{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-deepseek-deepseek-r1","slug":"deepseek-deepseek-r1","name":"DeepSeek: R1","type":"model","url":"https://openrouter.ai/models/deepseek~deepseek-r1","page_url":"https://unfragile.ai/deepseek-deepseek-r1","categories":["chatbots-assistants"],"tags":["deepseek","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$7.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-deepseek-deepseek-r1__cap_0","uri":"capability://planning.reasoning.chain.of.thought.reasoning.with.visible.inference.tokens","name":"chain-of-thought reasoning with visible inference tokens","description":"DeepSeek R1 implements explicit chain-of-thought reasoning by exposing intermediate reasoning tokens during inference, allowing developers to inspect and validate the model's step-by-step problem-solving process before final output generation. This differs from black-box reasoning where intermediate steps are hidden; here, the full reasoning trace is accessible via API response, enabling transparency into how the model arrived at conclusions.","intents":["I need to understand why the model gave a particular answer so I can debug incorrect reasoning","I want to extract and log the reasoning process for audit trails or educational purposes","I need to validate that the model is using correct logical steps before trusting its output in production"],"best_for":["AI researchers validating reasoning quality in LLM outputs","teams building explainable AI systems where reasoning transparency is required","developers debugging model failures by analyzing intermediate thought processes"],"limitations":["Reasoning token exposure increases response latency and total token consumption compared to non-reasoning models","Visible reasoning tokens may reveal model limitations or logical errors that could undermine user trust","Reasoning trace length is variable and unpredictable, making cost estimation difficult for high-volume applications"],"requires":["API client supporting streaming or full response parsing (OpenRouter or direct DeepSeek API)","Ability to handle variable-length token sequences in responses","Storage/logging infrastructure if persisting reasoning traces for analysis"],"input_types":["text prompts","multi-turn conversation context","structured problem statements"],"output_types":["reasoning tokens (intermediate steps)","final text response","structured reasoning trace"],"categories":["planning-reasoning","explainability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_1","uri":"capability://text.generation.language.open.source.model.weights.with.commercial.api.access","name":"open-source model weights with commercial api access","description":"DeepSeek R1 is available both as downloadable open-source weights (671B full model) and via commercial API endpoints (OpenRouter, direct DeepSeek API). This dual availability allows developers to either self-host for complete control and zero API costs, or use managed inference for simplified deployment without infrastructure overhead. The model uses a mixture-of-experts architecture where only 37B of 671B parameters activate per forward pass.","intents":["I want to run the model locally on my infrastructure without sending data to third-party APIs","I need to fine-tune or customize the model for domain-specific tasks using the open weights","I want to evaluate the model's reasoning quality before committing to API costs"],"best_for":["enterprises with data privacy requirements prohibiting cloud inference","researchers fine-tuning models for specialized domains","teams with GPU infrastructure seeking to minimize per-inference costs at scale"],"limitations":["Self-hosting requires significant GPU memory (671B model needs ~1.3TB in FP16, or ~670GB in 8-bit quantization) and specialized infrastructure","Open weights do not include training data or detailed training procedures, limiting reproducibility","API rate limits and pricing on OpenRouter may be less favorable than direct cloud providers for high-volume use"],"requires":["For self-hosting: GPU with minimum 80GB VRAM (H100) or distributed setup across multiple GPUs","For API access: OpenRouter API key or DeepSeek API credentials","Inference framework supporting mixture-of-experts (vLLM, TensorRT-LLM, or similar)","Python 3.9+ with CUDA 12.0+ for local deployment"],"input_types":["text prompts","conversation history","code snippets for analysis"],"output_types":["text completions","reasoning tokens","structured responses"],"categories":["text-generation-language","model-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_2","uri":"capability://planning.reasoning.multi.step.problem.solving.with.extended.context.windows","name":"multi-step problem solving with extended context windows","description":"DeepSeek R1 handles complex, multi-step problems by maintaining reasoning coherence across extended context, leveraging its 671B parameter capacity to decompose problems into logical substeps and track dependencies across reasoning chains. The model can process long problem statements and maintain consistency across multiple reasoning iterations without losing context, enabling solution of problems requiring 5-20+ reasoning steps.","intents":["I need to solve complex math problems that require multiple intermediate calculations and logical steps","I