DeepSeek V4.1-Flash vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare DeepSeek V4.1-Flash by DeepSeek against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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DeepSeek V4.1-Flash
Modelo Base de Pesos AbiertosDeepSeek V4.1-Flash de DeepSeek es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 1M, velocidad de 110 tokens/s, índice de inteligencia de 58/1
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Claude Opus 5.5
Modelo Insignia de FronteraClaude Opus 5.5 de Anthropic es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 1M, velocidad de 78 tokens/s, índice de inteligencia de 94/100 y ta
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Diferencias Clave de un Vistazo
- DeepSeek V4.1-Flash is 16.0x cheaper for API usage (input + output tokens combined).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 74.2%).
| Característica | DeepSeek V4.1-Flash | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 94 |
Provider | DeepSeek | Anthropic |
Input Price Cost per million input tokens | $0.30 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.18 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 384K | 128K |
Throughput Speed Tokens generated per second | 110 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 74.2% | 84.6% |
GPQA Diamond Graduate-level reasoning | 90.9% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.40 / 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 V4.1-Flash · Strengths & Key Advantages
- Ventana de contexto de 1M con hasta 384K tokens de salida máxima (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
- Rendimiento ágil de 110 tokens/segundo con latencia inicial (TTFT) de solo 460 ms
- Pesos abiertos (763B total (8B prefill / 16B decode )) 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
Claude Opus 5.5 · Strengths & Key Advantages
- Ventana de contexto de 1M con hasta 128K tokens de salida máxima (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
- Rendimiento ágil de 78 tokens/segundo con latencia inicial (TTFT) de solo 1400 ms
- Economía de API competitiva ($4/$20 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 V4.1-Flash | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.27 | $3.85 |
Output Tokens / Task Tokens generated per task | 58K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 42K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | DeepSeek V4.1-Flash | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | deepseek-reasoner | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-09 | 2026-09 |
| Knowledge Cutoff | June 2026 | June 2026 |
| Architecture | Causal Encoder-Decoder (20 enc + 20 dec layers) | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 763B total (8B prefill / 16B decode 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 | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.3/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.006/1M (no extra cache-write fee) | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| 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 Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | 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) · 473 GB VRAM (4-bit Quantized) · 1640 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) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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, vision | text, vision |
| Output Modalities | text | text |
DeepSeek V4.1-Flash vs Claude Opus 5.5: Respuestas Rápidas (FAQ)
Which is more intelligent, DeepSeek V4.1-Flash or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 58 on the composite Intelligence Index.
Which model is faster?
DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
DeepSeek V4.1-Flash is cheaper at $0.18 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
DeepSeek V4.1-Flash has lower latency, with a time to first token of 0.46s compared with 1.40s.
Veredicto
Choose DeepSeek V4.1-Flash for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api., or choose Claude Opus 5.5 for ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
Which is cheaper: DeepSeek V4.1-Flash or Claude Opus 5.5?+
DeepSeek V4.1-Flash costs $0.3/1M input and $1.2/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: DeepSeek V4.1-Flash or Claude Opus 5.5?+
DeepSeek V4.1-Flash supports 1M tokens whereas Claude Opus 5.5 supports 1M tokens.
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: DeepSeek & Anthropic Official Documentation