{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-deepseek-deepseek-r1-0528","slug":"deepseek-deepseek-r1-0528","name":"DeepSeek: R1 0528","type":"model","url":"https://openrouter.ai/models/deepseek~deepseek-r1-0528","page_url":"https://unfragile.ai/deepseek-deepseek-r1-0528","categories":["chatbots-assistants"],"tags":["deepseek","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-deepseek-deepseek-r1-0528__cap_0","uri":"capability://planning.reasoning.chain.of.thought.reasoning.with.visible.inference.tokens","name":"chain-of-thought reasoning with visible inference tokens","description":"Implements a two-stage reasoning architecture where the model first generates explicit chain-of-thought reasoning tokens (visible to users and developers) before producing final answers. The reasoning phase uses reinforcement learning from human feedback (RLHF) to learn when and how to reason deeply, with a 671B parameter base model and 37B active parameters enabling efficient inference. This differs from o1-style hidden reasoning by exposing the full reasoning process, allowing developers to audit, debug, and understand model decision-making.","intents":["I need to see how the model arrived at its answer for debugging and trust purposes","I want to understand the reasoning process for complex problem-solving tasks","I need to extract intermediate reasoning steps for educational or transparency use cases","I want to verify the model's logic before trusting its output in production systems"],"best_for":["AI researchers studying reasoning behavior and RLHF training dynamics","Enterprise teams requiring explainability and auditability in high-stakes decisions","Developers building educational AI systems where reasoning transparency is critical","Teams implementing AI safety monitoring and red-teaming workflows"],"limitations":["Reasoning token generation increases latency by 2-5x compared to standard LLM inference","Visible reasoning tokens consume additional context window space, reducing available tokens for user input/output","Reasoning quality depends on problem complexity; simple queries may produce verbose reasoning without proportional benefit","No fine-tuning capability for custom reasoning patterns or domain-specific reasoning strategies"],"requires":["API access to DeepSeek R1 0528 via OpenRouter or compatible endpoint","Support for streaming or batch API calls to handle extended reasoning token sequences","Client-side token counting or parsing logic to separate reasoning tokens from final output"],"input_types":["text (natural language questions, problem statements, code snippets)","structured prompts with explicit reasoning instructions"],"output_types":["text (reasoning tokens + final answer)","structured reasoning traces (if parsed by client)"],"categories":["planning-reasoning","transparency-explainability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_1","uri":"capability://planning.reasoning.multi.domain.complex.problem.solving.with.mathematical.and.logical.reasoning","name":"multi-domain complex problem solving with mathematical and logical reasoning","description":"Leverages a 671B parameter architecture trained on diverse reasoning tasks to solve problems spanning mathematics, physics, logic puzzles, code debugging, and multi-step planning. The model uses reinforcement learning to develop robust reasoning strategies that generalize across domains, with active parameter selection (37B active) enabling efficient routing of computation to relevant reasoning pathways. Handles problems requiring 5-20+ step logical chains without degradation in coherence or correctness.","intents":["I need to solve complex math problems with step-by-step verification","I want to debug subtle logic errors in code or system design","I need to verify mathematical proofs or derive new conclusions from premises","I want to solve multi-constraint optimization or planning problems"],"best_for":["Research teams solving novel mathematical or algorithmic problems","Software engineers debugging complex system behavior or race conditions","Educational platforms requiring detailed problem-solving walkthroughs","Competitive programming or mathematics olympiad preparation tools"],"limitations":["Reasoning depth increases latency significantly; typical response time 10-30 seconds for complex problems","May over-reason on simple problems, producing verbose output without proportional accuracy gains","Performance degrades on problems requiring specialized domain knowledge not well-represented in training data","No ability to learn from user feedback within a session; each query starts fresh without context of previous corrections"],"requires":["API endpoint supporting extended timeout windows (30+ seconds)","Sufficient context window to accommodate both problem statement and full reasoning trace","Client implementation to parse and validate reasoning steps if verification is required"],"input_types":["text (mathematical problems, code snippets, logic puzzles, system design questions)","structured problem specifications with constraints and objectives"],"output_types":["text (step-by-step reasoning + final answer)","code (for debugging or implementation