Head-to-Head Model Comparison

Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct

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.

Qwen 2.5 32B InstructProfile

Qwen 2.5 7B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen 2.5 32B Instruct

Modelo Base de Pesos Abiertos

Qwen 2.5 32B Instruct 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 131K, velocidad de 85 tokens/s, índice de inteligencia de 41

Intelligence Index41 / 100

Input Token:

$0.18 / 1M

Output Token:

$0.36 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40.8%

Alibaba

Qwen 2.5 7B Instruct

Modelo Ligero y Económico

Qwen 2.5 7B Instruct de Alibaba es un modelo de IA diseñado para máxima eficiencia de costos por token y alta velocidad en producción, con ventana de contexto de 131K, velocidad de 130 tokens/s, índice de inteligencia de 35/100 y tarifa de

Intelligence Index35 / 100

Input Token:

$0.06 / 1M

Output Token:

$0.12 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

28.5%

Diferencias Clave de un Vistazo

Qwen 2.5 7B Instruct is 3.0x cheaper for API usage (input + output tokens combined).
Qwen 2.5 7B Instruct is faster at 130 tok/s vs 85 tok/s.
Qwen 2.5 32B Instruct leads on SWE-bench coding (40.8% vs 28.5%).

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

Característica	Qwen 2.5 32B Instruct	Qwen 2.5 7B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	41	35
Provider	Alibaba	Alibaba
Input Price Cost per million input tokens	$0.18 / 1M	$0.06 / 1M
Output Price Cost per million output tokens	$0.36 / 1M	$0.12 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.10 / 1M	$0.03 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 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	85 tok/s	130 tok/s
SWE-bench Verified Real-world software engineering	40.8%	28.5%
GPQA Diamond Graduate-level reasoning	48.5%	36.8%
Cached Input Price Prompt caching discount per 1M tokens	$0.04 / 1M	$0.01 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Qwen 2.5 32B Instruct

+12.3% SWE-bench lead

Costo

Qwen 2.5 7B Instruct

3.0x cheaper monthly runtime

Velocidad

Qwen 2.5 7B Instruct

+53% faster generation

Contexto

Tied

131.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Qwen 2.5 32B InstructQwen 2.5 7B Instruct

SWE-bench Verified (Autonomous Coding)

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

40.8%vs28.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.5%vs36.8%

MMLU-Pro (Multi-task Language Understanding)

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

67.2%vs56.4%

LMSYS Chatbot Arena Elo

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

1255ptsvs1205pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

5.6%vs3.2%

Terminal-Bench (Agentic Coding & Terminal Use)

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

26.2%vs18.4%

SciCode (Scientific Computing)

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

26.5%vs18.2%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

42.1%vs29.8%

AIME (Advanced Math Competition)

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

52.3%vs38.6%

Long-Context Reasoning

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

64%vs58.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs130 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

Qwen 2.5 32B Instruct0.48s

Qwen 2.5 7B Instruct0.29s

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

Qwen 2.5 32B Instruct6.36s

Qwen 2.5 7B Instruct4.14s

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)

Qwen 2.5 32B Instruct Monthly API Bill

$0.65 / month

$0.18/1M in · $0.36/1M out

Qwen 2.5 7B Instruct Monthly API Bill

$0.22 / month

$0.06/1M in · $0.12/1M out

Qwen 2.5 7B Instruct saves approximately $0.43 / month (67%) under this workload.3.0x Savings

Qwen 2.5 32B Instruct · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 8K tokens de salida máxima (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
Rendimiento ágil de 85 tokens/segundo con latencia inicial (TTFT) de solo 480 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

Qwen 2.5 7B Instruct · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 8K tokens de salida máxima (SWE-bench Verified 28.5% · GPQA Diamond 36.8%)
Rendimiento ágil de 130 tokens/segundo con latencia inicial (TTFT) de solo 290 ms
Pesos abiertos (7B) 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	Qwen 2.5 32B Instruct	Qwen 2.5 7B Instruct
Cost per Task Estimated USD to finish one task	$0.16	$0.05
Output Tokens / Task Tokens generated per task	28K	20K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Qwen 2.5 32B Instruct	Qwen 2.5 7B Instruct
Official API Model ID (SDK)	qwen-2-5-32b-instruct	qwen-2-5-7b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-09	2024-09
Knowledge Cutoff	June 2024	June 2024
Architecture	Dense Autoregressive Transformer	Dense Autoregressive Transformer
Parameters	32B	7B
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 ($0.18/1M in) across full 131K window	Flat rate ($0.06/1M in) across full 131K window
Prompt Cache Policy & TTL	Cached input at $0.036/1M tokens	Cached input at $0.012/1M tokens
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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 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

Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct: Respuestas Rápidas (FAQ)

Which is more intelligent, Qwen 2.5 32B Instruct or Qwen 2.5 7B Instruct?

Qwen 2.5 32B Instruct is more intelligent. It scores 41 compared with 35 on the composite Intelligence Index.

Which model is faster?

Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s compared with 0.48s.

Veredicto

Tanto Qwen 2.5 32B Instruct (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Qwen 2.5 7B Instruct (Ideal para máxima eficiencia de costos por token y alta velocidad en producción, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen 2.5 32B Instruct lidera en inteligencia general y programación, Qwen 2.5 7B Instruct ofrece mayor velocidad de generación de tokens y Qwen 2.5 7B Instruct resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Qwen 2.5 32B Instruct o Qwen 2.5 7B Instruct?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen 2.5 32B Instruct obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Qwen 2.5 32B Instruct frente a Qwen 2.5 7B Instruct?+

Qwen 2.5 32B Instruct cuesta $0.18 / $0.36 por millón de tokens de entrada/salida, mientras que Qwen 2.5 7B Instruct cuesta $0.06 / $0.12. En costo combinado, Qwen 2.5 7B Instruct es la opción más económica.

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

Qwen 2.5 32B Instruct cuenta con 131.1K tokens de contexto frente a 131.1K tokens de Qwen 2.5 7B Instruct. En velocidad de inferencia, Qwen 2.5 7B Instruct 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)

Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct

Cambia y Compara Cualquier Modelo

Qwen 2.5 32B Instruct

Qwen 2.5 7B Instruct

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Qwen 2.5 32B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Qwen 2.5 7B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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