Alibaba's newly open - sourced large - scale video generation model supports tasks such as text - to - video, image - to - video, and text - image - to - video. It uses the MoE architecture and has movie - level aesthetics and the ability to generate high - efficiency 720P@24fps videos.
Intelligence(Weak)
Speed(Slow)
Input Supported Modalities
Yes
Is Reasoning Model
131,072
Context Window
8,192
Maximum Output Tokens
-
Knowledge Cutoff

Pricing

- /M tokens
Input
- /M tokens
Output
- /M tokens
Blended Price

Quick Simple Comparison

Input

Output

Qwen2.5 Turbo
Qwen Turbo
Wan2.2

Basic Parameters

Wan2.2Technical Parameters
Parameter Count
Not Announced
Context Length
131.07k tokens
Training Data Cutoff
Open Source Category
Open Source
Multimodal Support
Text, Image
Throughput
0
Release Date
2025-07-28
Response Speed
0 tokens/s

Benchmark Scores

Below is the performance of Wan2.2 in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
-
Large Language Model Intelligence Level
Coding Index
-
Indicator of AI model performance on coding tasks
Math Index
-
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
-
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
-
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
-
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
-
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
-
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
-
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
-
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
-
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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