Qwen3.5-397B-A17B

Our Take

A strong 397B-parameter MoE language model from Alibaba. Pulls ahead on competition math (AIME 2026) (93/100), so reach for it when that's the dimension that matters.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

Unlike traditional Transformers that rely solely on standard attention mechanisms, Qwen3.5 integrates Gated DeltaNet. This hybrid approach improves the model's ability to handle its massive 262,144 token native context window. For specialized long-form tasks, the model supports up to 1 million tokens via YaRN (Yet another RoPE extension), making it capable of "reading" entire codebases or multi-hundred-page technical manuals in a single prompt.

• Total Parameters: 397B (Determines VRAM requirements)
• Active Parameters: 17B (Determines inference speed/latency)
• Modality: Native Multimodal (Vision-Language-Action)
• Training Cutoff: 2025

The 17B active parameter count means that once the model is loaded into memory, the Qwen3.5-397B-A17B tokens per second (TPS) performance is remarkably high, often exceeding that of much smaller dense models like Llama 3 70B, provided the hardware interface has sufficient memory bandwidth.

Qwen3.5-397B-A17B is a general-purpose powerhouse with specific optimizations for technical and logical workloads. Its Qwen3.5-397B-A17B reasoning benchmark scores place it at the top of the open-weights leaderboard, particularly in math and symbolic logic.

For developers, Qwen3.5-397B-A17B for coding excels at multi-file architecture planning and debugging. Unlike smaller models that struggle with state management across large files, the 262K context window allows it to maintain a coherent understanding of complex dependencies. It supports modern programming languages and can generate production-ready boilerplate, unit tests, and documentation.

The vision capabilities are natively integrated, not bolted on. This allows for:

• Complex OCR: Extracting data from dense financial spreadsheets or handwritten notes.
• Spatial Reasoning: Analyzing architectural blueprints or circuit diagrams.
• Visual Function-Calling: Interpreting a UI screenshot to suggest the next sequence of API calls.

With support for 201 languages, the model is uniquely suited for global enterprise applications. It maintains high instruction-following accuracy even in low-resource languages, making it a preferred choice for local translation and summarization pipelines that require high nuance.

The primary challenge for any practitioner is the Qwen3.5-397B-A17B hardware requirements. Because the model has 397 billion parameters, the VRAM footprint is the most significant bottleneck for local execution.

To run Qwen3.5-397B-A17B locally, you must select a quantization level that fits your available memory. Running in full FP16 is impractical for most, requiring nearly 800GB of VRAM.

The best quantization for Qwen3.5-397B-A17B is generally Q4_K_M. This provides a near-lossless experience compared to FP16 while bringing the memory requirement down to a range manageable by high-end workstation clusters.

The best GPU for Qwen3.5-397B-A17B depends on your budget and form factor:

• Consumer Multi-GPU: A rig with 10x RTX 3090/4090 GPUs using NVLink or high-speed PCIe lanes. This is the most cost-effective way to reach the ~240GB VRAM threshold for 4-bit quantization.
• Mac Silicon: An Apple M4 Ultra (or M2/M3 Ultra) with 192GB of Unified Memory is the most "plug-and-play" solution, though it will likely necessitate using a lower quantization (Q2_K or Q3_K_L) to fit the model and a sizable KV cache.
• Professional Grade: 4x NVIDIA A100 (80GB) or H100 (80GB) cards provide the necessary headroom and memory bandwidth to achieve maximum tokens per second.

Ollama is the fastest way to get started, as it handles the MoE logic and memory mapping automatically. Use the command ollama run qwen3.5:397b (assuming you have the required VRAM). For more granular control over layers and GPU offloading, llama.cpp or vLLM are recommended.

When evaluating this model against its peers, the distinction usually comes down to the MoE architecture versus dense scaling.

Llama 3.1 405B is a dense model, meaning every parameter is active for every token.

• Performance: Qwen3.5-397B-A17B is significantly faster in terms of throughput (TPS) because it only activates 17B parameters.
• Hardware: Both require similar VRAM (~230GB+ for 4-bit), but Llama 405B requires significantly more compute power (TFLOPS) to generate text at the same speed.
• Context: Qwen3.5 offers a native 262K context, whereas Llama 3.1 405B is capped at 128K.

DeepSeek-V3 is a fellow MoE model that pioneered many of the efficiencies Qwen3.5 utilizes.

• Multimodality: Qwen3.5-397B-A17B has a slight edge in native vision-language tasks and broader multilingual support (201 languages vs DeepSeek's focus on English/Chinese).
• Inference: Both models offer excellent MoE efficiency, making them the two most viable "megamodels" for local practitioners who can afford the VRAM but don't want to wait seconds for a single token.

For practitioners looking for a local AI model 397B parameters 2025 edition, Qwen3.5-397B-A17B is currently the most versatile option for those who need a single model to handle vision, coding, and massive context windows without the latency penalties of dense 400B architectures.