{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-deepseek","slug":"deepseek","name":"DeepSeek","type":"model","url":"https://www.deepseek.com/","page_url":"https://unfragile.ai/deepseek","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-deepseek__cap_0","uri":"capability://text.generation.language.multi.variant.llm.inference.with.specialized.model.selection","name":"multi-variant llm inference with specialized model selection","description":"DeepSeek provides a model family spanning general-purpose (V3, V4), reasoning-optimized (R1), code-specialized (Coder V2), vision-language (VL), and mathematics-focused (Math) variants. Users select the appropriate model variant via web interface, mobile app, or API based on task requirements, with each variant optimized for distinct capability profiles. The architecture supports routing requests to task-specific model weights rather than using a single generalist model.","intents":["Select the right model variant for reasoning-heavy vs code generation vs math problem solving","Switch between general-purpose and specialized models without changing application code","Access vision-language capabilities for multimodal tasks alongside text-only models","Evaluate model performance across different domains without managing separate deployments"],"best_for":["Teams building multi-domain AI applications requiring specialized model selection","Enterprises evaluating model performance across reasoning, coding, and vision tasks","Developers prototyping domain-specific AI features without infrastructure overhead"],"limitations":["Model variant selection is manual — no automatic routing based on input type or task complexity","Specific performance characteristics and benchmark comparisons for each variant are unknown","No documented guidance on when to use V3 vs V4 or R1 vs general-purpose variants"],"requires":["API access to DeepSeek platform (credentials/API key)","Network connectivity to DeepSeek API endpoints","Knowledge of which model variant suits the target task"],"input_types":["text prompts","code snippets (for Coder V2)","images (for VL variant)","mathematical problem statements (for Math variant)"],"output_types":["text responses","code generation","structured reasoning traces (R1)","image descriptions/analysis (VL)"],"categories":["text-generation-language","code-generation-editing","image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_1","uri":"capability://text.generation.language.web.based.conversational.chat.interface.with.session.persistence","name":"web-based conversational chat interface with session persistence","description":"DeepSeek provides a web-accessible chat interface at deepseek.com enabling real-time conversational interaction with selected model variants. The interface maintains conversation history and context across multiple turns, allowing users to build multi-turn dialogues without manual context management. Session state is persisted server-side, enabling users to resume conversations across browser sessions.","intents":["Have natural multi-turn conversations with AI without managing context manually","Access DeepSeek models through a browser without API integration","Resume previous conversations and maintain context across sessions","Test model behavior interactively before integrating via API"],"best_for":["Non-technical users and business stakeholders evaluating model capabilities","Developers prototyping prompts and testing model behavior before API integration","Teams without engineering resources to build custom interfaces"],"limitations":["Web interface is stateless per browser session — no cross-device conversation sync documented","No documented export/download of conversation history","Rate limiting or usage quotas for web interface are unknown","No API for programmatic conversation management from web interface"],"requires":["Web browser with JavaScript enabled","Internet connectivity to deepseek.com","Optional: DeepSeek account for session persistence (account requirement unknown)"],"input_types":["text prompts","multi-turn conversational exchanges"],"output_types":["text responses","formatted markdown (assumed)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_10","uri":"capability://text.generation.language.multi.language.support.with.chinese.english.optimization","name":"multi-language support with chinese-english optimization","description":"DeepSeek models support Chinese and English language interfaces and likely support both languages in model inference. The platform provides Chinese-language website and documentation alongside English, suggesting dual-language optimization in training data and tokenization. Models are positioned for both Chinese and English-speaking users and enterprises.","intents":["Use DeepSeek models for Chinese language tasks without language barrier","Build applications serving Chinese and English-speaking users","Access model capabilities in preferred language (Chinese or English)","Leverage Chinese language expertise in model training and optimization"],"best_for":["Chinese enterprises and developers building AI applications","Multilingual teams requiring Chinese-English model support","Applications targeting Chinese-speaking