Qwen3 8B — Spécifications
| Développeur | Alibaba |
|---|---|
| Type | LLM (dense) |
| Modalité | Texte → Texte |
| Paramètres | 8B |
| Fenêtre de contexte | 128 K |
| Sortie maximale | — |
| Licence | Apache 2.0 (ouverte) |
| Poids ouverts | Oui |
| Publié | 2025 |
| Prix de l’entrée | 0,04 $ / 1 million |
| Prix de la sortie | 0,14 $ / 1 million |
| Fournisseurs d'API | Alibaba, OpenRouter, Ollama |
Exécutez-le localement
| VRAM (4 bits) | ~5 Go |
|---|---|
| GPU minimal requis | RTX 3060 8 Go / toute carte graphique 8 Go |
What is Qwen3 8B?
Qwen3 8B is a small, fast dense model from the Qwen3 family — Apache 2.0, a 128K context,
and about 5 GB of VRAM at 4-bit, which fits an RTX 3060 8GB or any 8 GB card. Hosted pricing
is $0.04 in / $0.14 out per million tokens.
Among models that fit an 8 GB GPU, it is one of the strongest available, and the 128K
context is what separates it from the older generation in the same bracket — Mistral 7B, its
closest historical equivalent, tops out at 32K. That difference decides whether a local
assistant can hold a real document or only a few pages. Apache 2.0 licensing means no
conditions to clear with legal, and the small footprint leaves room on the card for a
generous context or a second process. Treat it as the entry point to local inference: it will
run on hardware you almost certainly already have, and if it proves the use case, Qwen3 14B
and 32B are drop-in upgrades within the same family and licence as your hardware
allows.
Qwen3 8B pricing: API cost per 1M tokens
| Entrée (par million de jetons) | $0.0400 |
|---|---|
| Sortie (par million de jetons) | $0.140 |
| Output/input ratio | 3.5× |
| Blended (4:1 in:out) | $0.0600 per 1M tokens |
What Qwen3 8B 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 | $0.08 |
| Petite équipe | 20 millions en entrée / 5 millions en sortie | $1.50 |
| Production | 200 millions en entrée / 50 millions en sortie | $15 |
Run your own numbers in the Calculateur de coûts des API IA.
Cheaper alternatives to Qwen3 8B
| Modèle | Coût combiné par million de dollars | You save |
|---|---|---|
| Mistral 7B ouverte | $0.0220 | 63% cheaper |
| Llama 3.1 8B ouverte | $0.0220 | 63% cheaper |
| Mistral NeMo 12B ouverte | $0.0240 | 60% cheaper |
Self-host or pay the API?
Qwen3 8B is open-weight, so you can run it yourself. It needs ~5 Go of VRAM at 4-bit (RTX 3060 8GB / any 8GB GPU). 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 8B cost per 1M tokens?
Qwen3 8B costs $0.0400 per 1M input tokens and $0.140 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.0600 per 1M tokens.
How much does Qwen3 8B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $1.50 on Qwen3 8B. A side project (1M in / 0.25M out) costs roughly $0.08.
What is a cheaper alternative to Qwen3 8B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 63% less than Qwen3 8B. It is also open-weight, so self-hosting is an option.
Can I run Qwen3 8B locally?
Yes. Qwen3 8B is open-weight and needs about ~5 GB of VRAM at 4-bit quantisation (RTX 3060 8GB / any 8GB GPU).
Why does Qwen3 8B 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 8B 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 données des modèles d'IA ou le Classement des grands modèles linguistiques (LLM).
