{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"deepseek-v3","slug":"deepseek-v3","name":"DeepSeek V3","type":"model","url":"https://www.deepseek.com/","page_url":"https://unfragile.ai/deepseek-v3","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"deepseek-v3__cap_0","uri":"capability://text.generation.language.long.context.text.generation.with.128k.token.window","name":"long-context text generation with 128k token window","description":"Generates coherent text responses up to 128K tokens using a transformer architecture with Multi-Head Latent Attention (MLA), enabling processing of entire documents, codebases, or conversation histories in a single forward pass without context truncation. The MLA mechanism compresses attention heads into latent space, reducing memory overhead compared to standard multi-head attention while maintaining semantic coherence across extended sequences.","intents":["Process entire research papers or technical documentation in a single prompt without splitting","Generate long-form content like books, detailed reports, or comprehensive guides in one request","Maintain conversation context across 100+ turn interactions without losing earlier context","Analyze large codebases or multiple files together for refactoring or architecture decisions"],"best_for":["Developers building document analysis systems requiring full-file processing","Content creators generating long-form material without intermediate summaries","Research teams analyzing multi-document datasets in single inference calls","Teams migrating from models with 4K-32K context to handle real-world document sizes"],"limitations":["128K token hard limit — documents exceeding this require external chunking/summarization","Latency scales linearly with context length; 128K context incurs significantly higher per-token cost than shorter sequences","No documented performance degradation curve — unclear if quality degrades at 100K+ tokens","Requires sufficient GPU VRAM to hold full 128K sequence in memory during inference"],"requires":["API access via DeepSeek Open Platform or local deployment with sufficient GPU memory (VRAM requirements unspecified)","Input text must be valid UTF-8 encoded","Prompt + context must total ≤128K tokens"],"input_types":["text","code","structured text (markdown, JSON, XML)"],"output_types":["text","code","structured text"],"categories":["text-generation-language","long-context-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_1","uri":"capability://code.generation.editing.code.generation.and.completion.with.gpt.4o.level.performance","name":"code generation and completion with gpt-4o-level performance","description":"Generates syntactically correct, semantically meaningful code across 40+ programming languages using transformer-based sequence prediction trained on 14.8 trillion tokens including substantial code corpora. Achieves GPT-4o-level performance on coding benchmarks through instruction tuning and RLHF (post-training method unspecified in documentation), enabling both single-function completion and multi-file architectural generation.","intents":["Generate boilerplate code, utility functions, or API client libraries from natural language specifications","Complete partial code implementations with context-aware suggestions","Refactor existing code across multiple files while maintaining functionality","Generate test cases, fixtures, and mock implementations for unit testing"],"best_for":["Solo developers and small teams using API-based code generation without local deployment","Organizations seeking open-source alternative to GitHub Copilot with unrestricted commercial licensing","Teams building code generation features into products (MIT license permits redistribution)","Developers working in non-mainstream languages where Copilot support is limited"],"limitations":["Specific coding benchmark name and score not documented — 'GPT-4o-level' is marketing claim without detailed methodology","No explicit support matrix for programming languages — 40+ languages claimed but not enumerated","No documentation of code quality metrics (cyclomatic complexity, test coverage, security vulnerability detection)","Context window of 128K limits multi-file refactoring to projects under ~30K lines of code"],"requires":["API key from DeepSeek Open Platform or local GPU deployment","Code input must be valid UTF-8 text (binary formats not supported)","For local deployment: GPU with sufficient VRAM (specifications not provided)"],"input_types":["code","natural language specifications","code comments and docstrings"],"output_types":["code","code with inline comments","test cases"],"categories":["code-generation-editing","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_10","uri":"capability://automation.workflow.training.cost.efficiency.through.optimized.architecture","name":"training cost efficiency through optimized architecture","description":"Achieves GPT-4o-level performance (87.1% MMLU, 90.2% MATH) with training cost of $5.5M through DeepSeekMoE and MLA architectural innovations, reducing training cost by estimated 5-10x compared to dense models of equivalent capability. Cost efficiency enables rapid iteration on model improvements and makes large-scale model development accessible to organizations with limited compute budgets.","intents":["Develop competitive language models with limited training budgets","Iterate rapidly on model improvements without massive compute infrastructure","Reduce environmental impact of model training through efficient architecture","Enable smaller organizations to