Head-to-Head Model Comparison

DeepSeek R1 vs OpenAI o1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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DeepSeek R1Profile

OpenAI o1Profile

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5-Model Matrix Workbench

Popular Matchups:

DeepSeek

DeepSeek R1

Razonamiento de Pesos Abiertos

DeepSeek R1 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 45 tokens/s, índice de inteligencia de 49/100 y t

Intelligence Index49 / 100

Input Token:

$0.55 / 1M

Output Token:

$2.19 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

OpenAI

OpenAI o1

Motor de Razonamiento Profundo

OpenAI o1 de OpenAI es un modelo de IA diseñado para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, con ventana de contexto de 200K, velocidad de 60 tokens/s, índice de inteligencia de 55/100 y tari

Intelligence Index55 / 100

Input Token:

$15.00 / 1M

Output Token:

$60.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

48.9%

Diferencias Clave de un Vistazo

DeepSeek R1 is 29.2x cheaper for API usage (input + output tokens combined).
OpenAI o1 has a larger context window (200K vs 128K tokens).
OpenAI o1 is faster at 60 tok/s vs 45 tok/s.
DeepSeek R1 leads on SWE-bench coding (49.2% vs 48.9%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	DeepSeek R1	OpenAI o1
Intelligence Index Composite reasoning & knowledge score (0-100)	49	55
Provider	DeepSeek	OpenAI
Input Price Cost per million input tokens	$0.55 / 1M	$15.00 / 1M
Output Price Cost per million output tokens	$2.19 / 1M	$60.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.43 / 1M	$14.25 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	200K tokens~300 A4 pages of text
Max Output Tokens Per single request	8K	100K
Throughput Speed Tokens generated per second	45 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	49.2%	48.9%
GPQA Diamond Graduate-level reasoning	71.5%	77.3%
Cached Input Price Prompt caching discount per 1M tokens	$0.14 / 1M	$7.50 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

DeepSeek R1

+0.3% SWE-bench lead

Costo

DeepSeek R1

29.2x cheaper monthly runtime

Velocidad

OpenAI o1

+33% faster generation

Contexto

OpenAI o1

200K tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

DeepSeek R1OpenAI o1

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

49.2%vs48.9%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

71.5%vs77.3%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84%vs89.6%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1340ptsvs1355pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

11.4%vs10.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

32.5%vs43.5%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

29.5%vs44.2%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

51.7%vs72.1%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

82.2%vs83.3%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

75.6%vs75%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs60 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

DeepSeek R10.95s

OpenAI o13.20s

Est. End-to-End Response (500 tokens)TTFT + generation time

DeepSeek R112.06s

OpenAI o111.53s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

DeepSeek R1 Monthly API Bill

$3.01 / month

$0.55/1M in · $2.19/1M out

OpenAI o1 Monthly API Bill

$87.75 / month

$15/1M in · $60/1M out

DeepSeek R1 saves approximately $84.74 / month (97%) under this workload.29.2x Savings

DeepSeek R1 · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 49.2% · GPQA Diamond 71.5%)
Rendimiento ágil de 45 tokens/segundo con latencia inicial (TTFT) de solo 950 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

OpenAI o1 · Strengths & Key Advantages

Ventana de contexto de 200K con hasta 100K tokens de salida máxima (SWE-bench Verified 48.9% · GPQA Diamond 77.3%)
Rendimiento ágil de 60 tokens/segundo con latencia inicial (TTFT) de solo 3200 ms
Economía de API competitiva ($15/$60 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	DeepSeek R1	OpenAI o1
Cost per Task Estimated USD to finish one task	$0.65	$0.20
Output Tokens / Task Tokens generated per task	45K	24K
Reasoning Tokens / Task Thinking tokens burned per task	25K	15K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	DeepSeek R1	OpenAI o1
Official API Model ID (SDK)	deepseek-reasoner	o1
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2025-01	2024-12
Knowledge Cutoff	July 2024	October 2023
Architecture	Open-Weights Reasoning MoE	Chain-of-Thought Reasoning Foundation
Parameters	671B Total (37B Active)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.55/1M in) across full 128K window	Flat rate ($15/1M in) across full 200K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.14/1M (no extra cache-write fee)	Automatic prefix caching at $7.5/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
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)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision
Output Modalities	text	text

DeepSeek R1 vs OpenAI o1: Respuestas Rápidas (FAQ)

Which is more intelligent, DeepSeek R1 or OpenAI o1?

OpenAI o1 is more intelligent. It scores 55 compared with 49 on the composite Intelligence Index.

Which model is faster?

OpenAI o1 is faster, generating 60 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $14.25 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek R1 has lower latency, with a time to first token of 0.95s compared with 3.20s.

Which model has a larger context window?

OpenAI o1 has a larger context window, supporting 200K tokens compared with 128K tokens.

Veredicto

Tanto DeepSeek R1 (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) como OpenAI o1 (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) destacan en producción: OpenAI o1 lidera en inteligencia general y programación, OpenAI o1 ofrece mayor velocidad de generación de tokens y DeepSeek R1 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, DeepSeek R1 o OpenAI o1?+

En las pruebas de inteligencia compuesta y resolución de código, OpenAI o1 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de DeepSeek R1 frente a OpenAI o1?+

DeepSeek R1 cuesta $0.55 / $2.19 por millón de tokens de entrada/salida, mientras que OpenAI o1 cuesta $15.00 / $60.00. En costo combinado, DeepSeek R1 es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

DeepSeek R1 cuenta con 128K tokens de contexto frente a 200K tokens de OpenAI o1. En velocidad de inferencia, OpenAI o1 ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

DeepSeek R1 vs OpenAI o1

Cambia y Compara Cualquier Modelo

DeepSeek R1

OpenAI o1

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

DeepSeek R1 · Strengths & Key Advantages

Considerations & Trade-offs

OpenAI o1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

DeepSeek R1 vs OpenAI o1: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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