Qwen: Qwen3.5 397B A17B
ModelPaidThe Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...
Capabilities7 decomposed
multimodal text-image-video understanding with linear attention
Medium confidenceProcesses text, images, and video inputs through a unified vision-language model architecture that combines linear attention mechanisms with sparse mixture-of-experts routing. The linear attention reduces computational complexity from quadratic to linear in sequence length, enabling efficient processing of long contexts and high-resolution visual inputs without the quadratic memory overhead of standard transformer attention.
Hybrid architecture combining linear attention (O(n) complexity vs O(n²) for standard transformers) with sparse mixture-of-experts routing, enabling efficient processing of long multimodal sequences while maintaining model capacity through conditional expert activation
Achieves higher inference efficiency than dense vision-language models like GPT-4V or Claude 3.5 Vision through linear attention and sparse routing, reducing latency and computational cost while maintaining multimodal understanding capabilities
sparse mixture-of-experts conditional computation routing
Medium confidenceRoutes input tokens through a sparse mixture-of-experts layer where only a subset of expert networks activate per token based on learned routing decisions. This conditional computation pattern reduces per-token inference cost compared to dense models where all parameters process every token, enabling the 397B parameter model to achieve inference efficiency closer to much smaller dense models.
Implements sparse MoE with learned routing gates that selectively activate expert subnetworks per token, reducing active parameter count during inference while maintaining 397B total capacity for diverse task specialization
More efficient than dense 397B models (which activate all parameters per token) and more capable than smaller dense models of equivalent inference cost, through conditional expert activation
long-context multimodal sequence processing
Medium confidenceProcesses extended sequences combining text, images, and video through linear attention mechanisms that scale linearly rather than quadratically with sequence length. This enables handling of long documents with embedded visuals, multi-turn conversations with image history, and video analysis with detailed frame-by-frame reasoning without the memory constraints of quadratic attention.
Linear attention mechanism scales O(n) instead of O(n²), enabling practical processing of long multimodal sequences that would exceed memory limits in standard transformer architectures
Handles longer multimodal contexts than GPT-4V or Claude 3.5 Vision without quadratic memory scaling, enabling use cases like full-document analysis with embedded visuals
native vision-language unified representation
Medium confidenceProcesses images and text through a unified embedding space where visual and textual information are represented in the same latent space, enabling direct cross-modal reasoning without separate vision and language encoders. This native integration allows the model to reason about relationships between visual and textual content at the representation level rather than through post-hoc fusion.
Native vision-language architecture with unified embedding space rather than separate vision/language encoders, enabling direct cross-modal reasoning in the shared latent space
Deeper visual-textual integration than models using separate vision encoders (like CLIP-based approaches), potentially enabling more nuanced multimodal understanding
inference-time efficient parameter utilization
Medium confidenceAchieves 397B parameter capacity while maintaining inference efficiency through sparse mixture-of-experts routing that activates only a fraction of parameters per forward pass. The model dynamically selects which expert networks process each token based on learned routing decisions, reducing the effective active parameter count during inference compared to dense models where all parameters are always active.
Combines 397B parameter capacity with sparse MoE routing to achieve inference efficiency where only a subset of parameters activate per token, reducing per-token compute cost relative to dense models of similar capacity
More cost-efficient inference than dense 397B models while maintaining greater capacity than smaller dense models of equivalent inference cost
video frame-level temporal understanding
Medium confidenceProcesses video inputs by analyzing individual frames and their temporal relationships through the unified vision-language architecture. The model can reason about motion, scene changes, and temporal sequences by processing video as a series of visual inputs with implicit temporal context, enabling understanding of video content beyond single-frame analysis.
Processes video through unified vision-language architecture enabling temporal understanding across frames without explicit temporal modeling layers, treating video as a sequence of visual inputs with implicit temporal context
Enables video understanding through the same multimodal model as image understanding, avoiding separate video-specific encoders and enabling unified reasoning across static and dynamic visual content
api-based inference with openrouter integration
Medium confidenceProvides access to the Qwen3.5 397B model through OpenRouter's API infrastructure, handling model serving, load balancing, and request routing. The integration abstracts away infrastructure management and provides standardized API endpoints for text, image, and video inputs with response streaming support and usage tracking.
Provides managed API access to Qwen3.5 through OpenRouter's infrastructure, handling model serving, load balancing, and request routing without requiring local deployment
Easier deployment than self-hosting (no GPU infrastructure needed) while maintaining lower latency than some cloud alternatives through OpenRouter's optimized routing
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Qwen: Qwen3.5 397B A17B, ranked by overlap. Discovered automatically through the match graph.
Qwen: Qwen3.5-Flash
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...
Meta: Llama 4 Maverick
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Z.ai: GLM 4.5V
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...
Qwen: Qwen3.5 Plus 2026-02-15
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...
Qwen: Qwen3.5-122B-A10B
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...
Qwen: Qwen3.5-35B-A3B
The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...
Best For
- ✓teams building multimodal AI applications requiring efficient inference
- ✓developers processing video analysis pipelines with text annotations
- ✓enterprises needing cost-effective vision-language understanding at scale
- ✓cost-conscious teams running high-volume inference workloads
- ✓developers optimizing for latency-sensitive applications
- ✓researchers studying conditional computation and expert specialization
- ✓document analysis platforms processing PDFs with images and tables
- ✓conversational AI systems with visual context history
Known Limitations
- ⚠Linear attention may have different quality characteristics than standard attention for certain fine-grained visual reasoning tasks
- ⚠Sparse MoE routing adds conditional computation overhead that varies based on input characteristics
- ⚠No information available on maximum supported image resolution or video frame count per request
- ⚠Sparse routing decisions are non-deterministic and may vary slightly across inference runs
- ⚠Expert load balancing may be suboptimal for certain input distributions, causing uneven compute utilization
- ⚠No visibility into which experts activate for specific inputs through the API
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
Model Details
About
The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...
Categories
Alternatives to Qwen: Qwen3.5 397B A17B
Are you the builder of Qwen: Qwen3.5 397B A17B?
Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.
Get the weekly brief
New tools, rising stars, and what's actually worth your time. No spam.
Data Sources
Looking for something else?
Search →