tasks)","mathematical notation or proofs"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_2","uri":"capability://tool.use.integration.api.based.inference.with.streaming.and.batch.processing","name":"api-based inference with streaming and batch processing","description":"Exposes the R1 0528 model through OpenRouter's REST API with support for both streaming (Server-Sent Events) and batch inference modes. Implements standard OpenAI-compatible chat completion endpoints with support for system prompts, temperature control, max tokens, and token counting. Streaming mode enables real-time reasoning token delivery as they're generated, while batch mode optimizes throughput for non-latency-sensitive workloads.","intents":["I want to integrate R1 reasoning into my application without managing model infrastructure","I need real-time streaming of reasoning tokens for interactive user experiences","I want to process bulk reasoning tasks efficiently without paying per-token streaming overhead","I need to monitor token usage and costs across multiple API calls"],"best_for":["Startups and teams without ML infrastructure expertise","Applications requiring real-time reasoning feedback (educational tools, interactive debugging)","Batch processing pipelines analyzing large document sets or problem collections","Teams using multi-model inference with provider abstraction layers"],"limitations":["API latency adds 500ms-2s overhead per request due to network round-trips and queueing","Streaming mode requires persistent HTTP connections; incompatible with some corporate proxies or serverless environments","Rate limiting and quota management required; no built-in backoff or retry logic in base API","Cost per token is higher than self-hosted inference; reasoning tokens may incur premium pricing"],"requires":["OpenRouter API key (free tier available with limited quota)","HTTP/1.1 or HTTP/2 client library supporting streaming (e.g., fetch, requests, httpx)","Understanding of OpenAI chat completion API format for request/response mapping"],"input_types":["JSON (OpenAI-compatible chat completion request format)","text (user messages, system prompts)"],"output_types":["JSON (chat completion response with usage metadata)","Server-Sent Events stream (for streaming mode)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_3","uri":"capability://planning.reasoning.open.source.model.weights.with.reproducible.inference","name":"open-source model weights with reproducible inference","description":"Unlike proprietary o1, DeepSeek R1 0528 is open-sourced with publicly available model weights, enabling developers to run inference locally, fine-tune on custom datasets, or audit the model architecture. The 671B parameter model with 37B active parameters can be deployed on high-end GPUs (8x H100s or equivalent) or quantized for smaller hardware. Supports standard inference frameworks (vLLM, TensorRT-LLM, Ollama) with reproducible outputs given fixed random seeds.","intents":["I want to run R1 reasoning locally without API dependencies or latency","I need to fine-tune the model on proprietary data while maintaining reasoning capabilities","I want to audit the model weights and architecture for safety or bias concerns","I need to deploy R1 in air-gapped or regulated environments without external API calls"],"best_for":["Organizations with strict data privacy requirements (healthcare, finance, government)","Research teams studying reasoning mechanisms and RLHF training dynamics","Teams with sufficient GPU infrastructure (8+ H100s or equivalent) for efficient deployment","Companies building proprietary reasoning systems via fine-tuning on domain data"],"limitations":["Requires 1.3TB+ VRAM for full precision inference; quantization to 8-bit reduces to ~350GB but impacts reasoning quality","Deployment and optimization complexity significantly higher than API access; requires expertise in CUDA, vLLM, or TensorRT","Fine-tuning requires substantial compute resources and expertise in RLHF; no official fine-tuning recipes provided","No official support or SLA; community support only via GitHub issues and forums"],"requires":["GPU cluster with 8x H100 (80GB) or equivalent (e.g., 16x A100 80GB)","vLLM 0.4.0+ or TensorRT-LLM 0.10.0+ for optimized inference","PyTorch 2.0+ and CUDA 12.1+ for model loading and inference","Model weights downloaded from Hugging Face (requires ~1.3TB disk space)"],"input_types":["text (natural language prompts, code, problems)","structured datasets (for fine-tuning)"],"output_types":["text (reasoning tokens + final answer)","fine-tuned model weights (if training performed)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_4","uri":"capability://code.generation.editing.code.generation.and.debugging.with.reasoning.guided.analysis","name":"code generation and debugging with reasoning-guided analysis","description":"Applies chain-of-thought reasoning to code generation and debugging tasks, producing not just code but explicit reasoning about correctness, edge cases, and potential bugs. The model reasons through algorithm selection, data structure choices, and error handling before generating code, enabling detection of subtle logic errors that standard code generation