{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"deepseek-api","slug":"deepseek-api","name":"DeepSeek API","type":"api","url":"https://platform.deepseek.com","page_url":"https://unfragile.ai/deepseek-api","categories":["llm-apis","deployment-infra"],"tags":[],"pricing":{"model":"usage","free":false,"starting_price":"$0.07/1M tokens"},"status":"active","verified":false},"capabilities":[{"id":"deepseek-api__cap_0","uri":"capability://text.generation.language.openai.compatible.api.endpoint.for.llm.inference","name":"openai-compatible api endpoint for llm inference","description":"Provides drop-in compatible REST API endpoints matching OpenAI's chat completion and embedding interfaces, allowing existing OpenAI client libraries (Python, Node.js, Go, etc.) to route requests to DeepSeek models without code changes. Implements request/response schema parity with OpenAI's API including streaming, function calling, and token counting, enabling zero-friction migration from OpenAI to DeepSeek infrastructure.","intents":["Migrate existing OpenAI-dependent applications to DeepSeek without refactoring client code","Evaluate DeepSeek models as a cost-effective alternative to OpenAI by swapping API endpoints","Build multi-model applications that can dynamically route between OpenAI and DeepSeek based on cost or latency requirements","Use existing OpenAI SDKs and tooling (LangChain, LlamaIndex, Vercel AI SDK) with DeepSeek backend"],"best_for":["Teams with existing OpenAI integrations seeking cost optimization","Developers building cost-sensitive production applications","Organizations evaluating multi-provider LLM strategies"],"limitations":["API compatibility is schema-level only; some OpenAI-specific features (e.g., fine-tuning endpoints, organization management) may not be fully supported","Rate limits and quota management differ from OpenAI; requires separate monitoring and adjustment","Latency characteristics and model behavior differ; applications optimized for OpenAI's response patterns may need tuning"],"requires":["API key from DeepSeek platform (https://platform.deepseek.com)","OpenAI SDK (Python 1.0+, Node.js 4.0+, or equivalent) or raw HTTP client","Network access to platform.deepseek.com"],"input_types":["text (chat messages in OpenAI format)","structured JSON (function definitions for tool calling)"],"output_types":["text (streaming or non-streaming completion)","structured JSON (function call arguments)","token count metadata"],"categories":["text-generation-language","api-compatibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_1","uri":"capability://text.generation.language.reasoning.focused.model.inference.deepseek.r1","name":"reasoning-focused model inference (deepseek-r1)","description":"Exposes DeepSeek-R1, a reasoning-specialized model that performs explicit chain-of-thought computation before generating responses, using an internal reasoning token budget to decompose complex problems. The API returns both the reasoning trace (via special tokens or metadata) and the final answer, enabling applications to inspect the model's problem-solving process and validate correctness for high-stakes tasks.","intents":["Solve complex multi-step math, logic, and algorithmic problems with verifiable reasoning chains","Debug model outputs by examining the reasoning process that led to incorrect answers","Build applications requiring transparent decision-making (e.g., medical diagnosis support, legal analysis)","Evaluate model reasoning quality for research or benchmarking purposes"],"best_for":["Researchers and ML engineers evaluating reasoning capabilities","Teams building high-stakes applications (finance, healthcare, legal) requiring explainability","Developers optimizing for accuracy over latency on complex reasoning tasks"],"limitations":["Reasoning models incur higher latency (5-30s typical) and token costs due to internal reasoning computation; not suitable for real-time applications","Reasoning trace format and accessibility varies by model version; parsing reasoning output requires custom logic","Reasoning budget is finite; very complex problems may exhaust reasoning tokens before reaching a conclusion","Reasoning quality is task-dependent; performance gains over non-reasoning models vary significantly by problem domain"],"requires":["API key with access to DeepSeek-R1 model variant","Acceptance of higher per-request latency (5-30 seconds typical)","Handling of extended response times in application timeout configurations"],"input_types":["text (natural language problem statements)","structured prompts with step-by-step instructions"],"output_types":["text (final answer)","reasoning trace (internal chain-of-thought, format varies)","token usage metadata (reasoning tokens vs output tokens)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_10","uri":"capability://memory.knowledge.context.window.management.with.dynamic.prompt.optimization","name":"context window management with dynamic prompt optimization","description":"Supports variable context windows (4K, 8K, 32K, 128K tokens depending on model) allowing applications to include more or less context based on requirements. The API accepts full conversation history and context, and applications can implement dynamic optimization strategies (summarization, retrieval-augmented generation, or sliding window) to stay within context limits while preserving relevant information.","intents":["Build long-context applications that maintain awareness of extended conversation history","Implement RAG pipelines that inject relevant documents into context dynamically","Create applications that summarize old conversation turns to preserve context within token