Qwen3 14B — Specifiche
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Sviluppatore | Alibaba |
|---|---|
| Tipo | LLM (densa) |
| Modalità | Testo → Testo |
| Parametri | 14B |
| Finestra contestuale | 128K |
| Licenza | Apache 2.0 (open) |
| Pesi aperti | Sì |
| Pubblicato | 2025 |
| Prezzo dell’input | $0.12 /1M |
| Prezzo dell’output | $0.24 /1M |
| Provider API | Alibaba, OpenRouter, Ollama |
Esegui localmente
| VRAM (4-bit) | ~9 GB |
|---|---|
| GPU minima | RTX 4070 12 GB (Q4) |
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
| Input (per ogni milione di token) | $0.120 |
|---|---|
| Output (per ogni milione di token) | $0.240 |
| Rapporto output/input | 2× |
| Combinato (4:1 in:out) | $0.144 per 1 milione di token |
What Qwen3 14B costs per month
Spesa mensile reale con un mix input-to-output 4:1 — il rapporto effettivamente prodotto da un tipico carico di lavoro basato su chat o RAG.
| Carico di lavoro | Token/mese | Costo/mese |
|---|---|---|
| Progetto secondario | 1 milione in ingresso / 0,25 milioni in uscita | $0.18 |
| Piccolo team | 20 milioni in ingresso / 5 milioni in uscita | $3.60 |
| Produzione | 200 milioni in ingresso / 50 milioni in uscita | $36 |
Calcola i tuoi numeri personalizzati nel Calcolatore dei costi delle API per l'IA.
Cheaper alternatives to Qwen3 14B
| Modello | Costo combinato ($/1 milione) | Risparmi |
|---|---|---|
| Mistral 7B aperta | $0.0220 | 85% cheaper |
| Llama 3.1 8B aperta | $0.0220 | 85% cheaper |
| Mistral NeMo 12B aperta | $0.0240 | 83% cheaper |
Eseguirlo in autonomia o pagare l’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 calcolatore self-hosting vs API calcola il punto di pareggio in base al tuo volume di token.
Domande frequenti
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.
I prezzi indicati sono le tariffe ufficiali pubblicate per l'API principale del modello e vengono aggiornati man mano che i fornitori li modificano. Sconti per volumi elevati, elaborazione batch e input memorizzati nella cache non sono inclusi. Confronta tutti i modelli fianco a fianco nel Database di modelli IA o il Classifica LLM.