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Qwen3 235B-A22B

Qwen3 235B-A22B — Spezifikationen

Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated

Entwickler Alibaba
Typ LLM (MoE)
Modality Text → Text
Parameter 235 Mrd. insgesamt / 22 Mrd. aktiv (MoE)
Kontextfenster 128 K
Lizenz Apache 2.0 (offen)
Offene Gewichte Ja
Veröffentlicht 2025
Eingabepreis $0.45 /1M
Ausgabepreis $1.8 /1M
API-Anbieter Alibaba, OpenRouter

Lokal ausführen

VRAM (4-Bit) ~140 GB
Mindest-GPU Mehrere GPUs oder Mac mit 192 GB

Offizielle Seite →

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
Verhältnis Output/Input
Gemischt (4:1 Input:Output)$0.720 pro 1 Mio. Tokens

What Qwen3 235B-A22B costs per month

Tatsächliche monatliche Ausgaben bei einem 4:1-Input-zu-Output-Mix – dem Verhältnis, das typische Chat- oder RAG-Arbeitslasten tatsächlich erzeugen.

WorkloadTokens/MonatKosten pro Monat
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

Stellen Sie Ihre eigenen Berechnungen im KI-API-Kostenrechner.

Cheaper alternatives to Qwen3 235B-A22B

ModellGewichteter Preis pro Million US-DollarSie sparen
DeepSeek V4-Pro offenere $0.522 28% cheaper
DeepSeek V4-Flash offenere $0.168 77% cheaper
Llama 4 Maverick offenere $0.320 56% cheaper

Selbst hosten oder die API nutzen?

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 berechnet den Break-even-Punkt für Ihr Token-Volumen.

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.

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.

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