{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"deepseek-r1","slug":"deepseek-r1","name":"DeepSeek R1","type":"model","url":"https://www.deepseek.com/","page_url":"https://unfragile.ai/deepseek-r1","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"deepseek-r1__cap_0","uri":"capability://planning.reasoning.extended.chain.of.thought.reasoning.with.visible.traces","name":"extended chain-of-thought reasoning with visible traces","description":"DeepSeek R1 performs multi-step reasoning using reinforcement learning-trained chain-of-thought patterns, outputting intermediate reasoning steps visible to users. The model generates explicit reasoning traces before final answers, allowing inspection of the reasoning process. This is implemented through RL fine-tuning that rewards coherent step-by-step problem decomposition rather than direct answer generation.","intents":["I need to see how the model arrived at its answer for debugging or verification purposes","I want to understand the reasoning process for complex problems to validate correctness","I need to trace through multi-step logic to catch errors in the model's thinking","I want to use the reasoning traces as educational material to understand problem-solving approaches"],"best_for":["researchers validating model reasoning quality","educators using AI for teaching problem-solving methodology","developers building systems that require explainable reasoning","teams working on complex reasoning tasks where intermediate steps matter"],"limitations":["Reasoning traces increase latency significantly compared to direct-answer models — exact overhead unknown but typical for CoT models is 2-10x slower","Visible reasoning may expose model uncertainty or contradictions that could reduce user confidence","Reasoning trace quality and correctness are not guaranteed — model can produce plausible-sounding but incorrect intermediate steps","No control over reasoning verbosity or depth — cannot adjust trace granularity per request"],"requires":["API access to DeepSeek R1 (web, API, or local deployment)","Tolerance for increased latency due to reasoning computation","Ability to parse and handle multi-paragraph reasoning output before final answer"],"input_types":["natural language problem statements","mathematical questions","code debugging queries","scientific reasoning questions"],"output_types":["text with embedded reasoning traces","structured reasoning steps followed by final answer"],"categories":["planning-reasoning","explainability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_1","uri":"capability://planning.reasoning.mathematics.problem.solving.with.aime.level.performance","name":"mathematics problem solving with aime-level performance","description":"DeepSeek R1 achieves 79.8% accuracy on AIME 2024 (American Invitational Mathematics Examination), a competition-level mathematics benchmark. The model handles multi-step algebraic, geometric, and number-theoretic problems through its RL-trained reasoning capability combined with mathematical knowledge from pretraining. Performance is claimed to match OpenAI o1 on mathematics tasks.","intents":["I need to solve competition-level math problems programmatically","I want to verify mathematical solutions or generate step-by-step proofs","I need a model that can handle complex algebra, geometry, and number theory","I want to use AI for mathematics tutoring or homework assistance at advanced levels"],"best_for":["mathematics educators and tutoring platforms","competitive programming and math competition preparation","research teams validating mathematical reasoning in AI","educational technology companies building advanced problem-solving tools"],"limitations":["AIME 2024 benchmark is specific to competition mathematics — performance on other mathematical domains (statistics, applied math, numerical computation) is unknown","79.8% accuracy means ~20% of AIME problems still fail — not suitable for mission-critical mathematical verification without human review","No symbolic computation capability mentioned — cannot perform exact symbolic algebra or formal proof verification","Benchmark methodology and test set composition not documented — cannot assess whether performance generalizes to similar problems"],"requires":["API access to DeepSeek R1 model","Ability to format mathematical problems as natural language queries","Tolerance for reasoning latency (CoT models are slower than direct-answer models)"],"input_types":["natural language mathematical problem statements","LaTeX-formatted equations","geometry problem descriptions","number theory and combinatorics questions"],"output_types":["step-by-step mathematical reasoning","final numerical or symbolic answer","proof sketches"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_10","uri":"capability://text.generation.language.multi.language.problem.solving.with.chinese.and.english.support","name":"multi-language problem solving with chinese and english support","description":"DeepSeek R1 supports problem-solving in multiple languages, with explicit support for Chinese and English visible on the platform. The model can understand and reason about problems stated in these languages, producing reasoning traces and answers in the input language. Language support beyond Chinese and English is undocumented.","intents":["I need to solve problems stated in Chinese