DeepSeek R1 vs o3-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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DeepSeek R1 by DeepSeek and o3-mini 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, o3-mini provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, o3-mini 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%
o3-mini
Razonamiento de alta eficienciade OpenAI especializado, costo-efficient modelo de razonamiento diseñado para alto-rendimiento STEM, matemáticas competitions, y complejo generación de código con adjustable razonamiento effort.
$1.10 / 1M
$4.40 / 1M
200K tokens
49%
Diferencias Clave de un Vistazo
- DeepSeek R1 is 2.1x cheaper for API usage (input + output tokens combined).
- o3-mini has a larger context window (200K vs 128K tokens).
- o3-mini is faster at 85 tok/s vs 45 tok/s.
- DeepSeek R1 leads on SWE-bench coding (49.2% vs 49.0%).
| Característica | DeepSeek R1 | o3-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 49 | 51 |
Provider | DeepSeek | OpenAI |
Input Price Cost per million input tokens | $0.55 / 1M | $1.10 / 1M |
Output Price Cost per million output tokens | $2.19 / 1M | $4.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.43 / 1M | $1.05 / 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 | 85 tok/s |
SWE-bench Verified Real-world software engineering | 49.2% | 49% |
GPQA Diamond Graduate-level reasoning | 71.5% | 79.7% |
Cached Input Price Prompt caching discount per 1M tokens | $0.14 / 1M | $0.55 / 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)
o3-mini · Strengths & Key Advantages
- mundo-clase competitivo mathematics y STEM razonamiento (79.7% en GPQA Diamond)
- Configurable razonamiento effort parámetro (bajo, medium, alto)
- masivo 100K token maximum salida generación capacidad
- económico API precios ($1.10 entrada / $4.40 salida por 1M tokens)
Considerations & Trade-offs
- texto-solo modality (no nativo visión o audio procesamiento)
- mayor initial latencia due un internal cadena de pensamiento generación
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | DeepSeek R1 | o3-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $1.30 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 25K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | DeepSeek R1 | o3-mini |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | o3-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-01 | 2025-01 |
| Knowledge Cutoff | July 2024 | October 2023 |
| Architecture | Open-Weights Reasoning MoE | Chain-of-Thought Reasoning Dense |
| 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 ($1.1/1M 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 $0.55/1M (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 | 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) |
| 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 |
| Output Modalities | text | text |
DeepSeek R1 vs o3-mini: Respuestas Rápidas (FAQ)
Which is more intelligent, DeepSeek R1 or o3-mini?
o3-mini is more intelligent. It scores 51 compared with 49 on the composite Intelligence Index.
Which model is faster?
o3-mini is faster, generating 85 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 $1.05 (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 1.40s.
Which model has a larger context window?
o3-mini 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 o3-mini if your workflow prioritizes Competitive coding, mathematical research, algorithmic optimization, and backend logic. 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 o3-mini 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 o3-mini costs $1.10 input and $4.40 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 o3-mini?+
DeepSeek R1 supports a context window of 128K tokens (~96k words), while o3-mini supports 200K tokens (~150k words). Therefore, o3-mini 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 o3-mini?+
DeepSeek R1 scores 49.2% on SWE-bench Verified while o3-mini scores 49%. 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 o3-mini?+
DeepSeek R1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3-mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek R1 and o3-mini?+
DeepSeek R1 generates approximately 45 tokens per second, while o3-mini delivers 85 tokens per second. In latency-sensitive production environments, o3-mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek R1 and o3-mini 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 o3-mini. 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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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