Head-to-Head Model Comparison

Llama 3.2 1B vs Qwen 2.5 32B 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.

Llama 3.2 1BProfile

Qwen 2.5 32B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 3.2 1B

Modelo Base de Pesos Abiertos

Llama 3.2 1B de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 320 tokens/s, índice de inteligencia de 42/100 y tari

Intelligence Index42 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

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%

Diferencias Clave de un Vistazo

Llama 3.2 1B is 1.0x cheaper for API usage (input + output tokens combined).
Qwen 2.5 32B Instruct has a larger context window (131K vs 128K tokens).
Llama 3.2 1B is faster at 320 tok/s vs 85 tok/s.

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

Característica	Llama 3.2 1B	Qwen 2.5 32B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	42	41
Provider	Meta	Alibaba
Input Price Cost per million input tokens	$0.00 / 1M	$0.18 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.36 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.10 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	131.1K tokens~197 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	320 tok/s	85 tok/s
SWE-bench Verified Real-world software engineering	N/A	40.8%
GPQA Diamond Graduate-level reasoning	N/A	48.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.04 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Llama 3.2 1B

1.0x cheaper monthly runtime

Velocidad

Llama 3.2 1B

+276% faster generation

Contexto

Qwen 2.5 32B Instruct

131.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Llama 3.2 1BQwen 2.5 32B Instruct

SWE-bench Verified (Autonomous Coding)

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

N/Avs40.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs48.5%

MMLU-Pro (Multi-task Language Understanding)

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

46%vs67.2%

LMSYS Chatbot Arena Elo

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

1110ptsvs1255pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs5.6%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs26.2%

SciCode (Scientific Computing)

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

N/Avs26.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.1%

AIME (Advanced Math Competition)

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

N/Avs52.3%

Long-Context Reasoning

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

N/Avs64%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

320 tok/s vs85 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

Llama 3.2 1B0.12s

Qwen 2.5 32B Instruct0.48s

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

Llama 3.2 1B1.68s

Qwen 2.5 32B Instruct6.36s

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)

Llama 3.2 1B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen 2.5 32B Instruct Monthly API Bill

$0.65 / month

$0.18/1M in · $0.36/1M out

Llama 3.2 1B saves approximately $0.65 / month (100%) under this workload.1.0x Savings

Llama 3.2 1B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 4K tokens de salida máxima (MMLU-Pro 46% · Arena Elo 1110)
Rendimiento ágil de 320 tokens/segundo con latencia inicial (TTFT) de solo 120 ms
Pesos abiertos (1B) 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 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Llama 3.2 1B	Qwen 2.5 32B Instruct
Cost per Task Estimated USD to finish one task	$0.00	$0.16
Output Tokens / Task Tokens generated per task	2K	28K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Llama 3.2 1B	Qwen 2.5 32B Instruct
Official API Model ID (SDK)	meta-llama/Llama-3.2-1B	qwen-2-5-32b-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	December 2023	June 2024
Architecture	Pruned and distilled ultra-compact transformer	Dense Autoregressive Transformer
Parameters	1B	32B
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 ($0.18/1M in) across full 131K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.036/1M 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) · 2 GB VRAM (4-bit Quantized) · 4 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 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

Llama 3.2 1B vs Qwen 2.5 32B Instruct: Respuestas Rápidas (FAQ)

Which is more intelligent, Llama 3.2 1B or Qwen 2.5 32B Instruct?

Llama 3.2 1B is more intelligent. It scores 42 compared with 41 on the composite Intelligence Index.

Which model is faster?

Llama 3.2 1B is faster, generating 320 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Llama 3.2 1B is cheaper at $0.00 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.2 1B has lower latency, with a time to first token of 0.12s compared with 0.48s.

Which model has a larger context window?

Qwen 2.5 32B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Veredicto

Tanto Llama 3.2 1B (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 32B Instruct (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Llama 3.2 1B lidera en inteligencia general y programación, Llama 3.2 1B ofrece mayor velocidad de generación de tokens y Llama 3.2 1B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Llama 3.2 1B o Qwen 2.5 32B Instruct?+

En las pruebas de inteligencia compuesta y resolución de código, Llama 3.2 1B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Llama 3.2 1B frente a Qwen 2.5 32B Instruct?+

Llama 3.2 1B cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Qwen 2.5 32B Instruct cuesta $0.18 / $0.36. En costo combinado, Llama 3.2 1B es la opción más económica.

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

Llama 3.2 1B cuenta con 128K tokens de contexto frente a 131.1K tokens de Qwen 2.5 32B Instruct. En velocidad de inferencia, Llama 3.2 1B 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)

Llama 3.2 1B vs Qwen 2.5 32B Instruct

Cambia y Compara Cualquier Modelo

Llama 3.2 1B

Qwen 2.5 32B Instruct

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Llama 3.2 1B · Strengths & Key Advantages

Considerations & Trade-offs

Qwen 2.5 32B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Llama 3.2 1B vs Qwen 2.5 32B Instruct: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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