Qwen3 14B — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Alibaba |
|---|---|
| Tipo | LLM (densa) |
| Modalidad | Texto → Texto |
| Parámetros | 14B |
| Ventana de contexto | 128 K |
| Licencia | Apache 2.0 (abierta) |
| Pesos abiertos | Sí |
| Lanzado | 2025 |
| Precio de entrada | $0.12 /1M |
| Precio de salida | $0.24 /1M |
| Proveedores de API | Alibaba, OpenRouter, Ollama |
Ejecútelo localmente
| VRAM (4 bits) | ~9 GB |
|---|---|
| GPU mínima | RTX 4070 12 GB (cuarto trimestre) |
What is Qwen3 14B?
Qwen3 14B is a mid-size dense model from Alibaba’s Qwen3 family — Apache 2.0, a 128K
context, and roughly 9 GB of VRAM at 4-bit, which puts it on an RTX 4070 12GB. Hosted pricing
is $0.12 in / $0.24 out per million tokens.
It sits at the point where a local model stops feeling like a demo. The 8B tier is fine
for classification and short generation but starts to show its limits on multi-step
instructions; the 32B tier needs a 24 GB card that most developer machines do not have.
Qwen3 14B is the largest of the family that still fits a mainstream 12 GB GPU, and being
dense rather than mixture-of-experts, its memory requirement is predictable — 9 GB is 9 GB,
with no gap between active and resident parameters to catch you out when sizing hardware.
Apache 2.0 licensing means no conditions to review, and a 128K context is enough for
realistic document work. For a team building its first genuinely useful on-premises
deployment, this is a sensible default.
Qwen3 14B pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $0.120 |
|---|---|
| Salida (por cada millón de tokens) | $0.240 |
| Relación salida/entrada | 2× |
| Combinada (4:1 entrada:salida) | $0.144 por 1 millón de tokens |
What Qwen3 14B costs per month
Gasto mensual real con una mezcla entrada:salida de 4:1, es decir, la proporción que realmente genera una carga de trabajo típica de chat o RAG.
| Carga de trabajo | Tokens/mes | Coste mensual |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $0.18 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $3.60 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $36 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to Qwen3 14B
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| Mistral 7B abierta | $0.0220 | 85% cheaper |
| Llama 3.1 8B abierta | $0.0220 | 85% cheaper |
| Mistral NeMo 12B abierta | $0.0240 | 83% cheaper |
¿Autoalojarlo o pagar por la API?
Qwen3 14B is open-weight, so you can run it yourself. It needs ~9 GB of VRAM at 4-bit (RTX 4070 12GB (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 calcula el punto de equilibrio para su volumen de tokens.
Preguntas frecuentes
How much does Qwen3 14B cost per 1M tokens?
Qwen3 14B costs $0.120 per 1M input tokens and $0.240 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.144 per 1M tokens.
How much does Qwen3 14B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.60 on Qwen3 14B. A side project (1M in / 0.25M out) costs roughly $0.18.
What is a cheaper alternative to Qwen3 14B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 85% less than Qwen3 14B. It is also open-weight, so self-hosting is an option.
Can I run Qwen3 14B locally?
Yes. Qwen3 14B is open-weight and needs about ~9 GB of VRAM at 4-bit quantisation (RTX 4070 12GB (Q4)).
Why does Qwen3 14B 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 14B charges 2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Los precios corresponden a las tarifas oficiales publicadas para la API principal del modelo y se revisan periódicamente conforme los proveedores los actualicen. No incluyen descuentos por volumen, procesamiento por lotes ni entradas en caché. Compare todos los modelos uno al lado del otro en la Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).