Innovador modelo de razonamiento de pesos abiertos entrenado con aprendizaje por refuerzo un gran escala, igualando un gigantes propietarios por una fracción del costo.
Technical Specifications
- Provider
- DeepSeek
- Category
- reasoning
- Release Date
- 2025-01
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- deepseek-reasoner
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.55/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Automatic prefix caching at $0.14/1M (no extra cache-write fee)
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
DeepSeek R1 Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~187 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to DeepSeek R1:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 49.2%
- GPQA Diamond
- 71.5%
- MMLU-Pro
- 84%
- HumanEval
- 91%
- Humanity's Last Exam
- 11.4%
- Terminal-Bench
- 32.5%
- SciCode
- 29.5%
- LiveCodeBench
- 51.7%
- AIME
- 82.2%
- Long-Context Reasoning
- 75.6%
- Chatbot Arena Elo
- 1340pts
Strengths
- Phenomenal matemáticas y lógica benchmark results (71.5% en GPQA Diamond)
- Extremely económico abierto API precios ($0.55 en / $2.19 out)
- Permissive de pesos abiertos license para alojamiento propio en local clústeres
- Visible cadena de pensamiento internal razonamiento process
Limitations & Trade-offs
- Slower latencia due un extendido internal razonamiento generación
- texto-solo (no nativo imagen o audio modality)
Editorial Assessment
El principal modelo de razonamiento de pesos abiertos que rompió las barreras de precios para matemáticas y lógica de nivel de doctorado.
Best for: competitivo codificación, matemático investigación, y privacy-primer en-premise implementaciones
Compare DeepSeek R1
Frequently Asked Questions about DeepSeek R1
How much does the DeepSeek R1 API cost per 1M tokens?+
DeepSeek R1 by DeepSeek is priced at $0.55 per 1M input tokens and $2.19 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.14 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the DeepSeek R1 pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in DeepSeek R1's context window?+
DeepSeek R1 features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run DeepSeek R1 locally on my own GPU (Open Weights)?+
Yes, DeepSeek R1 (671B Total (37B Active)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does DeepSeek train on data sent to the DeepSeek R1 API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does DeepSeek R1 perform on coding and reasoning benchmarks?+
DeepSeek R1 achieves a composite Intelligence Index of 49/100, scoring 49.2% on SWE-bench Verified, 71.5% on GPQA Diamond, 84% on MMLU-Pro, and 1340 pts on Chatbot Arena Elo.
What is DeepSeek R1 best used for?+
DeepSeek R1 is best suited for: competitivo codificación, matemático investigación, y privacy-primer en-premise implementaciones. Assessment: El principal modelo de razonamiento de pesos abiertos que rompió las barreras de precios para matemáticas y lógica de nivel de doctorado.
DeepSeek Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to DeepSeek's flagship reasoning tier:
DeepSeek V4.1-Flash
DeepSeek V4 Pro
DeepSeek V4
DeepSeek Reasoner
DeepSeek Chat
DeepSeek-R1-0528
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to DeepSeek R1:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
Un modelo de IA entrenado para invertir cómputo adicional en pensar y validar hipótesis lógicas antes de responder, diseñado para problemas complejos de ingeniería y matemáticas.
Una técnica de razonamiento paso a paso donde el modelo genera pensamientos intermedios antes de formular la respuesta final, mejorando radicalmente la resolución de problemas matemáticos y lógicos.
La capacidad de un modelo de detectar la necesidad de una función externa y emitir argumentos JSON estructurados para ejecutar API, búsquedas web o consultas a bases de datos.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: DeepSeek Official Platform Documentation & Pricing↗