Head-to-Head Model Comparison

QwQ 32B Preview vs Qwen2.5-VL 72B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

QwQ 32B PreviewProfile

Qwen2.5-VL 72BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

QwQ 32B Preview

Razonamiento de Pesos Abiertos

QwQ 32B Preview de Alibaba es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 33K, velocidad de 52 tokens/s, índice de inteligencia de 47/100 y

Intelligence Index47 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

54.2%

Alibaba

Qwen2.5-VL 72B

IA Multimodal Nativa

Qwen2.5-VL 72B de Alibaba es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 131K, velocidad de 55 tokens/s, índice de inteligencia de 79/100 y tarifa

Intelligence Index79 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

38%

Diferencias Clave de un Vistazo

Qwen2.5-VL 72B is 1.0x cheaper for API usage (input + output tokens combined).
Qwen2.5-VL 72B has a larger context window (131K vs 33K tokens).
Qwen2.5-VL 72B is faster at 55 tok/s vs 52 tok/s.
QwQ 32B Preview leads on SWE-bench coding (54.2% vs 38.0%).

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

Característica	QwQ 32B Preview	Qwen2.5-VL 72B
Intelligence Index Composite reasoning & knowledge score (0-100)	47	79
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.20 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	32.8K tokens~49 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	52 tok/s	55 tok/s
SWE-bench Verified Real-world software engineering	54.2%	38%
GPQA Diamond Graduate-level reasoning	65.2%	58%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.00 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

QwQ 32B Preview

+16.2% SWE-bench lead

Costo

Qwen2.5-VL 72B

1.0x cheaper monthly runtime

Velocidad

Qwen2.5-VL 72B

+6% faster generation

Contexto

Qwen2.5-VL 72B

131.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

QwQ 32B PreviewQwen2.5-VL 72B

SWE-bench Verified (Autonomous Coding)

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

54.2%vs38%

GPQA Diamond (Doctorate-Level Science & Math)

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

65.2%vs58%

MMLU-Pro (Multi-task Language Understanding)

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

74.8%vs77.5%

LMSYS Chatbot Arena Elo

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

1312ptsvs1315pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

9.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

29.5%vsN/A

SciCode (Scientific Computing)

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

36.2%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.4%vsN/A

AIME (Advanced Math Competition)

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

79.7%vsN/A

Long-Context Reasoning

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

60.1%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

52 tok/s vs55 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

QwQ 32B Preview0.82s

Qwen2.5-VL 72B0.72s

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

QwQ 32B Preview10.44s

Qwen2.5-VL 72B9.81s

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)

QwQ 32B Preview Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

Qwen2.5-VL 72B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen2.5-VL 72B saves approximately $0.92 / month (100%) under this workload.1.0x Savings

QwQ 32B Preview · Strengths & Key Advantages

Ventana de contexto de 33K con hasta 8K tokens de salida máxima (SWE-bench Verified 54.2% · GPQA Diamond 65.2%)
Rendimiento ágil de 52 tokens/segundo con latencia inicial (TTFT) de solo 820 ms
Pesos abiertos (32B) 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

Qwen2.5-VL 72B · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 8K tokens de salida máxima (SWE-bench Verified 38% · GPQA Diamond 58%)
Rendimiento ágil de 55 tokens/segundo con latencia inicial (TTFT) de solo 720 ms
Pesos abiertos (72B) 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	QwQ 32B Preview	Qwen2.5-VL 72B
Cost per Task Estimated USD to finish one task	$0.45	$0.00
Output Tokens / Task Tokens generated per task	22K	20K
Reasoning Tokens / Task Thinking tokens burned per task	18K	6K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	QwQ 32B Preview	Qwen2.5-VL 72B
Official API Model ID (SDK)	qwq-32b-instruct	qwen2-5-vl-72b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-11	2024-09
Knowledge Cutoff	September 2024	June 2024
Architecture	Reinforcement Learning Reasoning Transformer	Open-Weights Multimodal Vision-Language Transformer
Parameters	32B	72B
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 ($0.2/1M in) across full 33K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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	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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 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, vision
Output Modalities	text	text

QwQ 32B Preview vs Qwen2.5-VL 72B: Respuestas Rápidas (FAQ)

Which is more intelligent, QwQ 32B Preview or Qwen2.5-VL 72B?

Qwen2.5-VL 72B is more intelligent. It scores 79 compared with 47 on the composite Intelligence Index.

Which model is faster?

Qwen2.5-VL 72B is faster, generating 55 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen2.5-VL 72B is cheaper at $0.00 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen2.5-VL 72B has lower latency, with a time to first token of 0.72s compared with 0.82s.

Which model has a larger context window?

Qwen2.5-VL 72B has a larger context window, supporting 131.1K tokens compared with 32.8K tokens.

Veredicto

Tanto QwQ 32B Preview (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) como Qwen2.5-VL 72B (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen2.5-VL 72B lidera en inteligencia general y programación, Qwen2.5-VL 72B ofrece mayor velocidad de generación de tokens y Qwen2.5-VL 72B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, QwQ 32B Preview o Qwen2.5-VL 72B?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen2.5-VL 72B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de QwQ 32B Preview frente a Qwen2.5-VL 72B?+

QwQ 32B Preview cuesta $0.20 / $0.60 por millón de tokens de entrada/salida, mientras que Qwen2.5-VL 72B cuesta $0.00 / $0.00. En costo combinado, Qwen2.5-VL 72B es la opción más económica.

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

QwQ 32B Preview cuenta con 32.8K tokens de contexto frente a 131.1K tokens de Qwen2.5-VL 72B. En velocidad de inferencia, Qwen2.5-VL 72B 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)

QwQ 32B Preview vs Qwen2.5-VL 72B

Cambia y Compara Cualquier Modelo

QwQ 32B Preview

Qwen2.5-VL 72B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

QwQ 32B Preview · Strengths & Key Advantages

Considerations & Trade-offs

Qwen2.5-VL 72B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

QwQ 32B Preview vs Qwen2.5-VL 72B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

Related reading