want the model to break down a coding problem into subtasks and verify each step before final implementation","I need to analyze a multi-part question where later answers depend on earlier reasoning"],"best_for":["educational platforms grading complex problem solutions with step-by-step verification","research teams analyzing multi-faceted problems requiring rigorous logical decomposition","developers building AI tutoring systems that need to explain solution paths"],"limitations":["Extended reasoning increases latency significantly (10-60 seconds for complex problems vs 1-5 seconds for simple queries)","Token consumption scales with reasoning depth, making cost unpredictable for variable-complexity workloads","Reasoning quality degrades on problems requiring domain knowledge outside training data (specialized physics, proprietary algorithms)"],"requires":["API client with timeout support (minimum 120 seconds for complex problems)","Token budget sufficient for 2-5x normal token consumption due to reasoning overhead","Streaming support recommended for user-facing applications to show reasoning progress"],"input_types":["text problem statements","mathematical expressions","code snippets with requirements","multi-part questions"],"output_types":["step-by-step reasoning trace","intermediate calculations","final answer with justification","code solutions with explanations"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_3","uri":"capability://code.generation.editing.code.generation.and.analysis.with.reasoning.transparency","name":"code generation and analysis with reasoning transparency","description":"DeepSeek R1 generates code by reasoning through requirements, constraints, and implementation details step-by-step, with full visibility into the reasoning process. The model can analyze existing code, suggest optimizations, identify bugs, and generate implementations across multiple programming languages while exposing intermediate reasoning about design decisions, trade-offs, and correctness verification.","intents":["I need to generate code for a complex algorithm and understand the reasoning behind design choices","I want the model to analyze my code and explain step-by-step why a bug exists and how to fix it","I need to generate code in an unfamiliar language and see the model's reasoning about language-specific patterns"],"best_for":["developers learning new programming languages or frameworks through reasoned code generation","code review teams using AI to validate implementation correctness with explainable reasoning","teams building AI-assisted development tools where code quality and reasoning transparency are critical"],"limitations":["Reasoning overhead adds 5-15 second latency for code generation, making real-time IDE integration challenging","Generated code quality depends on problem clarity; ambiguous requirements lead to verbose reasoning without clear resolution","Reasoning tokens may expose model uncertainty or logical gaps, potentially reducing developer confidence in generated code"],"requires":["API client supporting streaming for progressive code display","IDE or editor integration layer to parse and display reasoning alongside code","Token budget for 3-5x normal consumption due to reasoning overhead"],"input_types":["natural language requirements","code snippets for analysis","pseudocode or algorithm descriptions","existing codebase context"],"output_types":["generated code in multiple languages","reasoning about design decisions","bug analysis and fixes","optimization suggestions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_4","uri":"capability://planning.reasoning.mathematical.problem.solving.with.step.by.step.verification","name":"mathematical problem solving with step-by-step verification","description":"DeepSeek R1 solves mathematical problems by explicitly reasoning through each calculation step, intermediate results, and logical deductions, with full visibility into the reasoning process. The model can handle algebra, calculus, statistics, discrete mathematics, and applied math problems, verifying correctness at each step and backtracking if errors are detected during reasoning.","intents":["I need to solve a complex math problem and see each calculation step to verify correctness","I want to understand where my manual calculation went wrong by comparing against the model's step-by-step reasoning","I need to generate math problems with detailed solutions for educational content"],"best_for":["educational platforms and tutoring systems requiring step-by-step math solutions","researchers validating mathematical proofs and derivations","students learning mathematics through AI-generated explanations with transparent reasoning"],"limitations":["Reasoning quality degrades on problems requiring specialized mathematical knowledge (advanced topology, category theory, cutting-edge research mathematics)","Symbolic computation limitations mean the model may struggle with exact symbolic manipulation versus numerical approximation","Reasoning traces can be verbose for simple problems, reducing efficiency for straightforward calculations"],"requires":["API client with extended timeout support (30-120 seconds for complex proofs)","Ability to parse and render mathematical notation (LaTeX, MathML) from reasoning traces","Token budget for 2-4x