without translation","I want to use the model for Chinese-language education and tutoring","I need reasoning output in the same language as the input","I want to serve Chinese-speaking users without language barriers"],"best_for":["Chinese-speaking users and organizations","multilingual education platforms","teams serving non-English markets","research on reasoning in non-English languages"],"limitations":["Language support is only documented for Chinese and English — support for other languages is unknown","Reasoning quality may differ between languages — model may be better trained on English reasoning","No documentation of language-specific performance benchmarks","Mixed-language inputs (code with Chinese comments) may not be handled correctly","Translation quality if model internally translates to English is unknown"],"requires":["API access to DeepSeek R1","Input in Chinese or English"],"input_types":["natural language problems in Chinese or English","code with comments in Chinese or English","mathematical problems in either language"],"output_types":["reasoning traces and answers in the input language"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_11","uri":"capability://tool.use.integration.api.based.inference.with.cloud.deployment","name":"api-based inference with cloud deployment","description":"DeepSeek R1 is available through a cloud API allowing programmatic access to the model without local hardware requirements. Users submit queries via HTTP requests and receive responses containing reasoning traces and answers. The API abstracts away infrastructure management and provides scalable inference.","intents":["I want to use a reasoning model without managing GPU infrastructure","I need to integrate reasoning into my application via API calls","I want to scale reasoning inference without capacity planning","I need to avoid the complexity of local model deployment"],"best_for":["startups and small teams without ML infrastructure","applications with variable reasoning demand","rapid prototyping and MVP development","teams prioritizing time-to-market over cost"],"limitations":["API pricing model not documented — cost per request or subscription unclear","Latency not documented — reasoning models typically have 10-60 second latency","Rate limiting and quota policies unknown — cannot assess production scalability","Data privacy concerns — queries and responses are sent to DeepSeek servers","No SLA or uptime guarantee mentioned — reliability for production use unknown","API documentation not provided — integration details unknown","Vendor lock-in — switching to alternative models requires code changes"],"requires":["Internet connectivity","API key or authentication credentials","HTTP client library","Account on DeepSeek platform"],"input_types":["JSON payloads with problem statements"],"output_types":["JSON responses with reasoning traces and answers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_2","uri":"capability://code.generation.editing.competitive.programming.code.generation.with.codeforces.rating","name":"competitive programming code generation with codeforces rating","description":"DeepSeek R1 generates solutions to competitive programming problems with a Codeforces rating of 2029 (expert level). The model combines code generation with mathematical reasoning to solve algorithmic problems requiring optimization, data structures, and complex logic. Performance is claimed to match OpenAI o1 on coding benchmarks.","intents":["I need to generate solutions to competitive programming problems","I want to understand algorithmic approaches to complex coding challenges","I need a model that can reason about algorithm complexity and optimization","I want to use AI for competitive programming training and practice"],"best_for":["competitive programmers training for contests","algorithm education platforms and coding bootcamps","teams building AI-assisted code generation for algorithmic problems","researchers benchmarking code reasoning capabilities"],"limitations":["Codeforces rating of 2029 is expert-level but not grandmaster — approximately 20-30% of hardest problems still fail","Performance is specific to Codeforces-style problems — generalization to other coding tasks (web development, systems programming, etc.) is unknown","No information on execution time or memory efficiency of generated code — may produce correct but inefficient solutions","Benchmark methodology not documented — unclear if rating is based on actual contest submission or offline evaluation"],"requires":["API access to DeepSeek R1","Ability to format algorithmic problems as natural language or code","Programming language support (likely C++, Python, Java based on Codeforces standards)"],"input_types":["competitive programming problem statements","algorithm descriptions","code snippets with bugs to fix","optimization challenges"],"output_types":["executable code in multiple languages","step-by-step algorithmic reasoning","complexity analysis and optimization suggestions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_3","uri":"capability://planning.reasoning.multi.scale.model.distillation.from.1.5b.to.70b.parameters","name":"multi-scale model distillation from 1.5b to 70b parameters","description":"DeepSeek R1 provides distilled variants at 1.5B, 7B, 8B, 14B, 32B, and 70B parameters, allowing deployment across