Unsloth fixed version of Qwen3.5-35B-A3B is incredible at research tasks.

Unsloth has released a crucial fix for Alibaba's Qwen3.5-35B-A3B model that transforms its research capabilities. The updated quantized version on Hugging Face resolves persistent tool calling issues that previously hampered the model's ability to properly utilize search engines and web loaders. When tested against leading models including Gemini, ChatGPT, Deepseek, GLM, Kimi, and Perplexity in a comparative research task, the patched Qwen model delivered superior results with better solution discovery and more coherent recommendations. The fix comes after initial impressions suggested only marginal improvements over GLM-4.7-Flash, despite Qwen's 5-billion parameter advantage and hybrid linear attention architecture.

The technical breakthrough centers on native tool calling functionality that now works reliably through OpenWebUI with SearXNG search integration. