Head-to-Head Model Comparison

Qwen3.5 27B vs DeepSeek Chat

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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Qwen3.5 27BProfile

DeepSeek ChatProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3.5 27B

Modelo Base de Pesos Abiertos

Qwen3.5 27B 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 262K, velocidad de 90 tokens/s, índice de inteligencia de 75/100 y tar

Intelligence Index75 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

DeepSeek

DeepSeek Chat

Modelo Base de Pesos Abiertos

DeepSeek Chat 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 75 tokens/s, índice de inteligencia de 50/100 y ta

Intelligence Index50 / 100

Input Token:

$0.28 / 1M

Output Token:

$0.42 / 1M

Context Window:

1M tokens

SWE-bench Verified:

54%

Diferencias Clave de un Vistazo

Qwen3.5 27B is 1.0x cheaper for API usage (input + output tokens combined).
DeepSeek Chat has a larger context window (1M vs 262K tokens).
Qwen3.5 27B is faster at 90 tok/s vs 75 tok/s.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	Qwen3.5 27B	DeepSeek Chat
Intelligence Index Composite reasoning & knowledge score (0-100)	75	50
Provider	Alibaba	DeepSeek
Input Price Cost per million input tokens	$0.00 / 1M	$0.28 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.42 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.12 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	90 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	N/A	54%
GPQA Diamond Graduate-level reasoning	56.5%	65%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.03 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Qwen3.5 27B

1.0x cheaper monthly runtime

Velocidad

Qwen3.5 27B

+20% faster generation

Contexto

DeepSeek Chat

1M tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Qwen3.5 27BDeepSeek Chat

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs54%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

56.5%vs65%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

74%vs79.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1285ptsvs1335pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs10.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs36%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs33.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs56.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs74%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs75 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Qwen3.5 27B0.41s

DeepSeek Chat0.52s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen3.5 27B5.97s

DeepSeek Chat7.19s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen3.5 27B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

DeepSeek Chat Monthly API Bill

$0.84 / month

$0.28/1M in · $0.42/1M out

Qwen3.5 27B saves approximately $0.84 / month (100%) under this workload.1.0x Savings

Qwen3.5 27B · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 8K tokens de salida máxima (GPQA Diamond 56.5% · MMLU-Pro 74%)
Rendimiento ágil de 90 tokens/segundo con latencia inicial (TTFT) de solo 410 ms
Pesos abiertos (27B) 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 Chat · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 16K tokens de salida máxima (SWE-bench Verified 54% · GPQA Diamond 65%)
Rendimiento ágil de 75 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
Pesos abiertos (671B Total (37B )) 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	Qwen3.5 27B	DeepSeek Chat
Cost per Task Estimated USD to finish one task	$0.00	$0.15
Output Tokens / Task Tokens generated per task	13K	35K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Qwen3.5 27B	DeepSeek Chat
Official API Model ID (SDK)	qwen3-5-27b-instruct	deepseek-chat
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-02	2025-12
Knowledge Cutoff	December 2025	September 2025
Architecture	Dense Multimodal Autoregressive Transformer	Multi-Head Latent Attention (MLA) Sparse MoE
Parameters	27B	671B Total (37B 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 ($0.28/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $0.028/1M (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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 17 GB VRAM (4-bit Quantized) · 58 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, vision	text
Output Modalities	text	text

Qwen3.5 27B vs DeepSeek Chat: Respuestas Rápidas (FAQ)

Which is more intelligent, Qwen3.5 27B or DeepSeek Chat?

Qwen3.5 27B is more intelligent. It scores 75 compared with 50 on the composite Intelligence Index.

Which model is faster?

Qwen3.5 27B is faster, generating 90 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Qwen3.5 27B is cheaper at $0.00 per 1M tokens blended, compared with $0.12 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen3.5 27B has lower latency, with a time to first token of 0.41s compared with 0.52s.

Which model has a larger context window?

DeepSeek Chat has a larger context window, supporting 1M tokens compared with 262.1K tokens.

Veredicto

Tanto Qwen3.5 27B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como DeepSeek Chat (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3.5 27B lidera en inteligencia general y programación, Qwen3.5 27B ofrece mayor velocidad de generación de tokens y Qwen3.5 27B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Qwen3.5 27B o DeepSeek Chat?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen3.5 27B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Qwen3.5 27B frente a DeepSeek Chat?+

Qwen3.5 27B cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que DeepSeek Chat cuesta $0.28 / $0.42. En costo combinado, Qwen3.5 27B es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Qwen3.5 27B cuenta con 262.1K tokens de contexto frente a 1M tokens de DeepSeek Chat. En velocidad de inferencia, Qwen3.5 27B ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Qwen3.5 27B vs DeepSeek Chat

Cambia y Compara Cualquier Modelo

Qwen3.5 27B

DeepSeek Chat

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Qwen3.5 27B · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek Chat · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Qwen3.5 27B vs DeepSeek Chat: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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