compete with large AI labs on model capability"],"best_for":["Research organizations with limited compute budgets","Startups building proprietary models","Teams studying efficient model architectures","Organizations prioritizing environmental sustainability"],"limitations":["$5.5M training cost is claimed but methodology not documented — unclear if this includes data acquisition, annotation, or only compute","No comparison to actual training costs of GPT-4o or other baselines — efficiency claim not independently verified","Training cost does not include inference cost — may be offset by higher per-token inference expense","Architectural innovations (MoE, MLA) may not transfer to other domains (vision, multimodal) with same efficiency gains"],"requires":["Access to 14.8 trillion tokens of training data (composition unknown)","Specialized training infrastructure supporting MoE and MLA operations","Expertise in distributed training and optimization"],"input_types":["training data","model architecture specifications"],"output_types":["trained model weights","performance metrics"],"categories":["automation-workflow","infrastructure-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_11","uri":"capability://text.generation.language.multi.turn.conversation.with.context.preservation","name":"multi-turn conversation with context preservation","description":"Maintains conversation context across multiple turns using transformer-based attention mechanisms, enabling coherent multi-turn dialogues where the model references previous messages and maintains consistent persona and knowledge state. Context preservation operates within 128K token window, allowing conversations with 100+ turns before context truncation.","intents":["Build chatbots and conversational AI systems with natural dialogue flow","Enable iterative problem-solving where user refines requests across multiple turns","Support customer support systems with multi-turn interactions","Create interactive tutoring systems with persistent learning context"],"best_for":["Conversational AI and chatbot applications","Customer support and help desk systems","Interactive tutoring and educational assistants","Dialogue-based research and user studies"],"limitations":["Context window of 128K limits conversation length before truncation — approximately 100+ turns depending on message length","No explicit conversation memory management — unclear if model tracks conversation state or requires full history in each request","No documented degradation in coherence as conversation length increases","Context truncation strategy not specified — unclear if oldest messages are dropped or summarized"],"requires":["Application logic to maintain conversation history","API or local deployment to process multi-turn requests","Storage for conversation history (external database or session storage)"],"input_types":["text messages","conversation history"],"output_types":["text responses","contextual replies"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_2","uri":"capability://text.generation.language.mathematical.reasoning.and.problem.solving","name":"mathematical reasoning and problem-solving","description":"Solves mathematical problems including algebra, calculus, geometry, and formal logic through chain-of-thought reasoning patterns learned during training on 14.8 trillion tokens. Achieves 90.2% accuracy on MATH benchmark (claimed GPT-4o parity) by decomposing problems into intermediate reasoning steps and generating step-by-step solutions with symbolic manipulation.","intents":["Solve homework problems and provide detailed step-by-step explanations for educational contexts","Generate mathematical proofs and formal logic derivations for research or verification","Validate mathematical correctness of formulas and equations in scientific papers","Assist in numerical problem-solving for engineering and physics applications"],"best_for":["Educational platforms and tutoring systems requiring step-by-step math explanations","Research teams validating mathematical proofs and derivations","STEM educators building AI-assisted homework help systems","Organizations building scientific computing assistants"],"limitations":["90.2% MATH benchmark score is claimed but methodology not documented — unclear which specific MATH dataset and evaluation protocol","No explicit support for symbolic computation (e.g., SymPy integration) — generates text representations of math rather than executable symbolic code","Performance on novel problem types not documented — may struggle with out-of-distribution mathematical reasoning","No built-in verification mechanism — generated solutions require human validation for high-stakes applications"],"requires":["API access via DeepSeek Open Platform","Mathematical problems formatted as text or LaTeX","For complex problems: 128K context window to include full problem statement and reference materials"],"input_types":["text","LaTeX mathematical notation","code with mathematical expressions"],"output_types":["text with step-by-step reasoning","LaTeX formatted solutions","code (Python, Mathematica, etc.)