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LatestQwen · Alibaba3.5
Qwen · AlibabaVision-Language

Qwen Releases Flagship 122B Multimodal MoE Model

The new Qwen3.5-122B-A10B combines a massive parameter count with an efficient Mixture-of-Experts architecture for advanced vision and language tasks.

Feb 24, 2026
Major releaseApache 2.0
Qwen3.8-27B

Alibaba's Qwen team has unveiled Qwen3.5-122B-A10B, the flagship model in its new Qwen3.5 series. This release marks a significant entry into the high-end open-source model space, targeting complex multimodal tasks with a powerful and efficient architecture.

The model's main innovation is its scale combined with its structure. It is a 122-billion-parameter Mixture-of-Experts (MoE) model, but only activates 10 billion parameters for any given input. This design provides the knowledge capacity of a very large model while keeping the computational cost for inference relatively low, making it more accessible than a dense model of a similar size.

A Vision-Language Specialist

As a vision-language model (VLM), Qwen3.5-122B-A10B is designed to understand and process information from both images and text simultaneously. This makes it suitable for a wide range of applications, including visual question answering, image captioning, and object recognition within a broader textual context.

In keeping with the project's commitment to open-source development, the model is available under the permissive Apache 2.0 license, which allows for both academic and commercial use. Developers can access the model weights and explore its capabilities on the Hugging Face Hub.

Sources

  • Qwen/Qwen3.5-122B-A10B

    Hugging Face

    Visit
OlderQwen Releases 27B Vision Model with Long ContextQwen · Alibaba · Vision-Language · 8 months agoNewerMoonshine: Open STT Models Aim to Beat WhisperResemble AI · Speech → Text · 8 months ago

Get the model

Hugging Face

Specs

Parameters122B · MoE
Active params10B active
Context window262K tokens
Size250.2 GB
PrecisionBF16
ArchitectureQwen3_5MoeForConditionalGeneration
LicenseAPACHE-2.0
Downloads4.6M
Likes3.1K

Can you run it?

Needs a workstation GPU (96 GB) at 4-bit.

  • BF16 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    252 GB
  • 8-bit

    2×H100 80 GB · 256 GB Mac Studio

    134.3 GB
  • 4-bit

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    76.8 GB
Your machine
  • Mixture-of-experts: all 125B parameters must be resident even though only 10B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Vision-LanguageText / LLM
10 versions — view changelog

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