Qwen3 235B-A22B — Spezifikationen
| Entwickler | Alibaba |
|---|---|
| Typ | LLM (MoE) |
| Modality | Text → Text |
| Parameter | 235 Mrd. insgesamt / 22 Mrd. aktiv (MoE) |
| Kontextfenster | 128 K |
| Maximale Ausgabe | — |
| Lizenz | Apache 2.0 (offen) |
| Offene Gewichte | Ja |
| Veröffentlicht | 2025 |
| Eingabepreis | 0,45 $ pro 1 Million |
| Ausgabepreis | 1,80 $ pro 1 Million |
| API-Anbieter | Alibaba, OpenRouter |
Lokal ausführen
| VRAM (4-Bit) | ~140 GB |
|---|---|
| Mindest-GPU | Mehrere GPUs oder Mac mit 192 GB |
What is Qwen3 235B-A22B?
Qwen3 235B-A22B is the open flagship of Alibaba’s Qwen3 family — a 235B mixture-of-experts
activating 22B parameters per token, released under Apache 2.0 with a 128K context. Pricing
is $0.45 in / $1.80 out per million tokens; self-hosting needs around 140 GB at 4-bit, which
means a multi-GPU setup or a 192 GB Mac.
The Mac line is not a curiosity. Apple’s unified memory architecture lets a single
workstation address far more model memory than any consumer GPU, so a 192 GB Mac Studio can
hold a 235B model that would otherwise require a multi-GPU server — at a fraction of the
power draw, noise and facilities cost. For a small team that needs frontier-adjacent open
weights on-premises, that is often the only realistic path, and Qwen3 235B-A22B is one of the
strongest models that fits it. Apache 2.0 licensing makes it cleaner to adopt than the Llama 4
models, which carry EU restrictions. The 128K context is narrower than the 1M offered by
newer flagships, which is the main trade-off to weigh.
Qwen3 235B-A22B pricing: API cost per 1M tokens
| Eingabe (pro 1 Mio. Token) | $0.450 |
|---|---|
| Ausgabe (pro 1 Mio. Token) | $1.80 |
| Output/input ratio | 4× |
| Blended (4:1 in:out) | $0.720 per 1M tokens |
What Qwen3 235B-A22B costs per month
Real monthly spend at a 4:1 input-to-output mix — the ratio a typical chat or RAG workload actually produces.
| Workload | Tokens/Monat | Cost / month |
|---|---|---|
| Nebenprojekt | 1 Mio. Eingabe / 0,25 Mio. Ausgabe | $0.90 |
| Kleines Team | 20 Mio. Eingabe / 5 Mio. Ausgabe | $18 |
| Produktion | 200 Mio. Eingabe / 50 Mio. Ausgabe | $180 |
Run your own numbers in the KI-API-Kostenrechner.
Cheaper alternatives to Qwen3 235B-A22B
| Modell | Gewichteter Preis pro Million US-Dollar | You save |
|---|---|---|
| DeepSeek V4-Pro offenere | $0.522 | 28% cheaper |
| DeepSeek V4-Flash offenere | $0.168 | 77% cheaper |
| Llama 4 Maverick offenere | $0.240 | 67 % günstiger |
Self-host or pay the API?
Qwen3 235B-A22B is open-weight, so you can run it yourself. It needs ~140 GB of VRAM at 4-bit (Multi-GPU or Mac 192GB). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the Selbsthosting-vs.-API-Rechner works out the break-even point for your token volume.
Häufig gestellte Fragen
How much does Qwen3 235B-A22B cost per 1M tokens?
Qwen3 235B-A22B costs $0.450 per 1M input tokens and $1.80 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.720 per 1M tokens.
How much does Qwen3 235B-A22B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $18 on Qwen3 235B-A22B. A side project (1M in / 0.25M out) costs roughly $0.90.
What is a cheaper alternative to Qwen3 235B-A22B?
DeepSeek V4-Pro is the strongest cheaper option in our database at $0.522 per 1M blended — about 28% less than Qwen3 235B-A22B. It is also open-weight, so self-hosting is an option.
Can I run Qwen3 235B-A22B locally?
Yes. Qwen3 235B-A22B is open-weight and needs about ~140 GB of VRAM at 4-bit quantisation (Multi-GPU or Mac 192GB).
Why does Qwen3 235B-A22B 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 235B-A22B charges 4× 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 Datenbank für KI-Modelle oder das LLM-Leaderboard.