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_3","uri":"capability://text.generation.language.general.knowledge.retrieval.and.question.answering","name":"general knowledge retrieval and question-answering","description":"Answers factual questions and retrieves knowledge across diverse domains (science, history, culture, current events) using transformer-based language understanding trained on 14.8 trillion tokens. Achieves 87.1% accuracy on MMLU benchmark (claimed GPT-4o parity) by leveraging broad training data and instruction-tuned response formatting for structured knowledge extraction.","intents":["Answer factual questions across academic and professional domains for knowledge workers","Generate summaries of complex topics for educational or research purposes","Retrieve specific facts and definitions from training data without external knowledge bases","Validate factual claims and identify potential inaccuracies in statements"],"best_for":["Educational platforms and learning management systems requiring general knowledge QA","Customer support systems handling knowledge-based inquiries","Research assistants and literature review tools","Teams building chatbots and virtual assistants for knowledge-heavy domains"],"limitations":["87.1% MMLU accuracy is claimed but evaluation methodology not documented — unclear if this includes all MMLU subdomains or selected categories","Knowledge cutoff date not specified — may provide outdated information on recent events or rapidly evolving fields","No explicit fact-checking or confidence scoring — cannot distinguish high-confidence answers from hallucinations","Training data composition unknown — unclear if model has balanced coverage across all MMLU domains or exhibits domain-specific biases"],"requires":["API access via DeepSeek Open Platform","Questions formatted as natural language text","For multi-turn QA: conversation history within 128K token limit"],"input_types":["text","natural language questions","structured queries"],"output_types":["text","structured answers","explanations with reasoning"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_4","uri":"capability://automation.workflow.mixture.of.experts.sparse.activation.for.efficient.inference","name":"mixture-of-experts sparse activation for efficient inference","description":"Routes each token through a subset of 37B active parameters from a total 671B parameter pool using DeepSeekMoE architecture, enabling inference cost and latency comparable to much smaller dense models while maintaining capability parity with larger models. Expert routing is learned during training and applied deterministically at inference time, reducing GPU memory requirements and per-token computation.","intents":["Deploy large language models on resource-constrained infrastructure without sacrificing capability","Reduce inference latency for real-time applications requiring GPT-4o-level performance","Lower API inference costs for high-volume production deployments","Enable local deployment on consumer-grade GPUs that cannot fit dense 671B models"],"best_for":["Cost-sensitive organizations running high-volume inference workloads","Teams deploying models on edge devices or resource-constrained environments","Startups and small companies building LLM-powered products with tight infrastructure budgets","Researchers studying efficient scaling of language models"],"limitations":["Expert routing mechanism not documented — unclear if routing is deterministic, learned per-token, or uses load-balancing heuristics","No published comparison of inference latency vs dense models — claimed efficiency not independently verified","MoE training complexity higher than dense models — requires careful load balancing to avoid expert collapse","GPU memory requirements for 671B model not specified — unclear if sparse activation enables sub-100GB deployment"],"requires":["GPU with support for efficient sparse operations (NVIDIA A100/H100 recommended, but specific requirements unspecified)","Inference framework supporting MoE routing (vLLM, TensorRT-LLM, or custom implementation)","For local deployment: sufficient GPU VRAM to hold active 37B parameters plus KV cache"],"input_types":["text tokens"],"output_types":["text tokens","routing decisions (internal)"],"categories":["automation-workflow","infrastructure-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_5","uri":"capability://automation.workflow.multi.head.latent.attention.for.memory.efficient.long.context.processing","name":"multi-head latent attention for memory-efficient long-context processing","description":"Compresses multi-head attention mechanisms into latent space using learned projections, reducing memory overhead and computation of attention operations while maintaining semantic quality across 128K token sequences. MLA replaces standard multi-head attention's O(n²) memory complexity with a more efficient latent representation, enabling longer contexts on fixed GPU memory budgets.","intents":["Process documents longer than 32K tokens on GPUs with limited VRAM","Reduce inference latency for long-context applications by decreasing attention computation","Enable batch processing of multiple long documents simultaneously","Support real-time streaming of long conversations without context truncation"],"best_for":["Organizations processing documents at scale with memory-constrained infrastructure","Real-time systems requiring low-latency responses on long contexts","Research teams studying efficient attention mechanisms","Teams building document analysis and RAG systems"],"limitations":["MLA mechanism not formally documented — no published paper or technical specification provided","No published benchmarks comparing MLA vs standard attention on long contexts — efficiency gains claimed but not quantified","Latency scaling behavior at 100K+ tokens not documented — unclear if linear or sublinear","No guidance on optimal latent dimension selection — may require tuning for specific use cases"],"requires":["GPU with sufficient VRAM for 128K token sequences (exact requirements unspecified)","Inference framework supporting custom attention implementations","For optimal performance: NVIDIA GPUs with tensor cores (A100, H100, L40S)"],"input_types":["text tokens","attention queries and keys"],"output_types":["attention outputs","latent representations"],"categories":["automation-workflow","infrastructure-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_6","uri":"capability://tool.use.integration.unrestricted.commercial.use.under.mit.license","name":"unrestricted commercial use under mit license","description":"Distributes model weights and architecture under MIT license, permitting unrestricted commercial use, modification, and redistribution without royalty payments or usage restrictions. This licensing approach enables organizations to build proprietary products, fine-tune models for commercial applications, and integrate DeepSeek V3 into closed-source systems without legal constraints.","intents":["Build commercial products using DeepSeek V3 without licensing fees or usage restrictions","Fine-tune the model on proprietary datasets for domain-specific applications","Integrate model weights into closed-source applications and SaaS products","Redistribute modified versions of the model in commercial offerings"],"best_for":["Startups and small companies building LLM-powered products with limited budgets","Organizations requiring full control over model deployment and customization","Teams building proprietary fine-tuned variants for competitive advantage","Enterprises with strict data governance requiring on-premise deployment"],"limitations":["MIT license claim not independently verified — no official license file or legal documentation provided in source material","License terms may not cover training data usage — unclear if commercial training data usage is permitted","No warranty or liability protection — MIT license provides no guarantees on model performance or safety","Potential trademark restrictions — 'DeepSeek' branding may have separate usage restrictions not covered by MIT license"],"requires":["Acceptance of MIT license terms","Proper attribution if required by specific MIT license variant","Compliance with any applicable export controls (model may be subject to US export restrictions)"],"input_types":["model weights","architecture specifications"],"output_types":["modified models","fine-tuned variants","commercial products"],"categories":["tool-use-integration","legal-compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_7","uri":"capability://tool.use.integration.api.based.inference.via.deepseek.open.platform","name":"api-based inference via deepseek open platform","description":"Provides REST API access to DeepSeek V3 through the DeepSeek Open Platform, enabling developers to integrate the model into applications without local deployment. API supports standard text generation parameters (temperature, top-p, max-tokens) and returns structured JSON responses with generated text, token counts, and usage metadata.","intents":["Integrate DeepSeek V3 into web applications and mobile apps without GPU infrastructure","Build chatbots and conversational AI systems using API endpoints","Prototype and test model capabilities before committing to local deployment","Scale inference across multiple requests without managing GPU clusters"],"best_for":["Developers building web applications and SaaS products","Teams without GPU infrastructure or DevOps expertise","Rapid prototyping and MVP development","Organizations with variable inference loads requiring elastic scaling"],"limitations":["API documentation not provided in source material — endpoint specifications, rate limits, and pricing unknown","No published latency or throughput benchmarks — performance characteristics unclear","API availability and uptime SLA not documented","Data privacy and retention policies for API requests not specified — unclear if requests are logged or used for model improvement"],"requires":["API key from DeepSeek Open Platform (registration and authentication required)","HTTP client library (curl, requests, axios, etc.)","Network connectivity to DeepSeek API endpoints","Compliance with DeepSeek API terms of service"],"input_types":["text","JSON request bodies with prompt and parameters"],"output_types":["JSON responses with generated text","token usage metadata","error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_8","uri":"capability://text.generation.language.web.interface.and.chat.application.for.interactive.use","name":"web interface and chat application for interactive use","description":"Provides web-based chat interface (DeepSeek App and web version) enabling non-technical users to interact with V3 model through conversational UI without API integration or local deployment. Interface supports multi-turn conversations, context preservation across turns, and real-time streaming of generated responses.","intents":["Explore model capabilities through interactive conversation without technical setup","Use model for writing, brainstorming, and content creation tasks","Test model behavior and quality before integrating into applications","Access model functionality from any device with web browser"],"best_for":["Non-technical users and content creators","Researchers evaluating model capabilities","Teams prototyping use cases before development","Users without API integration expertise"],"limitations":["Web interface specifications not documented — unclear if it supports file uploads, image inputs, or other advanced features","No information on conversation history storage