limits","Optimize context usage by selecting only relevant information from large knowledge bases"],"best_for":["Teams building RAG and knowledge-augmented applications","Developers implementing long-context conversational AI","Organizations processing long documents or extended conversations"],"limitations":["Larger context windows increase latency and cost proportionally; 128K context requests may be 10-20x more expensive than 4K","Model quality may degrade with very long contexts; information in the middle of long contexts is sometimes ignored (lost-in-the-middle effect)","Context window limits are model-specific; applications must handle different limits for different models","No automatic context optimization; applications must implement summarization or retrieval logic"],"requires":["API key","Understanding of context window limits for chosen model","Optional: RAG framework (LangChain, LlamaIndex) or custom context management logic"],"input_types":["text (prompts with variable context length)"],"output_types":["text (responses)","metadata (token usage including context tokens)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_11","uri":"capability://tool.use.integration.model.version.management.and.deprecation.handling","name":"model version management and deprecation handling","description":"Provides versioned API endpoints and model identifiers (e.g., deepseek-chat, deepseek-coder, deepseek-r1) with clear deprecation timelines, allowing applications to pin specific model versions and migrate gradually to newer versions. The API maintains backward compatibility for deprecated models during transition periods, and provides migration guides and performance comparisons to help teams evaluate upgrades.","intents":["Pin production applications to specific model versions for stability and reproducibility","Evaluate new model versions in staging environments before production rollout","Plan model upgrades with clear deprecation timelines and migration paths","Compare performance across model versions to justify upgrade decisions"],"best_for":["Production teams requiring model stability and reproducibility","Organizations with strict change management processes","Teams evaluating model upgrades and performance improvements"],"limitations":["Maintaining multiple model versions increases API complexity and support burden","Deprecated models are eventually removed; applications must migrate before sunset dates","Model version differences may require prompt tuning; behavior is not guaranteed to be identical across versions","No automatic version selection; applications must explicitly specify model versions"],"requires":["API key","Explicit model version specification in requests (e.g., 'deepseek-chat-v3')","Monitoring of deprecation announcements and migration timelines"],"input_types":["text (same as other endpoints)"],"output_types":["text (responses from specified model version)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_2","uri":"capability://code.generation.editing.code.generation.and.completion.with.multi.language.support","name":"code generation and completion with multi-language support","description":"Provides specialized code generation capabilities across 40+ programming languages (Python, JavaScript, Go, Rust, Java, C++, etc.) using DeepSeek-V3's training on diverse code repositories. The API accepts partial code, docstrings, or natural language descriptions and generates syntactically valid, contextually appropriate code completions. Supports both single-line completions and full function/class generation with awareness of language-specific idioms and frameworks.","intents":["Generate boilerplate code and scaffolding for new projects or modules","Complete partial implementations based on function signatures or docstrings","Translate code between languages while preserving logic and idioms","Generate test cases and fixtures from source code or specifications"],"best_for":["Full-stack developers accelerating routine coding tasks","Teams building polyglot systems requiring code generation across multiple languages","Developers learning new languages or frameworks by generating example code"],"limitations":["Generated code quality varies by language; less common languages (Elixir, Clojure) produce lower-quality completions than Python/JavaScript","No built-in static analysis or type checking; generated code may have subtle bugs or type errors requiring manual review","Context window limits (typically 4K-32K tokens) restrict ability to generate code aware of large existing codebases","No direct IDE integration via this API; requires building custom editor plugins or using third-party integrations"],"requires":["API key with code generation model access","Code context (partial implementation, docstring, or natural language prompt)","Language specification in request (or inference from context)"],"input_types":["text (partial code, docstrings, natural language descriptions)","code (existing implementation to complete or refactor)"],"output_types":["code (generated completions in specified language)","text (explanations or comments)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_3","uri":"capability://text.generation.language.streaming.response.delivery.with.token.level.granularity","name":"streaming response delivery with token-level granularity","description":"Implements server-sent events (SSE) based streaming that delivers model outputs token-by-token in real-time, allowing clients to display partial results as they arrive rather than waiting for full completion. The API returns structured JSON events containing individual tokens, token probabilities, and cumulative token