different hardware constraints and latency requirements. These variants are created through knowledge distillation from the 671B base model, transferring reasoning capability to smaller models. The distillation methodology and performance degradation curves are not documented.","intents":["I need to deploy reasoning models on edge devices or resource-constrained hardware","I want to reduce inference latency while maintaining reasoning quality","I need to run models locally without cloud API dependency","I want to choose the right model size for my latency and accuracy tradeoffs"],"best_for":["edge computing and mobile deployment scenarios","teams with limited GPU/TPU budgets","applications requiring sub-second latency","organizations prioritizing data privacy and local inference"],"limitations":["Distillation methodology is undocumented — cannot assess knowledge transfer efficiency or performance degradation curves","No performance benchmarks provided for smaller variants — unclear how much reasoning quality is lost at each size","1.5B variant may be too small for complex reasoning — typical reasoning models require 7B+ for meaningful CoT","No guidance on which variant to choose for specific use cases — requires empirical testing","Distillation quality unknown — smaller models may produce less coherent reasoning traces"],"requires":["Hardware appropriate to model size (1.5B: ~3GB VRAM, 70B: ~140GB VRAM for full precision)","Quantization support (GGUF, int8, int4) for practical deployment — not documented if available","Local inference framework (vLLM, ollama, llama.cpp, or similar)","MIT license compliance for commercial use"],"input_types":["same as base model — natural language problems, code, math, science"],"output_types":["same as base model — reasoning traces and answers"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_4","uri":"capability://automation.workflow.open.source.model.access.with.mit.licensing","name":"open-source model access with mit licensing","description":"DeepSeek R1 is distributed under MIT license with full source code and model weights available for download and local deployment. This enables researchers and developers to run the model on their own infrastructure, fine-tune it, and integrate it into applications without API dependency. The MIT license permits commercial use, modification, and redistribution.","intents":["I need to deploy a reasoning model without relying on external APIs or vendor lock-in","I want to fine-tune a reasoning model on proprietary data","I need to modify the model architecture or training process for research","I want to integrate a reasoning model into a commercial product without licensing fees"],"best_for":["research teams building on frontier reasoning models","companies with data privacy requirements preventing cloud API use","organizations building proprietary AI products","developers in regions with restricted API access"],"limitations":["MIT license requires attribution — must include license text in distributions","Open-source status may create support burden — no official SLA or guaranteed maintenance","Model weights are large (671B base, 37B active) — downloading and storing requires significant bandwidth and storage","Fine-tuning infrastructure not provided — requires custom training setup","No official model card or documentation — community-driven documentation may be incomplete"],"requires":["Download infrastructure for model weights (671B+ file size)","Storage capacity for model weights and inference cache","Inference framework supporting the model format (likely safetensors or GGUF)","Compliance with MIT license terms","GPU/TPU hardware for inference (exact requirements unknown)"],"input_types":["model weights in open format","source code for training and inference"],"output_types":["deployable model artifacts","inference code and examples"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_5","uri":"capability://tool.use.integration.web.interface.and.api.access.with.quick.integration","name":"web interface and api access with quick integration","description":"DeepSeek R1 is accessible through multiple interfaces: a web application (deepseek.com), a mobile app, and an API with documented endpoints. The platform claims 'quick integration' and 'smooth experience' for developers. API access allows programmatic integration into applications with standard HTTP requests.","intents":["I want to quickly prototype with a reasoning model without local setup","I need to integrate reasoning capabilities into my application via API","I want to test the model interactively before committing to deployment","I need a web interface for non-technical users to access reasoning"],"best_for":["rapid prototyping and proof-of-concept development","teams without GPU infrastructure","applications requiring cloud-based reasoning","non-technical users wanting to interact with the model"],"limitations":["API documentation not provided in materials — integration details unknown","Pricing model not specified — cost per request or subscription model unclear","Rate limiting and quota policies unknown — cannot assess production scalability","API latency not documented — reasoning models typically have high latency (10-60 seconds)","No SLA or uptime guarantee mentioned — reliability for production use unknown","Web interface may have usage restrictions