markets","Teams evaluating non-English LLM capabilities"],"limitations":["Supported languages beyond Chinese and English are unknown","Relative model performance on Chinese vs English tasks is undocumented","Chinese-specific tokenization and optimization details are unknown","No documented support for other Asian languages (Japanese, Korean, etc.)","Code generation and technical content performance in Chinese is unknown"],"requires":["Input in Chinese or English language","Optional: language preference specification (if supported)"],"input_types":["Chinese text prompts","English text prompts","mixed Chinese-English prompts (support unknown)"],"output_types":["Chinese text responses","English text responses","mixed language output (support unknown)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_11","uri":"capability://tool.use.integration.usage.based.api.pricing.with.per.model.cost.tracking","name":"usage-based api pricing with per-model cost tracking","description":"DeepSeek Open Platform implements usage-based pricing where API calls are charged based on model variant, input/output tokens, and task complexity. Pricing page exists but specific rates are unknown. Different model variants (R1, V3, Coder V2, VL, Math) likely have different per-token costs reflecting computational requirements. Users can track usage and costs through platform dashboard.","intents":["Understand cost implications of different model variant selections","Budget and forecast API costs for production deployments","Optimize model selection based on cost-performance tradeoffs","Monitor and control API spending through usage tracking"],"best_for":["Teams deploying DeepSeek models to production with cost constraints","Enterprises evaluating DeepSeek cost vs alternative providers","Developers optimizing model selection for cost efficiency","Finance teams tracking AI infrastructure spending"],"limitations":["Specific pricing per model variant is unknown","Pricing structure (per-token, per-request, tiered) is unknown","Volume discounts or enterprise pricing are undocumented","Free tier or trial credits are unknown","Pricing for specialized variants (R1, Coder V2, VL, Math) vs base model is unknown","No documented price comparison with competitors"],"requires":["DeepSeek API account with billing setup","Payment method (credit card, etc. — payment methods unknown)","API usage tracking and monitoring (dashboard access unknown)"],"input_types":["API calls to any model variant"],"output_types":["usage reports and cost breakdowns","billing statements"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_2","uri":"capability://text.generation.language.mobile.application.deployment.with.native.platform.support","name":"mobile application deployment with native platform support","description":"DeepSeek offers native mobile applications (platform specifics unknown) enabling access to model variants from iOS and/or Android devices. Mobile apps provide offline-capable UI and potentially optimized inference for mobile hardware constraints, though specific optimization details are undocumented. Apps maintain feature parity with web interface for model selection and conversation management.","intents":["Access DeepSeek models from mobile devices without browser overhead","Use specialized models (Coder V2, Math, R1) on mobile for on-the-go tasks","Maintain conversation context across mobile and desktop sessions","Leverage mobile-optimized UI for touch-based interaction"],"best_for":["Mobile-first users and field teams requiring AI assistance on smartphones","Developers testing mobile-specific prompt behaviors and model responses","Teams deploying AI features to consumer mobile applications"],"limitations":["Supported platforms (iOS, Android, or both) are unknown","Minimum OS version requirements are unknown","Offline capability and local caching behavior are undocumented","Mobile-specific rate limiting or feature restrictions are unknown","Cross-device conversation sync is not documented"],"requires":["iOS or Android device (specific versions unknown)","Mobile app installation from app store (store links unknown)","Internet connectivity (offline support unknown)"],"input_types":["text prompts via mobile keyboard","voice input (if supported — undocumented)","image capture (if VL variant supported on mobile — undocumented)"],"output_types":["text responses","formatted content (markdown support unknown)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_3","uri":"capability://tool.use.integration.restful.api.access.with.multi.model.endpoint.routing","name":"restful api access with multi-model endpoint routing","description":"DeepSeek exposes an 'Open Platform' (开放平台) API enabling programmatic access to model variants via HTTP endpoints. Developers authenticate with API keys and route requests to specific model variants (R1, V3, V4, Coder V2, VL, Math) through distinct endpoints or model selection parameters. API supports standard request/response patterns for text generation, code completion, and vision tasks, with pricing tracked per API call.","intents":["Integrate DeepSeek models into custom applications without building chat UI","Programmatically select model variants based on task type or input