Qwen3 14B — Spécifications
| Développeur | Alibaba |
|---|---|
| Type | LLM (dense) |
| Modalité | Texte → Texte |
| Paramètres | 14B |
| Fenêtre de contexte | 128 K |
| Sortie maximale | — |
| Licence | Apache 2.0 (ouverte) |
| Poids ouverts | Oui |
| Publié | 2025 |
| Prix de l’entrée | 0,12 $ / 1 million |
| Prix de la sortie | 0,24 $ / 1 million |
| Fournisseurs d'API | Alibaba, OpenRouter, Ollama |
Exécutez-le localement
| VRAM (4 bits) | ~9 Go |
|---|---|
| GPU minimal requis | RTX 4070 12 Go (T4) |
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
| Entrée (par million de jetons) | $0.120 |
|---|---|
| Sortie (par million de jetons) | $0.240 |
| Output/input ratio | 2× |
| Blended (4:1 in:out) | $0.144 per 1M tokens |
What Qwen3 14B costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Charge de travail | Jetons/mois | Cost / month |
|---|---|---|
| Projet secondaire | 1 million en entrée / 0,25 million en sortie | $0.18 |
| Petite équipe | 20 millions en entrée / 5 millions en sortie | $3.60 |
| Production | 200 millions en entrée / 50 millions en sortie | $36 |
Run your own numbers in the Calculateur de coûts des API IA.
Cheaper alternatives to Qwen3 14B
| Modèle | Coût combiné par million de dollars | You save |
|---|---|---|
| Mistral 7B ouverte | $0.0220 | 85% cheaper |
| Llama 3.1 8B ouverte | $0.0220 | 85% cheaper |
| Mistral NeMo 12B ouverte | $0.0240 | 83% cheaper |
Self-host or pay the API?
Qwen3 14B is open-weight, so you can run it yourself. It needs ~9 Go 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 calculateur auto-hébergement vs API works out the break-even point for your token volume.
Questions fréquemment posées
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.
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 données des modèles d'IA ou le Classement des grands modèles linguistiques (LLM).