or data retention policies not documented"],"requires":["Internet connectivity for API access","API key or authentication credentials (format unknown)","HTTP client library for API integration","Account on deepseek.com platform"],"input_types":["text queries via web interface","JSON payloads via API"],"output_types":["HTML rendered responses in web interface","JSON responses from API with reasoning traces and answers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_6","uri":"capability://planning.reasoning.science.reasoning.with.o1.level.performance","name":"science reasoning with o1-level performance","description":"DeepSeek R1 is claimed to match OpenAI o1 performance on science benchmarks, including physics, chemistry, and biology reasoning tasks. The model applies its RL-trained reasoning capability to scientific problem-solving. Specific science benchmarks and performance metrics are not documented.","intents":["I need to solve complex science problems requiring multi-step reasoning","I want to generate scientific explanations and derivations","I need to validate scientific hypotheses or experimental designs","I want to use AI for science education and tutoring"],"best_for":["science education platforms and tutoring services","research teams validating scientific reasoning in AI","STEM educators building AI-assisted learning tools","scientists exploring AI for hypothesis generation"],"limitations":["Science benchmark performance is vaguely claimed as 'matching o1' without specific metrics — cannot assess actual capability","No documentation of which science domains are covered (physics, chemistry, biology, etc.)","Unknown if model can handle domain-specific notation (chemical formulas, physics equations, biological nomenclature)","No information on accuracy for experimental design or safety-critical applications","Science reasoning may require domain-specific knowledge not present in general pretraining"],"requires":["API access to DeepSeek R1","Ability to format science problems as natural language queries","Tolerance for reasoning latency"],"input_types":["natural language science problem statements","scientific notation and equations","experimental design descriptions","scientific literature summaries"],"output_types":["step-by-step scientific reasoning","explanations of physical/chemical/biological phenomena","derivations and proofs","experimental design suggestions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_7","uri":"capability://automation.workflow.sparse.mixture.of.experts.architecture.with.37b.active.parameters","name":"sparse mixture-of-experts architecture with 37b active parameters","description":"DeepSeek R1 uses a 671B parameter Mixture of Experts (MoE) architecture where only 37B parameters are active per forward pass. This sparse activation pattern reduces computational cost and latency compared to dense models of equivalent capability. The specific routing mechanism, expert specialization, and load balancing strategy are not documented.","intents":["I need a reasoning model with lower inference cost than dense equivalents","I want to deploy a large-capability model with reduced computational overhead","I need to understand the efficiency tradeoffs of sparse vs. dense architectures","I want to optimize inference latency and throughput for production deployment"],"best_for":["teams optimizing inference cost and latency","cloud providers deploying reasoning models at scale","researchers studying sparse model architectures","applications with strict latency requirements"],"limitations":["MoE architecture details are undocumented — cannot assess routing efficiency or load balancing","Sparse activation may cause uneven expert utilization — some experts may be underused","MoE models typically have higher memory footprint than dense models due to expert duplication","Inference latency and throughput not documented — cannot verify efficiency claims","Sparse models may have different failure modes than dense models (e.g., expert collapse)","No information on how MoE affects reasoning quality or consistency"],"requires":["Inference framework supporting MoE architectures (vLLM, TensorRT, or similar)","GPU with sufficient memory for expert parameters (exact requirements unknown)","Understanding of MoE-specific optimization techniques"],"input_types":["same as base model"],"output_types":["same as base model"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_8","uri":"capability://planning.reasoning.reasoning.model.distillation.to.smaller.parameter.scales","name":"reasoning model distillation to smaller parameter scales","description":"DeepSeek R1 applies knowledge distillation to transfer reasoning capability from the 671B base model to smaller variants (1.5B through 70B). The distillation process trains smaller models to mimic the reasoning behavior and output of the larger model. Distillation methodology, loss functions, and performance degradation are not documented.","intents":["I want to use reasoning models on hardware with limited VRAM","I need to reduce inference latency while preserving reasoning quality","I want to understand how much reasoning capability is preserved at smaller scales","I need to choose the optimal model size for my accuracy-latency tradeoff"],"best_for":["edge deployment and mobile applications","latency-sensitive applications","resource-constrained