characteristics","Build production systems with API-based inference and usage tracking","Batch process requests across multiple model variants for comparison or ensemble approaches"],"best_for":["Backend engineers building production AI applications with DeepSeek models","Teams requiring programmatic model selection and request routing","Enterprises with existing API-based ML infrastructure","Developers building multi-model systems with fallback/ensemble patterns"],"limitations":["Specific API endpoints, request/response schemas, and authentication patterns are unknown","Rate limiting, quota management, and pricing per model variant are undocumented","Streaming response support is unknown","Batch processing or async job submission capabilities are unknown","Error handling and retry semantics are undocumented","No documented SLA or uptime guarantees"],"requires":["API key from DeepSeek Open Platform (registration process unknown)","HTTP client library (language-agnostic)","Network connectivity to DeepSeek API endpoints","Understanding of model variant capabilities to select appropriate endpoint"],"input_types":["JSON request bodies with text prompts","Model variant identifier/selection parameter","Optional: image data for VL variant (format unknown)","Optional: code snippets for Coder V2"],"output_types":["JSON response with generated text","Structured metadata (token counts, model version, etc. — format unknown)","Optional: streaming token responses (if supported)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_4","uri":"capability://planning.reasoning.reasoning.optimized.inference.with.explicit.chain.of.thought.generation","name":"reasoning-optimized inference with explicit chain-of-thought generation","description":"DeepSeek R1 variant is specifically optimized for reasoning tasks, generating explicit reasoning traces or chain-of-thought outputs before final answers. The model architecture likely includes training objectives that encourage step-by-step problem decomposition and intermediate reasoning visibility. R1 is positioned as achieving 'world-class reasoning performance' (推理性能), suggesting architectural differences from general-purpose variants in how reasoning is represented and generated.","intents":["Solve complex reasoning problems with visible intermediate steps for verification","Debug model reasoning by inspecting chain-of-thought outputs","Improve answer quality for math, logic, and multi-step problems","Understand model decision-making process for explainability requirements"],"best_for":["Teams solving complex reasoning problems (math, logic, planning)","Enterprises requiring explainable AI with visible reasoning traces","Researchers studying reasoning capabilities and failure modes","Educational applications where showing work is required"],"limitations":["Reasoning trace format and structure are undocumented","No documented control over reasoning verbosity or depth","Performance overhead vs general-purpose models is unknown","Specific reasoning domains (math vs logic vs planning) are not differentiated","No documented limitations on reasoning complexity or problem size"],"requires":["Selection of R1 model variant (via web interface or API)","Problems requiring explicit reasoning (not applicable to simple factual queries)","Tolerance for longer response times (reasoning generation adds latency — amount unknown)"],"input_types":["text prompts for reasoning tasks","math problems","logic puzzles","multi-step planning problems"],"output_types":["reasoning trace/chain-of-thought (format unknown)","final answer","optional: intermediate conclusions or sub-problem solutions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_5","uri":"capability://code.generation.editing.code.generation.and.completion.with.language.specific.optimization","name":"code generation and completion with language-specific optimization","description":"DeepSeek Coder V2 variant is specialized for code generation, completion, and analysis tasks. The model is trained on code-heavy datasets and optimized for multiple programming languages, enabling context-aware code completion, function generation, and code review. Coder V2 likely uses code-specific tokenization and training objectives (e.g., next-token prediction on code, code-to-documentation generation) distinct from general-purpose models.","intents":["Generate code snippets and complete functions in multiple programming languages","Refactor or optimize existing code with language-aware transformations","Explain code behavior and generate documentation from code","Debug code by analyzing error messages and suggesting fixes"],"best_for":["Software developers using AI-assisted code generation and completion","Teams integrating code generation into IDE plugins or development tools","Enterprises with polyglot codebases requiring multi-language support","Code review and documentation automation workflows"],"limitations":["Supported programming languages are unknown","Code context window size and maximum file size are undocumented","No documented support for language-specific linting or type checking","Performance on domain-specific languages (DSLs) or less common languages is unknown","No documented integration with IDE plugins or