Alibaba

Wan 2.2 A14BOpen

Alibaba’s open-weight MoE video-diffusion model — cinematic 1080p text-to-video and image-to-video.

#33released Sep 15, 2026·Open Weights·27B (14B active MoE)
textvideo64K ctx
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)
Coding (15)Mathematics (15)Hard Science (GPQA) (35)Agentic & Tool Use (20)Knowledge (MMLU) (55)Vision / Multimodal (96)

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 key
curl 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."}]
  }'

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
View providers matrix →

Scores by Source

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

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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

Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.

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Wan 2.2 A14B LLMPodium Score
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