# LLM.txt - Qwen3.5: The Model Anthropic Didn't Name ## Article Metadata - **Title**: Qwen3.5: The Model Anthropic Didn't Name - **URL**: https://www.llmrumors.com/news/qwen35-alibaba-data-moat-distillation-absent - **Publication Date**: February 26, 2026 - **Reading Time**: 14 min read - **Tags**: Qwen3.5, Alibaba, AI Benchmarks, Distillation, Data Moat, Open Source AI, Chinese AI, MoE Architecture - **Slug**: qwen35-alibaba-data-moat-distillation-absent ## Summary Qwen3.5-397B-A17B scores 88.4 on GPQA Diamond and 87.8 on MMLU-Pro at $0.60/M input, conspicuously absent from Anthropic's distillation attack report. That absence explains more about the AI industry than the names that were included. ## Key Topics - Qwen3.5 - Alibaba - AI Benchmarks - Distillation - Data Moat - Open Source AI - Chinese AI - MoE Architecture ## Content Structure This article from LLM Rumors covers: - Technical implementation details - Legal analysis and implications - Industry comparison and competitive analysis - Data acquisition and training methodologies - Financial analysis and cost breakdown - Human oversight and quality control processes - Comprehensive source documentation and references ## Full Content Preview TL;DR: On February 16, 2026, Alibaba released Qwen3.5-397B-A17B: a 397B-parameter MoE model that activates only 17B per forward pass, scores 88.4 on GPQA Diamond, and runs at $0.60 per million input tokens, roughly 8x cheaper than Claude Opus 4.6.[1] Seven days earlier, Anthropic published a landmark distillation attack report naming three Chinese AI labs for industrial-scale theft.[2] Alibaba was not among them. That absence is not a coincidence. It is the entire story. --- On February 23, 2026, Anthropic named three Chinese AI labs (DeepSeek, Moonshot AI, and MiniMax) for running coordinated campaigns to extract Claude's capabilities through 16 million fraudulent API exchanges.[2] The industry parsed the names that were included. Almost nobody asked about the names that were left out. Alibaba was not named. ByteDance was not named. Baidu was not named. Tencent was not named. These are not small actors. Together they control more users, more compute, more revenue, and more AI deployment than the three companies Anthropic did name. And they are conspicuously, structurally, predictably absent from a report about labs that needed to steal training data because they could not generate it themselves. Qwen3.5 is the proof of concept. Frontier-tier benchmarks. Open-source Apache 2.0 licensing. Eight times cheaper than Claude at the API level. Built by a company that processes more commercial transactions annually than Amazon, eBay, and Etsy combined. They did not need to distill from anyone. They could not afford the reputational risk even if they had wanted to. And most importantly: they had better options. Anthropic's distillation report named every major pure-play Chinese AI startup. It named none of the Chinese Big Tech AI divisions. Alibaba, ByteDance, Baidu, Tencent, and Xiaomi, all running frontier AI programs, are absent. The companies that were caught are all data-poor relative to their frontier ambitions. The companies that were not caught are data-rich by construction. This is not a coincidence. It is the operating logic of the distillation problem. What Qwen3.5 Actually Is Before the strategic analysis, the technical reality deserves attention, because Qwen3.5 is genuinely impressive in ways that get buried under the geopolitical noise. Released on February 16, 2026, Qwen3.5-397B-A17B is the first open-weight model in the new Qwen3.5 series.[1] It is a native vision-language model built on a hybrid architecture that fuses linear attention via Gated Delta Networks with a sparse mixture-of-experts design. The architecture matters because it achieves something that was considered difficult eighteen months ago: 397 billion total parameters with only 17 billion activated per forward pass. That is a 95% reduction in active compute relative to total capacity without proportional capability loss. This is not a capability demo. Qwen3.5 is already deployed across Alibaba's product suite. The model supports 201 languages and dialects, up from 119 in the previous generation, reflecting Alibaba's global commercial footprint. The hosted Qwen3.5-Plus version includes a default 1 million token context window and built-in tool use with adaptive agent capabilities.[3] The architecture is genuinely novel. Most frontier models bolt on vision as a second stage. Qwen3.5 processes text, images up to 1344×1344 resolution, and 60-second video clips from the first pretraining stage. The multimodal capability is architectural, not cosmetic. The Benchmarks: Where Qwen3.5 Actually Sits Self-reported benchmarks from Chinese AI labs require caveat. Alibaba claims Qwen3.5 outperforms GPT-5.2, Claude Opus 4.6, and Gemini 3 Pro on roughly 80% of evaluated benchmark categories.[3] CNBC noted that CNBC could not independently verify thos... [Content continues - full article available at source URL] ## Citation Format **APA Style**: LLM Rumors. (2026). Qwen3.5: The Model Anthropic Didn't Name. Retrieved from https://www.llmrumors.com/news/qwen35-alibaba-data-moat-distillation-absent **Chicago Style**: LLM Rumors. "Qwen3.5: The Model Anthropic Didn't Name." Accessed February 26, 2026. https://www.llmrumors.com/news/qwen35-alibaba-data-moat-distillation-absent. ## Machine-Readable Tags #LLMRumors #AI #Technology #Qwen3.5 #Alibaba #AIBenchmarks #Distillation #DataMoat #OpenSourceAI #ChineseAI #MoEArchitecture ## Content Analysis - **Word Count**: ~2,520 - **Article Type**: News Analysis - **Source Reliability**: High (Original Reporting) - **Technical Depth**: High - **Target Audience**: AI Professionals, Researchers, Industry Observers ## Related Context This article is part of LLM Rumors' coverage of AI industry developments, focusing on data practices, legal implications, and technological advances in large language models. --- Generated automatically for LLM consumption Last updated: 2026-02-26T05:55:22.582Z Source: LLM Rumors (https://www.llmrumors.com/news/qwen35-alibaba-data-moat-distillation-absent)