Qwen3.8 Max Preview vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3.8 Max Preview de Alibaba y DeepSeek V4.1-Flash de DeepSeek compiten directamente en el ecosistema actual de IA. Qwen3.8 Max Preview ofrece una ventana de contexto de 1M tokens a $0.00/$0.00 por millón de tokens (entrada/salida), mientras que DeepSeek V4.1-Flash ofrece 1M tokens de contexto a $0.30/$1.20 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.
Cambia y Compara Cualquier Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Qwen3.8 Max Preview
Modelo Base de Pesos AbiertosQwen3.8 Max Preview de Alibaba 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 60 tokens/s, índice de inteligencia de 93/100
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
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%
Diferencias Clave de un Vistazo
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- DeepSeek V4.1-Flash is faster at 110 tok/s vs 60 tok/s.
- Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 74.2%).
| Característica | Qwen3.8 Max Preview | DeepSeek V4.1-Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 58 |
Provider | Alibaba | DeepSeek |
Input Price Cost per million input tokens | $0.00 / 1M | $0.30 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $1.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.18 / 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 | 32K | 384K |
Throughput Speed Tokens generated per second | 60 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 75.2% | 74.2% |
GPQA Diamond Graduate-level reasoning | 79.5% | 90.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.01 / 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.
Qwen3.8 Max Preview · Strengths & Key Advantages
- Ventana de contexto de 1M con hasta 32K tokens de salida máxima (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
- Rendimiento ágil de 60 tokens/segundo con latencia inicial (TTFT) de solo 680 ms
- Pesos abiertos (2.4T total (95B MoE)) 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
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.8 Max Preview | DeepSeek V4.1-Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.27 |
Output Tokens / Task Tokens generated per task | 35K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 28K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Qwen3.8 Max Preview | DeepSeek V4.1-Flash |
|---|---|---|
| Official API Model ID (SDK) | qwen3-8-max-preview-instruct | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | April 2026 | June 2026 |
| Architecture | Colossus-Scale Open Sparse Mixture-of-Experts | Causal Encoder-Decoder (20 enc + 20 dec layers) |
| Parameters | 2.4T total (95B active MoE) | 763B total (8B prefill / 16B decode active) |
| 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) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.3/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.006/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · 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) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 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, vision | text, vision |
| Output Modalities | text | text |
Qwen3.8 Max Preview vs DeepSeek V4.1-Flash: Respuestas Rápidas (FAQ)
Which is more intelligent, Qwen3.8 Max Preview or DeepSeek V4.1-Flash?
Qwen3.8 Max Preview is more intelligent. It scores 93 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 60 tokens per second.
Which model is cheaper to run?
Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $0.18 (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 0.68s.
Veredicto
Tanto Qwen3.8 Max Preview (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como DeepSeek V4.1-Flash (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3.8 Max Preview lidera en inteligencia general y programación, DeepSeek V4.1-Flash ofrece mayor velocidad de generación de tokens y Qwen3.8 Max Preview resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, Qwen3.8 Max Preview o DeepSeek V4.1-Flash?+
En las pruebas de inteligencia compuesta y resolución de código, Qwen3.8 Max Preview obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.
¿Cuánto cuesta la API de Qwen3.8 Max Preview frente a DeepSeek V4.1-Flash?+
Qwen3.8 Max Preview cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que DeepSeek V4.1-Flash cuesta $0.30 / $1.20. En costo combinado, Qwen3.8 Max Preview es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
Qwen3.8 Max Preview cuenta con 1M tokens de contexto frente a 1M tokens de DeepSeek V4.1-Flash. En velocidad de inferencia, DeepSeek V4.1-Flash ofrece un rendimiento superior.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- DeepSeek V4.1-Flash vs. Gemini 4 Argon: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- DeepSeek V4.1-Flash vs. GLM-5.2: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- GPT-6 Luna vs. DeepSeek V4.1-Flash: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Grok 4.7 vs. DeepSeek V4.1-Flash: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Llama 5 vs. Qwen3.8 Max Preview: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
- Mistral Medium 3.5 vs. DeepSeek V4.1-Flash: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
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: Documentación oficial de API de Alibaba y DeepSeek, tarjetas de sistema y benchmarks independientes (2026).