- Text Generation
Qwen3.5 Plus 2026-02-15 is a conversational AI model from Qwen, released on February 15, 2026, designed for general-purpose reasoning and assistance. It is positioned as a…
Powered by Alibaba
Wan 2.6
- Text Generation
Wan 2.6 is Alibaba’s advanced multimodal generative model for high-quality short-form video (and related image) creation, featuring multi-shot storytelling, 1080p output, and native audio‑visual synchronization.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Wan 2.6?
Wan 2.6 is a next-generation AI video generation model from Alibaba’s Wan/Tongyi labs designed for professional-quality, multimodal video creation from text and reference inputs. It is mainly used for text-to-video and image-to-video generation of short cinematic clips such as ads, social media content, and narrative scenes, supporting multi-shot sequences, character consistency, and AV-synced dialogue. It is also used in creative and production workflows via cloud and third-party platforms that expose Wan 2.6 for tasks like branded content, virtual characters, and automated video pipelines. Wan 2.6 belongs to Alibaba’s Wan (Wan AI) model family as an evolution of earlier Wan 2.x video models.
Model capabilities
5 Core Capabilities
-
Text-to-video
Generates short high-quality cinematic videos directly from text prompts, supporting multiple aspect ratios and up to around 10–15 seconds.
-
Image-to-video
Transforms a single reference image or first frame into a temporally consistent motion video while preserving layout, style, and composition.
-
Reference role-play
Uses reference videos or images to insert consistent character appearance and, in R2V variants, matching voice into new generated scenes.
-
Multi-shot storytelling
Automatically breaks prompts into coherent multi-shot narratives, stitching wide, medium, and close-up shots into smooth cinematic sequences.
-
Native audio sync
Generates videos with built-in audio, including speech, music, and effects, maintaining close audio-visual alignment and lip synchronization.
Use cases
6 Most Valuable Use Cases
- Short ad creation
- Social media clips
- Scripted short dramas
- Virtual avatar videos
- Reference-based roleplay
- Automated video workflows
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and fastest access for Wan 2.6–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~140ms | ~220 tps | 99.99% | $0.20 | $0.20 | 256K |
| Alibaba Cloud | APAC | ~220ms | ~150 tps | 99.95% | ~$0.60 | ~$0.60 | 128K |
| OpenAI (closest equivalent) | Global | ~180ms | ~180 tps | 99.9% | ~$1.00 | ~$4.00 | 128K |
| Azure AI (closest equivalent) | US East | ~200ms | ~160 tps | 99.9% | ~$1.10 | ~$4.40 | 128K |
| Google Cloud (closest equivalent) | Global | ~190ms | ~170 tps | 99.9% | ~$0.90 | ~$3.60 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Wan 2.6 (Alibaba) | Qwen2-72B-Instruct (Alibaba) | Llama 3 70B Instruct (Meta) |
|---|---|---|---|
| Avg Latency | ~220ms | ~260ms | ~280ms |
| Context Window | 128K | 128K | 8K |
| Input Price ($/1M tokens) | $0.80 | $0.60 | $1.00 |
| Output Price ($/1M tokens) | $1.60 | $1.20 | $2.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 80 tps | 70 tps | 65 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 9.4B
- Prompt tokens processed (30 days)
- 6.1B
- Completion tokens generated (30 days)
- 12.5M
- API requests served (30 days)
- 99.8%
- Average API uptime (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Automatically route each request to the optimal model and provider based on latency, reliability, and capabilities—without changing your integration or redeploying.
One endpoint. Any model. -
Cost-Aware Orchestration
Balance quality and price with dynamic cost controls, tiered model selection, and per-project limits so teams can ship faster without surprise bills.
Control spend by design. -
Resilient Fallback Flows
Define automatic fallbacks across providers and models so your apps stay online when APIs fail, throttle, or degrade—no manual incident wiring required.
Stay up, even when they’re down. -
Deep LLM Observability
Get tracing, metrics, and structured logs for every LLM call to debug latency, failures, and quality issues across providers from a single pane.
See every token hop. -
Task-Level Abstractions
Describe tasks like chat, tools, or RAG once and let LLM.API translate them into provider-specific calls, schemas, and parameters behind the scenes.
Think tasks, not endpoints. -
High-Throughput Batch
Submit massive batches of prompts or jobs through one API and let LLM.API optimize concurrency, retries, and rate limits across providers automatically.
Scale requests, not ops.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a powerful, general-purpose Alibaba cloud-hosted model for Chinese-language applications.
- You need strong code generation and completion integrated into an Alibaba-centric tech stack.
- Your use case involves building multilingual chatbots serving both Chinese and English users.
- Your use case involves leveraging Alibaba Cloud AI services within existing enterprise infrastructure.
- You need an LLM from a major Chinese provider for regulatory or data-sovereignty reasons.
- Your use case involves experimentation with multiple Chinese foundation models for benchmarking and evaluation.
Avoid if...
- You need guaranteed top-tier performance on complex English reasoning versus frontier US models.
- Your workload requires tight integration with non-Alibaba cloud ecosystems and proprietary toolchains.
- You need extensive, well-documented third-party tooling, plugins, and community examples in English.
- Your workload requires proven, battle-tested support for highly sensitive Western compliance frameworks.
- You need long-term vendor neutrality and avoid lock-in to a single regional cloud provider.
- Your workload requires fully transparent training data documentation and openly published safety evaluations.
FAQ
Frequently Asked Questions
-
What is Wan 2.6?
Wan 2.6 is an Alibaba multimodal large language model optimized for high-quality text and image generation tasks.
-
What modalities does Wan 2.6 support?
Wan 2.6 supports both natural language text and image inputs and outputs for vision-language applications.
-
How do I access Wan 2.6 through LLM.API?
You call the unified LLM.API endpoint with the provider set to Alibaba and the model name set to Wan 2.6.
-
What is the context window of Wan 2.6?
Wan 2.6 supports up to a 32K token context window for prompts and conversation history.
-
How fast is Wan 2.6 in terms of latency?
Wan 2.6 typically returns first tokens within a few seconds, depending on prompt size and LLM.API routing conditions.
-
What is the pricing for using Wan 2.6 via LLM.API?
Wan 2.6 usage is billed by input and output tokens according to LLM.API’s Alibaba-specific pricing schedule.
-
What is Wan 2.6 particularly good at?
Wan 2.6 excels at detailed image understanding, image generation from text, and complex vision-language reasoning tasks.
-
How does Wan 2.6 compare to similar multimodal models?
Wan 2.6 targets competitive multimodal quality with a strong balance between capability, latency, and cost versus other general-purpose vision-language models.
-
What limitations does Wan 2.6 have?
Wan 2.6 can produce inaccurate or outdated information, struggle with very long multi-step reasoning, and may misinterpret ambiguous images or prompts.
-
Can I use Wan 2.6 for pure text-only applications?
Yes, Wan 2.6 can be used as a text-only model, though it is primarily optimized for multimodal scenarios.
Compare
Competitive Models
- Text Generation
multi-qa-mpnet-base-dot-v1 is a Sentence Transformers model that encodes sentences and paragraphs into 768-dimensional embeddings optimized for semantic search using dot-product similarity. It is trained on large-scale…
- Text Generation
Ministral 3 8B 2512 is Mistral AI’s compact 8B-parameter multimodal language model with text-and-image input, tool use, and a long 262K-token context window at low cost.…