misses. Supports multiple programming languages and can reason about system-level concerns like concurrency, memory safety, and performance.","intents":["I need to generate correct code for complex algorithms with explanation of design choices","I want to debug subtle race conditions or memory safety issues in existing code","I need to understand why a piece of code is failing and get a fix with reasoning","I want to review code for correctness and security vulnerabilities with detailed analysis"],"best_for":["Senior engineers debugging complex systems or performance issues","Teams implementing safety-critical code (embedded systems, financial software)","Educational contexts where understanding algorithm correctness is important","Code review automation requiring detailed reasoning about correctness"],"limitations":["Reasoning overhead makes response time 5-10x slower than standard code generation models","Generated code may be over-engineered for simple tasks due to extensive reasoning about edge cases","Reasoning quality depends on problem clarity; ambiguous requirements produce verbose reasoning without clear resolution","No IDE integration or real-time code completion; designed for batch code generation and review"],"requires":["API access to DeepSeek R1 0528","Code context (existing codebase snippets) for debugging tasks","Clear problem specification or code snippet to analyze"],"input_types":["text (natural language problem description, algorithm specification)","code (existing code to debug or review, test cases)"],"output_types":["code (generated or fixed implementation)","text (reasoning about correctness, edge cases, design choices)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_5","uri":"capability://planning.reasoning.mathematical.proof.verification.and.derivation","name":"mathematical proof verification and derivation","description":"Uses chain-of-thought reasoning to verify mathematical proofs step-by-step, identify logical gaps, and derive new conclusions from premises. The model can work with formal notation, symbolic reasoning, and multi-step logical chains, producing intermediate steps that can be checked for correctness. Supports both proof verification (checking existing proofs) and proof generation (deriving new results from axioms and lemmas).","intents":["I need to verify that a mathematical proof is correct and identify any logical gaps","I want to derive a proof for a mathematical theorem from first principles","I need to understand the reasoning behind a complex proof step-by-step","I want to find counterexamples or identify assumptions in a proof"],"best_for":["Mathematics researchers and academics verifying proofs","Students learning proof techniques and mathematical reasoning","Automated theorem proving systems requiring reasoning-guided search","Mathematical software (Lean, Coq) integration for proof assistance"],"limitations":["Reasoning depth required for complex proofs may exceed practical latency budgets (30+ seconds)","Model may struggle with highly specialized mathematical domains not well-represented in training data","No formal verification; reasoning steps are plausible but not machine-checkable without external proof assistants","Cannot handle proofs requiring external computational verification (e.g., checking large combinatorial cases)"],"requires":["API access to DeepSeek R1 0528","Mathematical notation support in client (LaTeX rendering optional but helpful)","Clear statement of theorem or proof to verify"],"input_types":["text (mathematical theorem statements, proof sketches, axioms)","mathematical notation (LaTeX, symbolic expressions)"],"output_types":["text (step-by-step proof reasoning, verification results)","mathematical notation (derived expressions, counterexamples)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_6","uri":"capability://planning.reasoning.multi.turn.reasoning.with.context.preservation","name":"multi-turn reasoning with context preservation","description":"Maintains reasoning context across multiple turns in a conversation, enabling the model to build on previous reasoning steps and refine conclusions iteratively. Each turn generates new reasoning tokens that reference and build upon prior analysis, allowing developers to guide the reasoning process through follow-up questions and corrections. The model can revise earlier conclusions if new information contradicts prior reasoning.","intents":["I want to iteratively refine a solution by asking follow-up questions based on the model's reasoning","I need to correct the model's reasoning and have it adjust subsequent analysis","I want to explore alternative reasoning paths by asking 'what if' questions","I need to build complex solutions incrementally with reasoning validation at each step"],"best_for":["Interactive debugging sessions where reasoning is refined through dialogue","Educational tutoring systems where students can question reasoning steps","Collaborative problem-solving where human and AI reasoning interleave","Iterative design processes requiring reasoning validation at each stage"],"limitations":["Context window fills quickly with reasoning tokens; typical conversation depth 5-10 turns before context