normal consumption due to reasoning overhead"],"input_types":["mathematical problem statements","equations and expressions","proof requirements","numerical data for analysis"],"output_types":["step-by-step calculations","intermediate results with justification","final answer with verification","alternative solution approaches"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_5","uri":"capability://tool.use.integration.api.based.inference.with.streaming.reasoning.tokens","name":"api-based inference with streaming reasoning tokens","description":"DeepSeek R1 is accessible via OpenRouter and direct DeepSeek API endpoints, supporting streaming responses that progressively emit reasoning tokens followed by final output. The API implementation allows developers to subscribe to token streams, enabling real-time display of reasoning progress and early termination if reasoning diverges from desired direction. Streaming reduces perceived latency and enables interactive applications.","intents":["I want to show users the model's reasoning in real-time as it solves their problem","I need to build a web application where reasoning is streamed to the browser as it happens","I want to monitor reasoning progress and stop inference early if the model is heading in the wrong direction"],"best_for":["web and mobile applications requiring real-time reasoning display","interactive tutoring systems showing problem-solving progress","teams building AI assistants with transparent reasoning UI"],"limitations":["Streaming adds complexity to client-side parsing and error handling compared to batch requests","Network latency and buffering can cause reasoning tokens to arrive in unpredictable batches, complicating real-time display","Early termination of reasoning streams may result in incomplete or incorrect final answers"],"requires":["OpenRouter API key or DeepSeek API credentials","HTTP client supporting Server-Sent Events (SSE) or WebSocket streaming","Client-side JSON parsing for streaming token objects","Error handling for stream interruptions and timeouts"],"input_types":["text prompts","conversation context","system instructions"],"output_types":["streaming reasoning tokens","final text response","token metadata (type, count)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_6","uri":"capability://automation.workflow.sparse.mixture.of.experts.inference.optimization","name":"sparse mixture-of-experts inference optimization","description":"DeepSeek R1 uses a mixture-of-experts architecture where only 37B of 671B parameters activate per inference pass, reducing computational requirements and latency compared to dense models of equivalent capability. The sparse activation pattern is learned during training and dynamically selected based on input, enabling efficient inference on consumer-grade GPUs while maintaining reasoning quality comparable to much larger dense models.","intents":["I want to run a 671B parameter model on hardware that can't support dense models of that size","I need to reduce inference latency and token costs while maintaining reasoning quality","I want to self-host a capable reasoning model without enterprise-grade GPU infrastructure"],"best_for":["teams with limited GPU budgets seeking to maximize reasoning capability per dollar","organizations deploying models on consumer-grade or mid-range GPU clusters","researchers studying sparse activation patterns and mixture-of-experts efficiency"],"limitations":["Sparse activation patterns are not interpretable; developers cannot easily understand which experts activate for specific inputs","Load balancing across experts may be uneven, causing some GPUs to be underutilized in distributed setups","Quantization and optimization for sparse models is less mature than for dense models, limiting deployment options"],"requires":["Inference framework with mixture-of-experts support (vLLM 0.4+, TensorRT-LLM, or similar)","GPU with minimum 40GB VRAM for single-GPU inference (H100, A100, or equivalent)","Distributed setup with multiple GPUs for optimal throughput"],"input_types":["text prompts","code snippets","problem statements"],"output_types":["text completions","reasoning tokens","inference metrics (expert activation patterns)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_7","uri":"capability://code.generation.editing.multi.language.code.generation.and.reasoning","name":"multi-language code generation and reasoning","description":"DeepSeek R1 generates code across 20+ programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) with explicit reasoning about language-specific idioms, performance characteristics, and best practices. The model reasons through language selection trade-offs, explains why certain patterns are preferred in specific languages, and can refactor code between languages while maintaining semantic equivalence.","intents":["I need to generate code in a language I'm unfamiliar with and understand the reasoning behind language-specific patterns","I want to refactor code from one language to another and see the reasoning about equivalent patterns","I need to understand why a particular language is better suited for a specific problem"],"best_for":["polyglot development teams working across multiple languages","developers learning new programming languages through AI-guided examples","code migration