counts, enabling applications to implement progressive UI updates, early stopping, or dynamic prompt adjustment based on partial outputs.","intents":["Build responsive chat interfaces that display model output as it generates","Implement early stopping logic that terminates generation when confidence drops below threshold","Create real-time dashboards or monitoring tools that track token generation rates","Reduce perceived latency in user-facing applications by showing partial results immediately"],"best_for":["Web and mobile applications requiring real-time user feedback","Chat applications and conversational interfaces","Teams building low-latency user experiences"],"limitations":["Streaming adds complexity to error handling; connection drops mid-stream require client-side recovery logic","Token-level streaming increases network overhead compared to single-request completion; not ideal for batch processing","Streaming responses cannot be retried atomically; partial results must be managed by application logic","Some client libraries (older versions) have limited streaming support; requires modern HTTP/2 or WebSocket support"],"requires":["HTTP client with streaming support (fetch API, axios, requests library with stream=True, etc.)","Handling of Server-Sent Events (SSE) format","Timeout and error recovery logic for long-running streams"],"input_types":["text (chat messages, prompts)"],"output_types":["streaming JSON events (token, token_id, logprobs, cumulative_tokens)","text (reconstructed from token stream)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_4","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.binding","name":"function calling with schema-based tool binding","description":"Implements OpenAI-compatible function calling that allows models to request execution of external tools by generating structured JSON function calls matching predefined schemas. The API accepts a list of function definitions (name, description, parameters as JSON schema) and returns function call requests when the model determines a tool is needed, enabling agentic workflows where the model orchestrates multi-step tasks by calling external APIs, databases, or services.","intents":["Build autonomous agents that can call APIs, databases, or services to accomplish multi-step tasks","Create structured data extraction pipelines where the model calls specialized extraction functions","Implement tool-augmented reasoning where the model decides which tools to use based on task requirements","Develop chatbots that can take actions (send emails, create calendar events, query databases) based on user requests"],"best_for":["Teams building AI agents and autonomous workflows","Developers creating tool-augmented LLM applications","Organizations implementing structured data extraction at scale"],"limitations":["Function calling quality depends on schema clarity; poorly documented schemas lead to incorrect function calls","No built-in validation of function arguments against schema; applications must implement validation before execution","Model may hallucinate function names or parameters not in the schema; requires strict output parsing and error handling","Parallel function calling (multiple simultaneous calls) has limited support; sequential calling is more reliable"],"requires":["API key with function calling support","Function definitions in JSON schema format (OpenAI-compatible format)","Application logic to execute functions and return results to the model"],"input_types":["text (user prompts, instructions)","structured JSON (function definitions with schemas)"],"output_types":["structured JSON (function call requests with arguments)","text (model responses after function execution)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_5","uri":"capability://automation.workflow.batch.processing.api.for.cost.optimized.inference","name":"batch processing api for cost-optimized inference","description":"Provides a batch processing endpoint that accepts multiple requests in JSONL format and processes them asynchronously at reduced rates (typically 50% discount vs on-demand pricing). The API queues batch jobs, processes them during off-peak hours, and returns results via webhook or polling, enabling cost-effective processing of large volumes of inference requests without real-time latency requirements.","intents":["Process large datasets through the model at reduced cost (e.g., classifying 1M documents, generating embeddings for a corpus)","Run nightly batch jobs for content generation, summarization, or analysis","Evaluate model performance on benchmarks or datasets without incurring full on-demand costs","Build cost-optimized data pipelines that tolerate 1-24 hour latency"],"best_for":["Data teams processing large datasets","Organizations with non-real-time inference workloads","Cost-sensitive applications willing to trade latency for savings"],"limitations":["Batch processing introduces 1-24 hour latency; not suitable for real-time applications","Batch jobs are queued and processed in order; no priority or expedited processing available","Failed requests in a batch require resubmission of the entire batch; no granular retry logic","Batch API has lower throughput guarantees than on-demand; suitable for background processing only"],"requires":["API key with batch processing access","Requests formatted as JSONL (one JSON object per line)","Webhook endpoint or polling mechanism to retrieve results","Tolerance for 1-24 hour processing latency"],"input_types":["JSONL (newline-delimited JSON, each line is a complete request)"],"output_types":["JSONL (results in same format as input, with responses appended)","webhook notifications (optional)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_6","uri":"capability://tool.use.integration.token.counting.and.cost.estimation.before.execution","name":"token counting and cost estimation before execution","description":"Provides a dedicated token counting endpoint that accepts prompts and returns exact token counts for input and estimated output tokens, allowing applications to calculate costs before making requests. The endpoint uses the same tokenizer as the inference engine, ensuring accuracy for cost estimation and quota management. Supports counting tokens for chat messages, function definitions, and system prompts with language-specific tokenization rules.","intents":["Estimate API costs before submitting requests to avoid unexpected charges","Implement quota management and rate limiting based on token consumption","Optimize prompts by measuring token impact of different phrasings or context lengths","Build cost-aware applications that reject requests exceeding token budgets"],"best_for":["Teams managing API budgets and cost controls","Applications with strict cost constraints or quota limits","Developers optimizing prompt efficiency"],"limitations":["Token counting is synchronous and adds latency to request preparation; not suitable for real-time request generation","Output token estimation is approximate; actual output may vary based on model behavior","Tokenization rules may differ slightly between model versions; requires re-counting when upgrading models","Token counting endpoint has separate rate limits; high-volume counting may require batching"],"requires":["API key with token counting access","Prompts or messages to count (in OpenAI chat format or raw text)"],"input_types":["text (raw prompts)","structured JSON (chat messages in OpenAI format)"],"output_types":["JSON (input_tokens, estimated_output_tokens, total_estimated_tokens)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_7","uri":"capability://text.generation.language.multi.turn.conversation.state.management.with.context.preservation","name":"multi-turn conversation state management with context preservation","description":"Implements stateless conversation handling where clients manage conversation history by including full message arrays in each request, with the API maintaining no server-side session state. The API accepts a messages array (system, user, assistant messages in chronological order) and generates the next response while preserving context from previous turns. Supports conversation branching, message editing, and context window management through client-side logic.","intents":["Build multi-turn chatbots and conversational interfaces with full conversation history","Implement conversation branching where users can explore alternative response paths","Create applications that edit or regenerate previous messages in a conversation","Manage conversation context across distributed systems without server-side session storage"],"best_for":["Web and mobile applications with stateless backend requirements","Teams building conversational AI without session management infrastructure","Applications requiring conversation portability (export/import, sharing)"],"limitations":["Full conversation history must be sent with each request, increasing token usage and latency as conversations grow","No server-side conversation persistence; applications must implement storage separately","Context window limits (4K-32K tokens) restrict conversation length; long conversations require summarization or pruning","No built-in conversation search or indexing; applications must implement these features"],"requires":["API key","Client-side conversation history storage","Handling of context window limits (truncation or summarization logic)"],"input_types":["structured JSON (messages array with role, content fields)"],"output_types":["text (assistant response)","structured JSON (full response object with metadata)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_8","uri":"capability://text.generation.language.self.hosted.model.deployment.with.open.source.variants","name":"self-hosted model deployment with open-source variants","description":"Provides open-source versions of DeepSeek models (e.g., DeepSeek-7B, DeepSeek-33B) available on Hugging Face that can be self-hosted on private infrastructure using standard frameworks (vLLM, Ollama, llama.cpp, etc.). Enables organizations to run DeepSeek models on-premises with full control over data, latency, and costs, while maintaining compatibility with the same prompting and function-calling patterns as the API.","intents":["Deploy DeepSeek models on private infrastructure for data privacy and compliance requirements","Run inference locally or on-premises to avoid cloud API dependencies and latency","Fine-tune open-source DeepSeek models on proprietary data without sending data to external APIs","Reduce inference costs by self-hosting on owned hardware"],"best_for":["Organizations with strict data privacy or compliance requirements (healthcare, finance, government)","Teams with existing GPU infrastructure seeking to maximize utilization","Developers building offline-capable applications"],"limitations":["Self-hosting requires GPU infrastructure (NVIDIA A100/H100 for production, consumer GPUs for development); significant capital investment","Operational overhead: model serving, scaling, monitoring, and updates are the responsibility of the organization","Smaller open-source variants (7B, 33B) have lower quality than API models (V3, R1); trade-off between capability and resource requirements","No automatic updates or security patches; organizations must manage model versioning and