Qwen3 32B — Especificaciones
| Desarrollador | Alibaba |
|---|---|
| Tipo | LLM (densa) |
| Modalidad | Texto → Texto |
| Parámetros | 32B |
| Ventana de contexto | 128 K |
| Salida máxima | — |
| Licencia | Apache 2.0 (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2025 |
| Precio de entrada | 0,08 $/millón |
| Precio de salida | 0,28 USD / millón |
| Proveedores de API | Alibaba, OpenRouter, Ollama |
Ejecútelo localmente
| VRAM (4 bits) | ~20 GB |
|---|---|
| GPU mínima | RTX 4090 de 24 GB (Q4) |
What is Qwen3 32B?
Qwen3 32B is Alibaba’s largest dense Qwen3 — strong general reasoning and coding under a
fully permissive Apache 2.0 licence, with a 128K context. It runs comfortably at 4-bit on a
24 GB consumer GPU, needing about 20 GB, and costs $0.08 in / $0.28 out per million tokens
hosted.
This is the model that makes an RTX 4090 or 3090 worth owning for inference. At 20 GB it
leaves just enough headroom on a 24 GB card for a real context window, and being dense, its
quality per gigabyte is the best available in that bracket — mixture-of-experts models of
similar capability need far more memory resident even though they compute less per token. For
local coding assistants and private document work on a single consumer card, it is close to
the practical maximum. Apache 2.0 removes any licensing review, and the hosted price is low
enough to prototype against before buying hardware. Check your intended quantisation against
el Calculadora de VRAM — the margin on a
24 GB card is real but not generous.
Qwen3 32B pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.0800 |
|---|---|
| Salida (por cada millón de tokens) | $0.280 |
| Output/input ratio | 3.5× |
| Blended (4:1 in:out) | $0.120 per 1M tokens |
What Qwen3 32B costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Carga de trabajo | Tokens/mes | Cost / month |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $0.15 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $3.00 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $30 |
Run your own numbers in the Calculadora de costos de API de IA.
Cheaper alternatives to Qwen3 32B
| Modelos | Dólares por millón combinados | You save |
|---|---|---|
| Gemma 3 27B abierta | $0.0960 | 20% cheaper |
Self-host or pay the API?
Qwen3 32B is open-weight, so you can run it yourself. It needs ~20 GB of VRAM at 4-bit (RTX 4090 24GB (Q4)). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calculadora de autohospedaje frente a API works out the break-even point for your token volume.
Preguntas frecuentes
How much does Qwen3 32B cost per 1M tokens?
Qwen3 32B costs $0.0800 per 1M input tokens and $0.280 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.120 per 1M tokens.
How much does Qwen3 32B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.00 on Qwen3 32B. A side project (1M in / 0.25M out) costs roughly $0.15.
What is a cheaper alternative to Qwen3 32B?
Gemma 3 27B is the strongest cheaper option in our database at $0.0960 per 1M blended — about 20% less than Qwen3 32B. It is also open-weight, so self-hosting is an option.
Can I run Qwen3 32B locally?
Yes. Qwen3 32B is open-weight and needs about ~20 GB of VRAM at 4-bit quantisation (RTX 4090 24GB (Q4)).
Why does Qwen3 32B charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Qwen3 32B charges 3.5× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Prices are the published list rates for the model's primary API and are reviewed as providers change them. Volume, batch and cached-input discounts are not included. Compare every model side by side in the Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).
