Qwen3.8-2.4T-A95B

What Is Qwen3.8-2.4T-A95B?

Qwen3.8-2.4T-A95B is the largest open-weight model in Alibaba's Qwen3.8 series — a Mixture-of-Experts (MoE) large language model with 2.4 trillion total parameters and 95 billion active per token. It is the first time Alibaba's Qwen team has released a Qwen-Max-class model as open weights, hosted on Hugging Face for the research and developer community.

What It's Good For

Qwen3.8-2.4T-A95B is purpose-built for tasks that demand deep, multi-step reasoning. Because it always runs in thinking mode — producing explicit chain-of-thought reasoning before each final answer — it is particularly well-suited for:

• Complex coding tasks: Writing, debugging, and reviewing code where thorough analysis matters. The model can be used directly with frameworks like vLLM and SGLang to serve an OpenAI-compatible Chat Completions API.
• Research and analytical work: Synthesizing information, solving mathematical problems, and generating detailed technical explanations.
• Long-horizon agentic tasks: Built on the Qwen3.5 architectural foundation, the model is designed to carry multi-step tasks through to completion with greater reliability than prior generations.
• RAG and chatbot systems: Developers can integrate it into retrieval-augmented generation pipelines, AI agent frameworks, and conversational applications.

The model also supports a reasoning_effort parameter (xhigh, medium, or low) so developers can trade off inference cost against reasoning depth depending on task complexity. Inference is compatible with vLLM, SGLang, and TokenSpeed, and a managed option is available via Qwen Cloud. For teams that prefer self-hosting, quantized variants — including FP8, GGUF, and NVFP4 — are available from the community.

If you need a comparable open model for coding or reasoning tasks, DeepSeek and Llama 4 are alternative open-weight options worth evaluating alongside this model.

Limitations and Where It Falls Short

• Text-only: The open-weight release does not support image, audio, or other multimodal inputs. Vision capabilities are reserved for the cloud-hosted Qwen3.8-Max variant.
• Thinking always on: Unlike some other models in the Qwen family, thinking mode cannot be disabled — every response includes a reasoning trace, which adds latency and token overhead even for simple queries.
• Context window: The native context length is 262,144 tokens. The 1M context length available in Qwen3.8-Max is not part of the open-weight release.
• Heavy hardware requirements: At 2.4T total parameters, self-hosting this model requires substantial GPU infrastructure. Most teams will need to rely on quantized variants or managed inference rather than running the full-precision model directly.