy than Python/JavaScript","No real-time IDE integration without custom plugin development","Generated code may require security review; model can produce code with subtle vulnerabilities","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=deepseek-deepseek-v3.1-terminus","compare_url":"https://unfragile.ai/compare?artifact=deepseek-deepseek-v3.1-terminus"}},"signature":"Q7oJidypXQUsPIctNLPQEzfA+WLbSKoCuirUHXx6M5nfP3KmbXkbYb4fVWV+x2YtMpctM+h0poQtS4rmwwYgBQ==","signedAt":"2026-06-22T13:27:44.805Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-deepseek-v3.1-terminus","artifact":"https://unfragile.ai/deepseek-deepseek-v3.1-terminus","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-deepseek-v3.1-terminus","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"}}