Alibaba
Wan 2.2 A14BAperto
Alibaba’s open-weight MoE video-diffusion model — cinematic 1080p text-to-video and image-to-video.
Intelligenza
87.4 #33
AAQI 87.0
Velocità Uscita
15 t/s
ITL 66.7 ms/tok
Prezzo combinato (3:1)
$0.06 /1M
Input $0.06/M · Output $0.06/M
Contesto
64K
≈ 160 pagine A4 · output max 2,048
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Wan 2.2 A14B
Claude Mythos Preview (#1)
Average (68)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- Best open-weight video model — rivals Kling/Veo tiers
- Consumer-GPU serving — ~18GB at Q4
- Strong prompt adherence on complex motion
- Free to self-host and fine-tune
A cosa fare attenzione
- Multi-second clips only — no long-form generation
- Slow sampling — minutes per clip without distillation
- No native audio track
Casi d'uso killer
Marketing & product clips
Short cinematic b-roll and product loops from prompts.
Storyboarding pipelines
Rapid previz for film and ad concepts.
Self-hosted video APIs
Own the full generation stack — no per-clip vendor fees.
Quickstart per sviluppatori e API
Imposta prima
DASHSCOPE_API_KEY — ottieni una chiave APIcurl https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "wan2.2-t2v-a14b",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
api_key=os.environ["DASHSCOPE_API_KEY"],
)
resp = client.chat.completions.create(
model="wan2.2-t2v-a14b",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DASHSCOPE_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "wan2.2-t2v-a14b",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Specifiche live e architettura
Sotto il cofano
Wan 2.2 A14B — MoE video diffusion, 14B active
- High/low-noise expert pair — 14B active per denoising step
- Cinematic training corpus — film-grade lighting and camera control
- Text-to-video and image-to-video from one checkpoint
- Runs on a single 24GB consumer GPU at Q4
Data di Rilascio
15 set 2026
Architettura
Wan 2.2 A14B — MoE video diffusion, 14B active
Parametri
27B (14B active MoE)
Licenza
Open Weights
Finestra di Contesto
64,000 tokens
Output max
2,048 tokens
Modalità
text + video
Prezzi cache
lettura $0.01 · scrittura $0.08 /1M
Ragionamento
—
Prezzo Ingresso
$0.06/M
Prezzo Uscita
$0.06/M
Velocità Uscita
15 t/s
Tempo al Primo Token
—
Dati aggiornati
22 ott 2026
Hardware e inferenza locale
Dati: Convly AI (CC BY 4.0) ↗VRAM (4-bit Q4)
18 GB
GPU min.
RTX 4090 24GB / RTX 5090 32GB
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
Cita e incorpora
Badge Markdown GitHub
Loading badge markdown...BibTeX
@misc{llmpodium2026wan22a14b,
title = {Benchmark Analysis and Intelligence Evaluation of Wan 2.2 A14B},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/wan-2-2-a14b}
}Incorpora il badge del punteggio
Aggiungi un badge Podium Score live al tuo README su GitHub o Hugging Face — rimanda alla classifica live.
Anteprima
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Modelli alternativi
Migliori alternative a Wan 2.2 A14B in base a capacità, prezzi e benchmark.