version control systems"],"requires":["Selection of Coder V2 model variant","Code input in supported programming language (language list unknown)","Optional: existing codebase context for completion (context size unknown)"],"input_types":["code snippets","partial function definitions","error messages with code context","natural language code requests (e.g., 'write a function that...')"],"output_types":["generated code","code completions","refactored code","code explanations","documentation/comments"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_6","uri":"capability://image.visual.vision.language.multimodal.understanding.with.image.analysis","name":"vision-language multimodal understanding with image analysis","description":"DeepSeek VL (vision-language) variant processes both text and image inputs, enabling image understanding, visual question answering, and image-to-text tasks. The model architecture integrates vision encoders (likely transformer-based) with language generation components, allowing unified reasoning over visual and textual information. VL variant supports image input in unspecified formats and generates text descriptions, answers, or analysis.","intents":["Analyze images and answer questions about visual content","Generate descriptions or captions for images","Extract text or structured information from images (OCR-adjacent)","Perform visual reasoning tasks combining image and text context"],"best_for":["Teams building image understanding features without separate vision models","Applications requiring visual question answering or image captioning","Document processing and form understanding workflows","Accessibility applications generating image descriptions"],"limitations":["Supported image formats (JPEG, PNG, WebP, etc.) are unknown","Maximum image resolution and file size are undocumented","Number of images per request is unknown","OCR accuracy and text extraction capabilities are undocumented","No documented support for video or multi-frame image sequences","Vision-specific performance benchmarks are unknown"],"requires":["Selection of VL model variant","Image input in supported format (formats unknown)","Optional: text prompt or question about image"],"input_types":["images (format unknown)","text prompts or questions about images","combined image + text queries"],"output_types":["text descriptions of images","answers to visual questions","extracted text from images","structured analysis (format unknown)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_7","uri":"capability://planning.reasoning.mathematics.specialized.reasoning.with.domain.specific.optimization","name":"mathematics-specialized reasoning with domain-specific optimization","description":"DeepSeek Math variant is optimized for mathematical problem solving, including symbolic manipulation, equation solving, and mathematical reasoning. The model is trained on mathematical datasets and likely uses specialized tokenization or training objectives for mathematical notation and symbolic reasoning. Math variant generates step-by-step solutions with mathematical notation preservation.","intents":["Solve mathematical problems with step-by-step solutions","Perform symbolic manipulation and equation solving","Generate mathematical proofs or derivations","Explain mathematical concepts and problem-solving approaches"],"best_for":["Educational platforms requiring math problem solving and tutoring","Scientific computing workflows needing symbolic math assistance","Research teams exploring mathematical reasoning in AI","STEM content creation and homework assistance applications"],"limitations":["Supported mathematical notation formats are unknown","Maximum equation complexity or problem size is undocumented","No documented support for symbolic math libraries (SymPy, Mathematica) integration","Numerical vs symbolic reasoning capabilities are not differentiated","Performance on advanced mathematics (topology, abstract algebra) is unknown"],"requires":["Selection of Math model variant","Mathematical problem in text or notation format","Optional: context or constraints for the problem"],"input_types":["mathematical problems in text","equations and mathematical notation","word problems with mathematical content"],"output_types":["step-by-step solutions","mathematical notation and equations","numerical answers","proofs or derivations"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_8","uri":"capability://planning.reasoning.agentic.workflow.support.with.tool.integration.and.planning","name":"agentic workflow support with tool integration and planning","description":"DeepSeek V4 (preview) explicitly adds 'Agent capabilities' (Agent能力), suggesting architectural support for agentic workflows where models decompose tasks, select tools, and execute multi-step plans. The implementation likely includes function calling, tool schema definition, and execution feedback loops enabling the model to iteratively refine plans based on tool outputs. V4 represents evolution toward autonomous agent support beyond single-turn inference.","intents":["Build autonomous agents that decompose complex tasks into subtasks","Enable models to call external tools and APIs as part of reasoning","Create multi-step