Key points
- Release overview: On August 12, 2026 (Beijing time), the Qwen team publicly released the full weights of Qwen3.8-2.4T-A95B on the ModelScope community, with simultaneous availability on Hugging Face Transformers, vLLM, SGLang, and TokenSpeed. It is the first fully open Qwen-Max class model.
- Model specs: 2.4T total parameters, 95B activated per token, native 262,144 (256K) context window extendable to ~1,010,000 (1M) tokens. Text-only input and output. Mandatory "thinking" mode is enforced; no non-thinking option is provided.
- Comparative scale: Against frontier closed models, the open-vs-closed parameter gap reportedly shrinks from ~10x to ~1.5x. Comparable open/half-open peers include Moonshot Kimi K3 (2.8T / 104B activated, mainly distilled open release) and DeepSeek V4-Pro (1.6T / ~49B activated, partial architecture open). Closed peers at similar scale include Meta Llama 4 Behemoth (~2T), xAI Grok 4.6 (1.5T), Microsoft MAI-Thinking-1 (1T / 35B), and Anthropic Claude Fable 5 Max (parameters undisclosed).
- Mixture-of-Experts routing: 512 experts with 10 routed + 1 shared per token (11 active). Reported sparsity ~1/218, described as the densest expert pool but sparlest activation among Chinese open-source MoE models. Reference: Ling-3.0-flash 5.1B / 124B at 1/24 sparsity; Ling-3.0-tiny 1.3B / 7.9B; Kimi K3 256 experts / 12 active.
- Hybrid attention: 92 layers total, 69 linear-attention layers for KV-cache efficiency, 23 full-attention layers for precision. This design makes 256K / 1M context feasible under finite memory, since full attention alone cannot fit 1M-token KV cache in typical hardware.
- Multi-Token Prediction (MTP): Built-in MTP draft head accelerates inference and supports speculative decoding on vLLM, SGLang, and TokenSpeed.
- Vision input (image understanding)
- Non-thinking (direct-answer) mode
- Built-in tools (search, calculator, code execution)
- Default 1M context
- 8-08 NVIDIA Cosmos 3 foundation model
- 8-08 Unitree Robotics STAR Market IPO at 219x PE
- 8-09 Ant Ling-3.0-flash open-source (5.1B / 124B, 1/64 sparse)
- 8-11 Ant Ling-3.0-tiny open-source (1.3B / 7.9B)
- 8-12 Alibaba Qwen3.8-2.4T-A95B open-source
- 8-12 NVIDIA Nemotron 4T-parameter research leak
- Text-only; no native tool calling; vision/tool/non-thinking require Qwen3.8-Max cloud or community patches.
- Benchmark numbers (PaperBench, IFBench, etc.) are self-reported; independent third-party verification pending.
- 512-expert routing learning curve is unverified; community fine-tuning cost is non-trivial.
- Commercial license text has not been publicly disclosed beyond a referenced "Qwen3.8-specific license file."
- Hardware threshold blocks independent developers unless aggressive quantization is accepted.
- Total-parameter edge over Kimi K3 is marginal; Alibaba's advantage is full openness and enforced thinking.
- https://modelscope.cn/Qwen
- https://finance.sina.cn/tech/2026-08-13/detail-ininarri3875962.d.html
- https://www.163.com/dy/article/L46N0SML0556I485.html
- https://www.donews.com/news/detail/8/6668953.html
- https://setupai.cc/update/alibaba-opens-1ea180b0
- https://wpnews.pro/news/alibaba-releases-open-qwen3-8-with-2-4t-total-95b-active-parameters
Architecture details
Hardware footprint and quantization
| Storage format | Approx. size | Runnable hardware | |---|---|---| | BF16 full precision | ~4.9 TB | DGX-class H100 / RTX PRO, 8-GPU minimum | | FP8 quant | ~2.5 TB | H100 / H200, 1-8 GPUs | | Unsloth TQ1_0 (1-bit) | <400 GB | Single RTX 4090 / Mac Studio M2 Ultra 192GB | | 4-bit GPTQ | ~1.2 TB | Single RTX 5090 / Mac Pro |
Alibaba officially recommends the FP8 build. The 1-bit Unsloth path compresses 4.9 TB to under 400 GB but at throughput and long-context quality cost. The practical effect: "weights open, hardware back to datacenter."
Capability caps vs. cloud version
The open release omits features reserved for the paid cloud Qwen3.8-Max:
The open weights thus expose text, general language, long-context, and agent foundations, while vision, tool use, and fast response remain commercial.
Position in the August 2026 "sovereign AI" timeline
Compared to Alibaba's prior largest open model (Qwen3-235B / 22B activated), the 2.4T release lifts Alibaba's open-source ceiling to parity with its closed flagship.