Alibaba

Wan 2.2 A14BTerbuka

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

#33dirilis 15 Sep 2026·Open Weights·27B (14B active MoE)
textvideo64K ctx
Penyedia APIDapatkan Kunci API →Peringkat
Inteligensi
87.4 #33
AAQI 87.0
Kecepatan output
15 t/s
ITL 66.7 ms/tok
Harga gabungan (3:1)
$0.06 /1M
Input $0.06/M · Output $0.06/M
Konteks
64K
≈ 160 halaman A4 · output maks 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)

Sumbu radar diaproksimasi dari skor kategori jika pengukuran benchmark langsung tidak tersedia — lihat Metodologi.

Kelebihan & Trade-off

Di mana ia menang

  • 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

Hal yang perlu diperhatikan

  • Multi-second clips only — no long-form generation
  • Slow sampling — minutes per clip without distillation
  • No native audio track

Kasus Penggunaan 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 Developer & API

Atur DASHSCOPE_API_KEY dulu — dapatkan kunci API
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."}]
  }'

Sumber Daya & Tautan Resmi

Deployment API

Bandingkan host & latensi

Throughput, TTFT, dan harga cache di Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock, dan Azure.

GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Lihat matriks penyedia →

Skor per sumber

Spesifikasi Live & Arsitektur

Di balik kap
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
Tanggal rilis
15 Sep 2026
Arsitektur
Wan 2.2 A14B — MoE video diffusion, 14B active
Parameter
27B (14B active MoE)
Lisensi
Open Weights
Jendela konteks
64,000 tokens
Output maks
2,048 tokens
Modalitas
text + video
Harga cache
baca $0.01 · tulis $0.08 /1M
Penalaran
—
Harga input
$0.06/M
Harga output
$0.06/M
Kecepatan output
15 t/s
Waktu ke token pertama
—
Data diperbarui
22 Okt 2026
Perangkat Keras & Inferensi Lokal
Data: Convly AI (CC BY 4.0) ↗
VRAM (4-bit Q4)
18 GB
GPU Min.
RTX 4090 24GB / RTX 5090 32GB

Estimasi tagihan bulanan

Estimasi pengeluaran bulanan
$0.00
Menghitung penghematan vs GPT-4o…

Kutip & sematkan

Badge Markdown GitHub
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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}
}

Sematkan Lencana Skor

Tambahkan lencana Podium Score langsung ke README GitHub atau Hugging Face Anda — menautkan ke peringkat langsung.

Pratinjau
Wan 2.2 A14B LLMPodium Score
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