workflows where model decisions drive tool selection","Implement feedback loops where tool outputs inform subsequent model decisions"],"best_for":["Teams building autonomous AI agents for business processes","Enterprises automating multi-step workflows with AI decision-making","Developers creating tool-using AI systems without custom orchestration","Research teams exploring agentic AI architectures"],"limitations":["Specific agent capabilities and supported patterns are undocumented","Tool schema definition format and constraints are unknown","Maximum tool call depth or iteration limits are undocumented","Error handling and recovery mechanisms for failed tool calls are unknown","V4 is in preview — stability and API compatibility are uncertain","No documented comparison with other agent frameworks (LangChain, AutoGPT, etc.)"],"requires":["Selection of V4 model variant (preview availability and timeline unknown)","Tool definitions in supported schema format (format unknown)","Execution environment for tool calls (local or remote)"],"input_types":["high-level task descriptions","tool schemas and definitions","feedback from tool execution results"],"output_types":["task decomposition and planning","tool selection and invocation","final results after multi-step execution"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-deepseek__cap_9","uri":"capability://text.generation.language.base.model.inference.with.general.purpose.language.understanding","name":"base model inference with general-purpose language understanding","description":"DeepSeek LLM base model provides general-purpose language understanding and generation across diverse tasks without domain specialization. The base model serves as foundation for other variants (R1, Coder V2, VL, Math) and is available as standalone option for applications not requiring specialized capabilities. Base model uses standard transformer architecture with unspecified parameter count and context window.","intents":["Perform general-purpose text generation and language understanding tasks","Use as baseline for comparison with specialized variants","Build custom applications without domain-specific model overhead","Access DeepSeek inference without committing to specialized variants"],"best_for":["Teams building general-purpose AI applications without specific domain focus","Developers evaluating DeepSeek model quality before specializing","Applications with diverse task requirements not matching specialized variants","Cost-sensitive deployments where general-purpose model suffices"],"limitations":["Model size, parameter count, and architecture are unknown","Context window size is undocumented","Performance on specialized tasks (code, math, reasoning) is unknown","Training data composition and knowledge cutoff are undocumented","No documented performance benchmarks vs other general-purpose models"],"requires":["Selection of DeepSeek LLM base model variant","Text input in any language (supported languages unknown)"],"input_types":["text prompts","multi-turn conversations","diverse task descriptions"],"output_types":["text responses","formatted content (markdown support unknown)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":22,"verified":false,"data_access_risk":"high","permissions":["API access to DeepSeek platform (credentials/API key)","Network connectivity to DeepSeek API endpoints","Knowledge of which model variant suits the target task","Web browser with JavaScript enabled","Internet connectivity to deepseek.com","Optional: DeepSeek account for session persistence (account requirement unknown)","Input in Chinese or English language","Optional: language preference specification (if supported)","DeepSeek API account with billing setup","Payment method (credit card, etc. — payment methods unknown)"],"failure_modes":["Model variant selection is manual — no automatic routing based on input type or task complexity","Specific performance characteristics and benchmark comparisons for each variant are unknown","No documented guidance on when to use V3 vs V4 or R1 vs general-purpose variants","Web interface is stateless per browser session — no cross-device conversation sync documented","No documented export/download of conversation history","Rate limiting or usage quotas for web interface are unknown","No API for programmatic conversation management from web interface","Supported languages beyond Chinese and English are unknown","Relative model performance on Chinese vs English tasks is undocumented","Chinese-specific tokenization and optimization details are unknown","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.25,"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":"2026-06-17T09:51:03.037Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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","compare_url":"https://unfragile.ai/compare?artifact=deepseek"}},"signature":"kcM7ygiNEhjsThcKBg3kz+Xko8pAb5bt506S7yFKQi2MyFa95+5IsvxSV2/boPcNZFh84fS/dHLocxvHeYg9BQ==","signedAt":"2026-06-22T12:18:28.546Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek","artifact":"https://unfragile.ai/deepseek","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek","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"}}