exhaustion","Model may become inconsistent if contradictory information is introduced; no explicit conflict resolution","Reasoning tokens accumulate costs; multi-turn conversations can be 5-10x more expensive than single-turn","No explicit memory of reasoning patterns across sessions; each new conversation starts fresh"],"requires":["API supporting multi-turn chat with message history","Client-side context management to track conversation state and token usage","Sufficient context window (8k+ tokens) to accommodate reasoning + history"],"input_types":["text (user messages, follow-up questions, corrections)"],"output_types":["text (reasoning tokens + response, updated conclusions)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-deepseek-deepseek-r1-0528__cap_7","uri":"capability://automation.workflow.cost.optimized.inference.with.sparse.activation","name":"cost-optimized inference with sparse activation","description":"Implements mixture-of-experts or sparse activation patterns where only 37B of the 671B parameters are active per inference step, reducing computational cost and latency compared to dense 671B models while maintaining reasoning quality. The sparse routing mechanism learns which parameter subsets are relevant for different problem types, enabling efficient allocation of compute. This architecture enables deployment on smaller GPU clusters than would be required for dense models of equivalent quality.","intents":["I want o1-level reasoning quality at lower cost than dense 671B models","I need to deploy reasoning models on limited GPU infrastructure","I want to reduce inference latency while maintaining reasoning depth","I need to optimize token costs for high-volume reasoning workloads"],"best_for":["Cost-sensitive teams running high-volume reasoning workloads","Organizations with limited GPU infrastructure (4-8 H100s rather than 16+)","Applications requiring sub-10-second latency for reasoning tasks","Batch processing pipelines where cost per token is critical"],"limitations":["Sparse activation may reduce reasoning quality on problems requiring broad parameter coverage","Routing overhead adds latency; actual speedup depends on sparsity level and hardware optimization","Quantization of sparse models is less studied; 8-bit quantization may degrade reasoning quality more than dense models","No public benchmarks comparing sparse vs dense reasoning quality on diverse problem types"],"requires":["Inference framework supporting sparse activation (vLLM with MoE support, TensorRT-LLM)","GPU hardware with good sparse tensor support (H100, A100 with recent CUDA versions)"],"input_types":["text (any reasoning task)"],"output_types":["text (reasoning tokens + answer)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["API access to DeepSeek R1 0528 via OpenRouter or compatible endpoint","Support for streaming or batch API calls to handle extended reasoning token sequences","Client-side token counting or parsing logic to separate reasoning tokens from final output","API endpoint supporting extended timeout windows (30+ seconds)","Sufficient context window to accommodate both problem statement and full reasoning trace","Client implementation to parse and validate reasoning steps if verification is required","OpenRouter API key (free tier available with limited quota)","HTTP/1.1 or HTTP/2 client library supporting streaming (e.g., fetch, requests, httpx)","Understanding of OpenAI chat completion API format for request/response mapping","GPU cluster with 8x H100 (80GB) or equivalent (e.g., 16x A100 80GB)"],"failure_modes":["Reasoning token generation increases latency by 2-5x compared to standard LLM inference","Visible reasoning tokens consume additional context window space, reducing available tokens for user input/output","Reasoning quality depends on problem complexity; simple queries may produce verbose reasoning without proportional benefit","No fine-tuning capability for custom reasoning patterns or domain-specific reasoning strategies","Reasoning depth increases latency significantly; typical response time 10-30 seconds for complex problems","May over-reason on simple problems, producing verbose output without proportional accuracy gains","Performance degrades on problems requiring specialized domain knowledge not well-represented in training data","No ability to learn from user feedback within a session; each query starts fresh without context of previous corrections","API latency adds 500ms-2s overhead per request due to network round-trips and queueing","Streaming mode requires persistent HTTP connections; incompatible with some corporate proxies or serverless environments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=deepseek-deepseek-r1-0528","compare_url":"https://unfragile.ai/compare?artifact=deepseek-deepseek-r1-0528"}},"signature":"Norl/zatiSsjSjH6Ismzjt7d8yYYI4LvpdK+d1s3R4XGyVfeuF2WD+GdgnqmCsH1mioDDJPqF2CPw4qzdcG/BA==","signedAt":"2026-06-21T18:32:05.322Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-deepseek-r1-0528","artifact":"https://unfragile.ai/deepseek-deepseek-r1-0528","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-deepseek-r1-0528","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"}}