DeepSeek Reasoner vs DeepSeek-R1-0528
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek Reasoner de DeepSeek y DeepSeek-R1-0528 de DeepSeek compiten directamente en el ecosistema actual de IA. DeepSeek Reasoner ofrece una ventana de contexto de 1M tokens a $0.28/$0.42 por millón de tokens (entrada/salida), mientras que DeepSeek-R1-0528 ofrece 128K tokens de contexto a $0.55/$2.19 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.
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DeepSeek Reasoner
Razonamiento de Pesos AbiertosDeepSeek Reasoner de DeepSeek es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 1M, velocidad de 52 tokens/s, índice de inteligencia de 54/100
$0.28 / 1M
$0.42 / 1M
1M tokens
56.5%
DeepSeek-R1-0528
Razonamiento de Pesos AbiertosDeepSeek-R1-0528 de DeepSeek es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 128K, velocidad de 48 tokens/s, índice de inteligencia de 52/10
$0.55 / 1M
$2.19 / 1M
128K tokens
51.8%
Diferencias Clave de un Vistazo
- DeepSeek Reasoner is 3.6x cheaper for API usage (input + output tokens combined).
- DeepSeek Reasoner has a larger context window (1M vs 128K tokens).
- DeepSeek Reasoner is faster at 52 tok/s vs 48 tok/s.
- DeepSeek Reasoner leads on SWE-bench coding (56.5% vs 51.8%).
| Característica | DeepSeek Reasoner | DeepSeek-R1-0528 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 52 |
Provider | DeepSeek | DeepSeek |
Input Price Cost per million input tokens | $0.28 / 1M | $0.55 / 1M |
Output Price Cost per million output tokens | $0.42 / 1M | $2.19 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.12 / 1M | $0.43 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 33K | 8K |
Throughput Speed Tokens generated per second | 52 tok/s | 48 tok/s |
SWE-bench Verified Real-world software engineering | 56.5% | 51.8% |
GPQA Diamond Graduate-level reasoning | 76.5% | 74.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.14 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
DeepSeek Reasoner · Strengths & Key Advantages
- Ventana de contexto de 1M con hasta 33K tokens de salida máxima (SWE-bench Verified 56.5% · GPQA Diamond 76.5%)
- Rendimiento ágil de 52 tokens/segundo con latencia inicial (TTFT) de solo 820 ms
- Pesos abiertos (671B Total (37B )) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
DeepSeek-R1-0528 · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 51.8% · GPQA Diamond 74.2%)
- Rendimiento ágil de 48 tokens/segundo con latencia inicial (TTFT) de solo 900 ms
- Pesos abiertos (671B (37B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek Reasoner | DeepSeek-R1-0528 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $0.62 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 26K | 25K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | DeepSeek Reasoner | DeepSeek-R1-0528 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-12 | 2025-05 |
| Knowledge Cutoff | September 2025 | March 2025 |
| Architecture | Reinforcement-Learned Reasoning MoE | Open-Weights Reasoning MoE |
| Parameters | 671B Total (37B Active) | 671B (37B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.28/1M in) across full 1000K window | Flat rate ($0.55/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.028/1M (no extra cache-write fee) | Automatic prefix caching at $0.14/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
DeepSeek Reasoner vs DeepSeek-R1-0528: Respuestas Rápidas (FAQ)
Which is more intelligent, DeepSeek Reasoner or DeepSeek-R1-0528?
DeepSeek Reasoner is more intelligent. It scores 54 compared with 52 on the composite Intelligence Index.
Which model is faster?
DeepSeek Reasoner is faster, generating 52 tokens per second compared with 48 tokens per second.
Which model is cheaper to run?
DeepSeek Reasoner is cheaper at $0.12 per 1M tokens blended, compared with $0.43 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek Reasoner has lower latency, with a time to first token of 0.82s compared with 0.90s.
Which model has a larger context window?
DeepSeek Reasoner has a larger context window, supporting 1M tokens compared with 128K tokens.
Veredicto
Tanto DeepSeek Reasoner (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) como DeepSeek-R1-0528 (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) destacan en producción: DeepSeek Reasoner lidera en inteligencia general y programación, DeepSeek Reasoner ofrece mayor velocidad de generación de tokens y DeepSeek Reasoner resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, DeepSeek Reasoner o DeepSeek-R1-0528?+
En las pruebas de inteligencia compuesta y resolución de código, DeepSeek Reasoner obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.
¿Cuánto cuesta la API de DeepSeek Reasoner frente a DeepSeek-R1-0528?+
DeepSeek Reasoner cuesta $0.28 / $0.42 por millón de tokens de entrada/salida, mientras que DeepSeek-R1-0528 cuesta $0.55 / $2.19. En costo combinado, DeepSeek Reasoner es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
DeepSeek Reasoner cuenta con 1M tokens de contexto frente a 128K tokens de DeepSeek-R1-0528. En velocidad de inferencia, DeepSeek Reasoner ofrece un rendimiento superior.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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: Documentación oficial de API de DeepSeek y DeepSeek, tarjetas de sistema y benchmarks independientes (2026).