projects requiring language-to-language translation with reasoning"],"limitations":["Reasoning quality varies by language; less common languages (Kotlin, Elixir, Clojure) receive less detailed reasoning","Language-specific idioms and best practices may be outdated or reflect training data biases","Refactoring between languages may not preserve all non-functional properties (performance, memory usage)"],"requires":["API client supporting streaming for progressive code display","Syntax highlighting and language detection for generated code","Token budget for 3-5x normal consumption due to reasoning overhead"],"input_types":["natural language requirements","code snippets in any language","pseudocode or algorithm descriptions","language-specific constraints"],"output_types":["generated code in specified language","reasoning about language selection and idioms","refactored code with equivalence explanation","performance and best-practice recommendations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_8","uri":"capability://text.generation.language.conversational.reasoning.with.multi.turn.context.preservation","name":"conversational reasoning with multi-turn context preservation","description":"DeepSeek R1 maintains reasoning coherence across multi-turn conversations, allowing users to ask follow-up questions that build on previous reasoning steps. The model can reference earlier parts of a reasoning chain, correct previous conclusions, and extend reasoning in new directions while preserving context consistency. This enables iterative problem-solving where each turn refines or extends the previous reasoning.","intents":["I want to ask follow-up questions about a previous answer and have the model reference earlier reasoning steps","I need to iteratively refine a solution by asking the model to reconsider specific reasoning steps","I want to explore alternative solution paths while maintaining context from previous reasoning"],"best_for":["interactive tutoring systems where students ask clarifying questions about reasoning","collaborative problem-solving sessions where multiple stakeholders refine solutions iteratively","debugging workflows where developers ask the model to reconsider specific implementation choices"],"limitations":["Context window limitations mean very long conversations may lose earlier reasoning context","Multi-turn reasoning compounds latency; each turn adds 5-15 seconds, making rapid iteration slow","Token consumption grows linearly with conversation length, making long sessions expensive"],"requires":["API client supporting conversation history management","Storage for conversation context and reasoning traces","Token budget scaling with conversation length (2-3x for typical multi-turn sessions)"],"input_types":["initial problem statement","follow-up questions","clarifications and constraints","alternative problem formulations"],"output_types":["reasoning traces for each turn","updated solutions incorporating feedback","references to previous reasoning steps","alternative approaches"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1__cap_9","uri":"capability://data.processing.analysis.structured.output.generation.with.reasoning.validation","name":"structured output generation with reasoning validation","description":"DeepSeek R1 can generate structured outputs (JSON, XML, YAML) with explicit reasoning about schema compliance, data validation, and semantic correctness. The model reasons through each field in the output structure, validates constraints, and explains why specific values were chosen, enabling developers to understand and verify the correctness of structured data generation before using it in downstream systems.","intents":["I need to generate JSON data and understand the reasoning behind each field value","I want the model to validate that generated structured data complies with a schema before returning it","I need to extract structured information from unstructured text with reasoning about extraction decisions"],"best_for":["data extraction pipelines requiring validation and explainability","API response generation systems where output correctness is critical","teams building AI-assisted data entry systems with reasoning transparency"],"limitations":["Reasoning overhead makes structured generation slower than non-reasoning models (5-15 second latency)","Schema compliance is not guaranteed; the model may generate invalid JSON or violate constraints despite reasoning","Complex nested structures may result in verbose reasoning that doesn't proportionally improve output quality"],"requires":["Schema definition (JSON Schema, OpenAPI, or similar) provided to the model","JSON parsing and validation library for output verification","Token budget for 2-4x normal consumption due to reasoning overhead"],"input_types":["unstructured text","schema definitions","extraction requirements","validation constraints"],"output_types":["structured data (JSON, XML, YAML)","reasoning about field values","validation results","extraction confidence indicators"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API client supporting streaming or full response parsing (OpenRouter or direct DeepSeek API)","Ability to handle variable-length token sequences in responses","Storage/logging infrastructure 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