Qwen3.5 397B-A17B is Alibaba's largest model ever — 397 billion total parameters activating 17B per token, the most powerful open-weight model in existence and a new global standard for AI capability.
Qwen3.5 397B-A17B (MoE) is an AI model released 2025 by Qwen, a leading organization in large language model research. Massive Qwen3.5 MoE — 397B total, 17B active. Near-GPT-4o quality at significantly reduced inference cost. With 397B total (17B active) and a 128K tokens context window, Qwen3.5 397B-A17B (MoE) is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
Qwen3.5 397B-A17B (MoE) employs a Mixture-of-Experts (MoE) architecture with 397B total (17B active). In a MoE design, only a subset of "expert" sub-networks activates for each token during inference — Qwen3.5 397B-A17B (MoE) has 397B total (17B active) total parameters but uses only a fraction per token. This allows the model to store significantly more learned knowledge than a comparably-priced dense model. MoE architectures have proven effective at reaching frontier-level capability while keeping serving costs manageable — a key reason Qwen3.5 397B-A17B (MoE) can be offered at the scale that platforms like LITHOVEX AI make possible.
As of 2026-10-16, Qwen3.5 397B-A17B (MoE) by Qwen demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 94.1%. For software engineering tasks, Qwen3.5 397B-A17B (MoE) scores 73.5% on LiveCodeBench, which tests practical software engineering. In mathematical reasoning, Qwen3.5 397B-A17B (MoE) records 88.2% on AIME 2025. Across all 4 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place Qwen3.5 397B-A17B (MoE) — at 397B total (17B active) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of Qwen3.5 397B-A17B (MoE) are: Qwen3.5 flagship model — 397B knowledge at 17B inference cost; Near Qwen3 235B quality at more efficient inference; Apache 2.0. These make it particularly well-suited for Frontier-level AI applications with efficiency constraints, Production deployments of Qwen3.5 quality at scale. Users working on complex, multi-step problems benefit most from Qwen3.5 397B-A17B (MoE)'s ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, Qwen3.5 397B-A17B (MoE) has limitations: Requires substantial multi-GPU cluster for any practical deployment; Highest inference latency in the Qwen family — not for real-time use; 397B total weight requires significant storage (terabytes). For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. Qwen continues to improve Qwen3.5 397B-A17B (MoE) through ongoing research.
Qwen3.5 397B-A17B (MoE) is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making Qwen3.5 397B-A17B (MoE) accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, Qwen3.5 397B-A17B (MoE) by Qwen demonstrates measurable performance on standard AI evaluation benchmarks. On MMLU — which tests world knowledge across 57 academic subjects from elementary to professional level — it achieves 94.1%. For software engineering tasks, Qwen3.5 397B-A17B (MoE) scores 73.5% on LiveCodeBench, which tests practical software engineering. In mathematical reasoning, Qwen3.5 397B-A17B (MoE) records 88.2% on AIME 2025. Across all 4 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place Qwen3.5 397B-A17B (MoE) — at 397B total (17B active) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
MoE: 397B total / 17B active — Qwen3.5 flagship
The training pipeline for Qwen3.5 397B-A17B (MoE) involves multiple stages: large-scale pretraining on diverse multilingual text and code corpora to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. Qwen's research team — based in China — has optimized Qwen3.5 397B-A17B (MoE) for the specific capability profile described above.
Inference efficiency: Qwen3.5 397B-A17B (MoE) at 397B total (17B active) is designed to deliver quality outputs at practical serving speeds. Its 128K tokens context window enables processing entire documents, multi-file codebases, and extended conversation histories — particularly valuable for contract review, technical documentation analysis, and codebase-level AI assistance.
Getting started with Qwen3.5 397B-A17B (MoE) on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with Qwen3.5 397B-A17B (MoE): provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.
Frontier AI research requiring open-weight access
National AI program deployments with full model ownership
Maximum-quality autonomous AI agents
Scientific discovery support at research-frontier level
Government AI deployments requiring no proprietary vendor lock-in
Qwen (Tongyi Qianwen) is the AI model family from Alibaba Cloud, the cloud computing arm of Alibaba Group — one of the world's largest technology companies with over $100 billion in annual revenue. Alibaba invested in AI research through its DAMO Academy starting in 2017. The Qwen3 generation, released in April 2025, represents a major leap in open-model capability: models from 8B to 235B parameters, with hybrid thinking modes and 119-language support, all released under Apache 2.0 licenses for commercial use. Qwen models consistently rank at the top of the Open LLM Leaderboard and have been widely adopted across Asia, Europe, and North America for both commercial and research applications.
Funding: Part of Alibaba Group (NYSE: BABA, $100B+ annual revenue)
Founded: 1999, headquartered in China.
Qwen3.5 397B-A17B (MoE) by Qwen competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both Qwen3.5 397B-A17B (MoE) and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
Qwen3.5 397B-A17B (MoE) is released under Apache 2.0, which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs Qwen3.5 397B-A17B (MoE) on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
Qwen3.5 397B-A17B represents the apex of Alibaba's AI research and the current peak of open-source AI globally. Research institutions, national AI programs, and enterprise AI labs worldwide are evaluating it as the first open model to challenge frontier proprietary models on most benchmarks.
Qwen3.5 397B-A17B (MoE) by Qwen has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official Qwen model page.