environments","teams experimenting with model size tradeoffs"],"limitations":["Distillation methodology is undocumented — cannot assess knowledge transfer efficiency","No performance benchmarks for distilled variants — unclear how much reasoning quality degrades at each size","Smaller models may produce less coherent or less detailed reasoning traces","1.5B variant may be too small for meaningful reasoning — typical reasoning models require 7B+","No guidance on which variant to use for specific tasks — requires empirical evaluation","Distillation may not preserve all reasoning capabilities of the base model"],"requires":["Hardware appropriate to chosen model size","Inference framework supporting the model","Empirical testing to validate performance on target tasks"],"input_types":["same as base model"],"output_types":["same as base model"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__cap_9","uri":"capability://planning.reasoning.transparent.reasoning.output.with.step.by.step.traces","name":"transparent reasoning output with step-by-step traces","description":"DeepSeek R1 outputs reasoning traces as part of standard model output, making the intermediate steps of problem-solving visible to users. This transparency is built into the model's training objective through RL, not added as post-processing. Users can inspect and validate the reasoning process before the final answer.","intents":["I need to audit the model's reasoning for correctness and bias","I want to understand why the model arrived at a particular answer","I need to use reasoning traces for educational purposes","I want to catch errors in the model's logic before relying on the answer"],"best_for":["applications requiring explainability and auditability","educational platforms emphasizing learning methodology","high-stakes domains (medicine, law, finance) requiring reasoning validation","research on model reasoning quality and failure modes"],"limitations":["Reasoning traces increase output length and latency significantly","Visible reasoning may expose model uncertainty or contradictions, reducing user confidence","Reasoning traces are not guaranteed to be correct — model can produce plausible-sounding but incorrect intermediate steps","No control over reasoning verbosity or depth — cannot request concise vs. detailed reasoning","Reasoning quality varies by task — may be incoherent for out-of-distribution problems"],"requires":["API access to DeepSeek R1","Ability to parse and process multi-paragraph reasoning output","Tolerance for increased latency and output length"],"input_types":["same as base model"],"output_types":["text with embedded reasoning traces","structured reasoning steps followed by final answer"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-r1__headline","uri":"capability://planning.reasoning.advanced.reasoning.model.for.mathematics.and.coding","name":"advanced reasoning model for mathematics and coding","description":"DeepSeek R1 is an advanced reasoning model designed for extended chain-of-thought reasoning, excelling in mathematics, coding, and science tasks, with transparent reasoning traces in its output.","intents":["best reasoning model for coding","AI model for mathematics benchmarks","top model for science reasoning","open-source reasoning model for developers","best AI for extended reasoning tasks"],"best_for":["developers seeking open-source solutions","users needing transparent reasoning"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["API access to DeepSeek R1 (web, API, or local deployment)","Tolerance for increased latency due to reasoning computation","Ability to parse and handle multi-paragraph reasoning output before final answer","API access to DeepSeek R1 model","Ability to format mathematical problems as natural language queries","Tolerance for reasoning latency (CoT models are slower than direct-answer models)","API access to DeepSeek R1","Input in Chinese or English","Internet connectivity","API key or authentication credentials"],"failure_modes":["Reasoning traces increase latency significantly compared to direct-answer models — exact overhead unknown but typical for CoT models is 2-10x slower","Visible reasoning may expose model uncertainty or contradictions that could reduce user confidence","Reasoning trace quality and correctness are not guaranteed — model can produce plausible-sounding but incorrect intermediate steps","No control over reasoning verbosity or depth — cannot adjust trace granularity per request","AIME 2024 benchmark is specific to competition mathematics — performance on other mathematical domains (statistics, applied math, numerical computation) is unknown","79.8% accuracy means ~20% of AIME problems still fail — not suitable for mission-critical mathematical verification without human review","No symbolic computation capability mentioned — cannot perform exact symbolic algebra or formal proof verification","Benchmark methodology and test set composition not documented — cannot assess whether performance generalizes to similar problems","Language support is only documented for Chinese and English — support for other languages is unknown","Reasoning quality may differ between languages — model may be better trained on English reasoning","builder identity is not verified yet","no observed match outcomes 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