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Alibaba: Wan 2.6

alibaba/wan-2.6

BenchmarksCompare

Alibaba's most advanced video generation model, supporting over 10 visual creation capabilities in a unified system. Wan 2.6 generates 1080p video at 24fps from text, images, reference videos, or audio, with native audio-visual synchronization and precise lip-sync. Key features include reference-to-video (insert a character's appearance and voice into new scenes), multi-shot storytelling from simple prompts, synchronized sound effects and music, and support for 16:9, 9:16, and 1:1 aspect ratios with clips up to 15 seconds.

Modalities
Price
from $0.04/second
Released
Mar 28, 2026
BenchmarksCompare
PlaygroundBenchmarksProvidersPerformanceUptimeAppsActivityFAQExplore

Playground

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for Alibaba: Wan 2.6 (Artificial Analysis)
SourceBenchmarkScore
Artificial AnalysisWan 2.6 Image Elo1,026
Artificial AnalysisWan2.6 Text to Image Elo1,017
Artificial AnalysisWan 2.6 Elo1,222

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

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$0.04$0.10135.4s
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End-to-end latency
135.42s

P50, best provider

Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
74.31%

Availability over the last 24 hours

OpenRouter Availability
85.29%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.

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Reelz
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alice-media
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0tokens
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Nelvio AI
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EXI - Exagon AI
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Simplabots
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Frequently asked questions

Alibaba's most advanced video generation model, supporting over 10 visual creation capabilities in a unified system. Wan 2.6 generates 1080p video at 24fps from text, images, reference videos, or audio, with native audio-visual synchronization and precise lip-sync.

Wan 2.6 costs from $0.04/second at 480p to $0.12/second at 1080p for Text to Video and from $0.10/second at 720p to $0.15/second at 1080p for Image to Video.

Wan 2.6 supports 5 and 10 second clips, can be steered with a first frame image, and produces a matching audio track.

Wan 2.6 accepts text and images as input and returns video.

Wan 3.0 Prime, Wan 3.0, HappyHorse 1.1 and 2 more are other video models from Alibaba.

Wan 2.6 was released on March 28, 2026.

More models from alibaba

Wan 3.0 Prime

Wan 3.0 Prime is a fast-mode variant of Wan 3.0 from Alibaba. It supports text-to-video and first-frame image-to-video generation.

Videofrom $0.068/second
Wan 3.0

Wan 3.0 is a video generation model from Alibaba for text-to-video, image-to-video, and reference-guided video generation. It produces 480p, 720p, or 1080p video with durations from 2 to 30 seconds.

Videofrom $0.0425/second
HappyHorse 1.1

HappyHorse 1.1 is a video generation model from Alibaba. It generates short videos from a text prompt, a single starting image, or a set of reference images, with output up to 1080p and durations of 3 to 15 seconds. It is suited for creative content, social media clips, and image-driven animation, and improves on the prior version with stronger prompt adherence, smoother motion, and more consistent characters across frames.

Videofrom $0.0988/second
HappyHorse 1.0

HappyHorse 1.0 is a video generation model from Alibaba. It generates short videos from a text prompt, a single starting image, or a set of reference images, with output up to 1080p and durations of 3 to 15 seconds. It is suited for creative content, social media clips, and image-driven animation across a range of aspect ratios.

Videofrom $0.0988/second
Wan 2.7

Wan 2.7 is a video generation model from Alibaba. It supports text-to-video, image-to-video with first and last frame control, and reference-to-video, where multiple reference images guide the style and content of the generated scene.

Video$0.10/second
Tongyi DeepResearch 30B A3B

Tongyi DeepResearch is an agentic large language model developed by Tongyi Lab, with 30 billion total parameters activating only 3 billion per token. It's optimized for long-horizon, deep information-seeking tasks and delivers state-of-the-art performance on benchmarks like Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch, and FRAMES. This makes it superior for complex agentic search, reasoning, and multi-step problem-solving compared to prior models.

The model includes a fully automated synthetic data pipeline for scalable pre-training, fine-tuning, and reinforcement learning. It uses large-scale continual pre-training on diverse agentic data to boost reasoning and stay fresh. It also features end-to-end on-policy RL with a customized Group Relative Policy Optimization, including token-level gradients and negative sample filtering for stable training. The model supports ReAct for core ability checks and an IterResearch-based 'Heavy' mode for max performance through test-time scaling. It's ideal for advanced research agents, tool use, and heavy inference workflows.

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