DeepSeek R1 vs OpenAI o1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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DeepSeek R1 by DeepSeek and OpenAI o1 by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, DeepSeek R1 holds a notable pricing advantage for token ingestion. On output generation, DeepSeek R1 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, OpenAI o1 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, OpenAI o1 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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DeepSeek R1
Revolución de razonamiento abiertoInnovador modelo de razonamiento de pesos abiertos entrenado con aprendizaje por refuerzo un gran escala, igualando un gigantes propietarios por una fracción del costo.
$0.55 / 1M
$2.19 / 1M
128K tokens
49.2%
OpenAI o1
Mente maestra del razonamientode OpenAI líder pensamiento modelo trained con grande-escala reinforcement learning un deliberate un través de complejo science, matemáticas, y ingeniería de software questions.
$15.00 / 1M
$60.00 / 1M
200K tokens
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%).
| 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 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 R1 · Strengths & Key Advantages
- 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
Considerations & Trade-offs
- Slower latencia due un extendido internal razonamiento generación
- texto-solo (no nativo imagen o audio modality)
OpenAI o1 · Strengths & Key Advantages
- Phenomenal GPQA Diamond (77.3%) y AIME matemáticas rendimiento
- extendido internal cadena de pensamiento auto-corrects subtle logical errors before responding
- 200K ventana de contexto con up un 100K salida tokens
Considerations & Trade-offs
- Significant latencia before initial token salida due un razonamiento deliberation
- mayor precios nivel ($15/$60 por 1M tokens) suitable para profundo razonamiento sobre casual chat
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
Verdict & Model Selection Recommendation: Choose DeepSeek R1 if your priority is Competitive coding, mathematical research, and privacy-first on-premise deployments. Its performance in demanding tasks justifies its operational token cost. Choose OpenAI o1 if your workflow prioritizes Doctorate-level scientific research, competitive algorithmic coding, and high-stakes mathematical proofs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does DeepSeek R1 compare to OpenAI o1 in API token pricing and cost per token?+
DeepSeek R1 costs $0.55 per 1M input tokens and $2.19 per 1M output tokens, whereas OpenAI o1 costs $15.00 input and $60.00 output. Overall, DeepSeek R1 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: DeepSeek R1 or OpenAI o1?+
DeepSeek R1 supports a context window of 128K tokens (~96k words), while OpenAI o1 supports 200K tokens (~150k words). Therefore, OpenAI o1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: DeepSeek R1 or OpenAI o1?+
DeepSeek R1 scores 49.2% on SWE-bench Verified while OpenAI o1 scores 48.9%. For autonomous software engineering and multi-file debugging, DeepSeek R1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek R1 and OpenAI o1?+
DeepSeek R1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, OpenAI o1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek R1 and OpenAI o1?+
DeepSeek R1 generates approximately 45 tokens per second, while OpenAI o1 delivers 60 tokens per second. In latency-sensitive production environments, OpenAI o1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek R1 and OpenAI o1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek R1 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to OpenAI o1. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- OpenAI o1 vs o1-mini: Benchmarks, Pricing & Speed ComparisonComparar
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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: GetInfoUs Independent AI Benchmarks & Official API Specs