Model Detail
Qwen3.8-2.4T-A95B
—Qwen3.8-2.4T-A95B is a large language model with 95B parameters released by Qwen. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, distributed under a other license.
Qwen3.8-2.4T-A95B is priced at $2/M input tokens and $6/M output tokens. Operationally the model offers a 1010K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.
Qwen3.8-2.4T-A95B ships with 95B parameters. Total weight footprint is approximately 2446.2 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
Downloads of Qwen3.8-2.4T-A95B have moved -1.4% over the trailing seven days. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
Qwen3.8-2.4T-A95B is best fit for general-purpose chat and instruction-following workloads, and long-context tasks such as full-codebase analysis or book-length summarization (1010K tokens). Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Privacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27B
arXiv:2610.27817v1 Announce Type: new Abstract: Every prompt sent to a hosted language model is readable by whoever runs it. We study whether the prompt can remain on the client while the host receives transformed vectors that stand in for it. A language model converts text to vectors exactly once,
Qwen3.8-Omni: Towards Native Omni-Modal Agents
arXiv:2609.25611v1 Announce Type: new Abstract: We introduce Qwen3.8-Omni-Flash, a natively multimodal agentic model for real-world multimodal productivity. Compared with previous omni models, which primarily emphasized perception and interaction, Qwen3.8-Omni-Flash substantially improves multimodal
On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability
arXiv:2608.30320v1 Announce Type: new Abstract: We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-trainin