or privacy — unclear if conversations are persisted or deleted","Session management and timeout policies not specified","No documented rate limiting or usage quotas for free web access"],"requires":["Web browser with JavaScript support","Internet connectivity to DeepSeek servers","Optional: DeepSeek account for conversation history (account requirement unclear)"],"input_types":["text","natural language prompts"],"output_types":["text","streaming responses","formatted output"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__cap_9","uri":"capability://text.generation.language.instruction.tuned.response.formatting.for.structured.outputs","name":"instruction-tuned response formatting for structured outputs","description":"Generates responses formatted according to instruction-tuning objectives, producing structured outputs including step-by-step reasoning, code with comments, formatted lists, and other organized response formats. Instruction tuning (method unspecified) enables the model to follow complex multi-part instructions and produce outputs matching specified formats without explicit prompt engineering.","intents":["Generate code with inline documentation and type hints","Produce step-by-step solutions with clear reasoning for educational content","Create structured data (JSON, CSV, tables) from natural language specifications","Format responses for specific use cases (emails, reports, technical documentation)"],"best_for":["Applications requiring structured, formatted outputs","Educational systems needing step-by-step explanations","Data extraction and transformation pipelines","Content generation systems with specific formatting requirements"],"limitations":["Instruction tuning methodology not documented — unclear if RLHF, supervised fine-tuning, or other approach used","No published evaluation of instruction-following accuracy — unclear how reliably model follows complex format specifications","Format compliance not guaranteed — model may deviate from requested formats in edge cases","No explicit support for custom output schemas or validation"],"requires":["Clear, specific instructions in prompts describing desired output format","For complex formats: examples or templates in prompt context"],"input_types":["text","natural language instructions","format specifications"],"output_types":["formatted text","code with documentation","structured data","step-by-step reasoning"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-v3__headline","uri":"capability://code.generation.editing.open.source.mixture.of.experts.model.for.text.and.code.generation","name":"open-source mixture-of-experts model for text and code generation","description":"DeepSeek V3 is an advanced open-source mixture-of-experts model designed for high-performance text and code generation, achieving top benchmark scores at a fraction of the training cost, making it ideal for developers seeking powerful AI capabilities.","intents":["best open-source AI model","mixture-of-experts model for code generation","high-performance text generation model","open-source model for commercial use","AI model with large context window"],"best_for":["developers looking for cost-effective AI solutions","companies needing unrestricted commercial use"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["API access via DeepSeek Open Platform or local deployment with sufficient GPU memory (VRAM requirements unspecified)","Input text must be valid UTF-8 encoded","Prompt + context must total ≤128K tokens","API key from DeepSeek Open Platform or local GPU deployment","Code input must be valid UTF-8 text (binary formats not supported)","For local deployment: GPU with sufficient VRAM (specifications not provided)","Access to 14.8 trillion tokens of training data (composition unknown)","Specialized training infrastructure supporting MoE and MLA operations","Expertise in distributed training and optimization","Application logic to maintain conversation history"],"failure_modes":["128K token hard limit — documents exceeding this require external chunking/summarization","Latency scales linearly with context length; 128K context incurs significantly higher per-token cost than shorter sequences","No documented performance degradation curve — unclear if quality degrades at 100K+ tokens","Requires sufficient GPU VRAM to hold full 128K sequence in memory during inference","Specific coding benchmark name and score not documented — 'GPT-4o-level' is marketing claim without detailed methodology","No explicit support matrix for programming languages — 40+ languages claimed but not enumerated","No documentation of code quality metrics (cyclomatic complexity, test coverage, security vulnerability detection)","Context window of 128K limits multi-file refactoring to projects under ~30K lines of code","$5.5M training cost is claimed but methodology not documented — unclear if this includes data acquisition, annotation, or only compute","No comparison to actual training costs of GPT-4o or other baselines — efficiency claim not independently verified","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"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-v3","compare_url":"https://unfragile.ai/compare?artifact=deepseek-v3"}},"signature":"mPG+eev1XSyv/MHAmdML7zSfkgR+GQ9Qb6ysnRin8VWAe4jPHBkLgsW5F/09kA+Ln26/KjBAPhiikBMR1ofzAQ==","signedAt":"2026-06-19T23:06:14.228Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-v3","artifact":"https://unfragile.ai/deepseek-v3","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-v3","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"}}