Alibaba
Wan 2.2 A14BOpen
Alibaba’s open-weight MoE video-diffusion model — cinematic 1080p text-to-video and image-to-video.
Intelligence
87.4 #33
AAQI 87.0
Output Speed
15 t/s
ITL 66.7 ms/tok
Blended price (3:1)
$0.06 /1M
In $0.06/M · Out $0.06/M
Context
64K
≈ 160 A4 pages · max out 2,048
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Wan 2.2 A14B
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- 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
Trade-offs to watch
- Multi-second clips only — no long-form generation
- Slow sampling — minutes per clip without distillation
- No native audio track
Killer Use Cases
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.
Developer Quickstart & API
Set
DASHSCOPE_API_KEY first — get an API keycurl 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);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Head-to-head Live Matchups
Live Specs & Architecture
Under the hood
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
Release date
Sep 15, 2026
Architecture
Wan 2.2 A14B — MoE video diffusion, 14B active
Parameters
27B (14B active MoE)
License
Open Weights
Context Window
64,000 tokens
Max Output
2,048 tokens
Modalities
text + video
Cache Pricing
read $0.01 · write $0.08 /1M
Reasoning
—
Input Price
$0.06/M
Output Price
$0.06/M
Output Speed
15 t/s
Time to First Token
—
Data Updated
Oct 22, 2026
Hardware & Local Inference
Data: Convly AI (CC BY 4.0) ↗VRAM (4-bit Q4)
18 GB
Min GPU
RTX 4090 24GB / RTX 5090 32GB
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
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}
}Embed Score Badge
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