Qwen3 8B — Spezifikationen
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Entwickler | Alibaba |
|---|---|
| Typ | LLM (dicht) |
| Modality | Text → Text |
| Parameter | 8B |
| Kontextfenster | 128 K |
| Lizenz | Apache 2.0 (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2025 |
| Eingabepreis | $0.04 /1M |
| Ausgabepreis | $0.14 /1M |
| API-Anbieter | Alibaba, OpenRouter, Ollama |
Lokal ausführen
| VRAM (4-Bit) | ~5 GB |
|---|---|
| Mindest-GPU | RTX 3060 8 GB / beliebige 8-GB-GPU |
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
| Eingabe (pro 1 Mio. Token) | $0.0400 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $0.140 |
| Verhältnis Output/Input | 3.5× |
| Gemischt (4:1 Input:Output) | $0.0600 pro 1 Mio. Tokens |
What Qwen3 8B costs per month
Tatsächliche monatliche Ausgaben bei einem 4:1-Input-zu-Output-Mix – dem Verhältnis, das typische Chat- oder RAG-Arbeitslasten tatsächlich erzeugen.
| Workload | Tokens/Monat | Kosten pro Monat |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $0.08 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $1.50 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $15 |
Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.
Cheaper alternatives to Qwen3 8B
| Modell | Gewichteter Preis pro Million US-Dollar | Sie sparen |
|---|---|---|
| Mistral 7B offenere | $0.0220 | 63 % günstiger |
| Llama 3.1 8B offenere | $0.0220 | 63 % günstiger |
| Mistral NeMo 12B offenere | $0.0240 | 60% cheaper |
Selbst hosten oder die API nutzen?
Qwen3 8B is open-weight, so you can run it yourself. It needs ~5 GB 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 Selbsthosting vs. API-Rechner berechnet den Break-even-Punkt für Ihr Token-Volumen.
Häufig gestellte Fragen
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.
Die Preise entsprechen den offiziell veröffentlichten Listenpreisen für die primäre API des jeweiligen Modells und werden regelmäßig aktualisiert, sobald Anbieter diese ändern. Volumen-, Batch- und Cached-Input-Rabatte sind nicht enthalten. Vergleichen Sie alle Modelle nebeneinander im Datenbank für KI-Modelle oder das LLM-Leaderboard.