security independently"],"requires":["GPU hardware (NVIDIA A100/H100 recommended for production, RTX 4090/4080 for development)","Model serving framework (vLLM, Ollama, llama.cpp, TensorRT-LLM, etc.)","Kubernetes or container orchestration for production deployment (optional but recommended)","Python 3.9+ and CUDA 11.8+ for GPU support"],"input_types":["text (same as API)","structured JSON (function definitions, chat messages)"],"output_types":["text (model outputs)","streaming tokens (with vLLM or similar)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__cap_9","uri":"capability://data.processing.analysis.embedding.generation.for.semantic.search.and.similarity","name":"embedding generation for semantic search and similarity","description":"Provides a dedicated embedding endpoint that converts text into fixed-dimensional dense vectors (typically 1536 or 3072 dimensions) suitable for semantic search, clustering, and similarity comparison. The embeddings are trained on diverse text corpora and optimized for retrieval tasks, enabling applications to build vector databases, implement semantic search, or compute text similarity without training custom embedding models.","intents":["Build semantic search systems that find relevant documents based on meaning rather than keyword matching","Implement recommendation systems based on content similarity","Create vector databases for RAG (retrieval-augmented generation) applications","Compute similarity between texts for clustering, deduplication, or anomaly detection"],"best_for":["Teams building search and recommendation systems","Developers implementing RAG pipelines","Organizations building vector databases (Pinecone, Weaviate, Milvus, etc.)"],"limitations":["Embeddings are task-specific; embeddings trained for one domain may not transfer well to different domains","Embedding quality depends on text length and domain; very short texts or out-of-domain content produce lower-quality embeddings","Embeddings are not human-interpretable; debugging similarity issues requires manual inspection of similar texts","Vector database operations (indexing, search) add latency; not suitable for real-time single-query scenarios"],"requires":["API key with embedding access","Text input (strings or arrays of strings)","Vector database or similarity computation library (optional but recommended)"],"input_types":["text (single string or array of strings)"],"output_types":["JSON (array of embeddings, each a float array of 1536 or 3072 dimensions)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-api__headline","uri":"capability://llm.apis.openai.compatible.api.for.advanced.reasoning.and.coding.tasks","name":"openai-compatible api for advanced reasoning and coding tasks","description":"DeepSeek API offers powerful models like DeepSeek-V3 and DeepSeek-R1, known for their exceptional coding abilities and competitive pricing, making it an ideal choice for developers seeking an effective AI solution.","intents":["best AI coding API","AI reasoning API for developers","OpenAI-compatible APIs for coding","affordable AI models for self-hosting","API for advanced coding tasks"],"best_for":["developers seeking coding assistance","teams needing affordable AI solutions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["API key from DeepSeek platform (https://platform.deepseek.com)","OpenAI SDK (Python 1.0+, Node.js 4.0+, or equivalent) or raw HTTP client","Network access to platform.deepseek.com","API key with access to DeepSeek-R1 model variant","Acceptance of higher per-request latency (5-30 seconds typical)","Handling of extended response times in application timeout configurations","API key","Understanding of context window limits for chosen model","Optional: RAG framework (LangChain, LlamaIndex) or custom context management logic","Explicit model version specification in requests (e.g., 'deepseek-chat-v3')"],"failure_modes":["API compatibility is schema-level only; some OpenAI-specific features (e.g., fine-tuning endpoints, organization management) may not be fully supported","Rate limits and quota management differ from OpenAI; requires separate monitoring and adjustment","Latency characteristics and model behavior differ; applications optimized for OpenAI's response patterns may need tuning","Reasoning models incur higher latency (5-30s typical) and token costs due to internal reasoning computation; not suitable for real-time applications","Reasoning trace format and accessibility varies by model version; parsing reasoning output requires custom logic","Reasoning budget is finite; very complex problems may exhaust reasoning tokens before reaching a conclusion","Reasoning quality is task-dependent; performance gains over non-reasoning models vary significantly by problem domain","Larger context windows increase latency and cost proportionally; 128K context requests may be 10-20x more expensive than 4K","Model quality may degrade with very long contexts; information in the middle of long contexts is sometimes ignored (lost-in-the-middle effect)","Context window limits are model-specific; applications must handle different limits for different models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"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-api","compare_url":"https://unfragile.ai/compare?artifact=deepseek-api"}},"signature":"yJlHvOVZI6UY6nuOKa9RK10V20ga8svDo8LsmPxui2fpFenCUqfdhOv/V5OhdbnezmNq5EODv9+CwaOuNjUeBQ==","signedAt":"2026-06-19T17:28:50.672Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-api","artifact":"https://unfragile.ai/deepseek-api","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-api","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"}}