DeepSeek Reasoner vs DeepSeek-R1-0528
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
DeepSeek Reasoner by DeepSeek and DeepSeek-R1-0528 by DeepSeek 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 Reasoner holds a notable pricing advantage for token ingestion. On output generation, DeepSeek Reasoner is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek Reasoner provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, DeepSeek Reasoner 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 Reasoner
Razonador abierto1-millones-token abierto razonamiento potencia desarrollado para multipaso mathematics, complejo codificación, y estructurado herramienta usage.
$0.28 / 1M
$0.42 / 1M
1M tokens
56.5%
DeepSeek-R1-0528
Actualización de razonador abiertoMay 2025 checkpoint de DeepSeek-R1 ofreciendo refined reinforcement learning, stabilized cadena de pensamiento, y improved matemáticas razonamiento.
$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
- extendido 1M contexto capacidad combined con rigorous cadena de pensamiento lógica
- inigualable precios disruption: solo $0.28 entrada y $0.42 salida por millones tokens
- alto precisión en STEM pruebas de rendimiento, AIME, y complejo programación challenges
Considerations & Trade-offs
- Highly verbose responses que puede consume significant salida quota if no constrained
- texto-solo entrada modality sin diagram o documento imagen soporte
DeepSeek-R1-0528 · Strengths & Key Advantages
- de pesos abiertos razonamiento champion con transparent y inspectable cadena de pensamiento
- Highly competitivo GPQA Diamond (74.2%) y AIME (85.0%) competition matemáticas scores
- Inexpensive API precios y completo en-premise implementación rights
Considerations & Trade-offs
- Verbose token generación resulting en mayor latencia por question
- Strict 128K ventana de contexto y texto-solo modality
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
Verdict & Model Selection Recommendation: Choose DeepSeek Reasoner if your priority is Long-form code audits, mathematical analysis, and budget-friendly autonomous reasoning loops. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek-R1-0528 if your workflow prioritizes Mathematical theorem proving, rigorous algorithm design, and privacy-compliant research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does DeepSeek Reasoner compare to DeepSeek-R1-0528 in API token pricing and cost per token?+
DeepSeek Reasoner costs $0.28 per 1M input tokens and $0.42 per 1M output tokens, whereas DeepSeek-R1-0528 costs $0.55 input and $2.19 output. Overall, DeepSeek Reasoner 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 Reasoner or DeepSeek-R1-0528?+
DeepSeek Reasoner supports a context window of 1,000K tokens (~750k words), while DeepSeek-R1-0528 supports 128K tokens (~96k words). Therefore, DeepSeek Reasoner can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: DeepSeek Reasoner or DeepSeek-R1-0528?+
DeepSeek Reasoner scores 56.5% on SWE-bench Verified while DeepSeek-R1-0528 scores 51.8%. For autonomous software engineering and multi-file debugging, DeepSeek Reasoner holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between DeepSeek Reasoner and DeepSeek-R1-0528?+
DeepSeek Reasoner features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek-R1-0528 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between DeepSeek Reasoner and DeepSeek-R1-0528?+
DeepSeek Reasoner generates approximately 52 tokens per second, while DeepSeek-R1-0528 delivers 48 tokens per second. In latency-sensitive production environments, DeepSeek Reasoner provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining DeepSeek Reasoner and DeepSeek-R1-0528 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek Reasoner to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to DeepSeek-R1-0528. 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.
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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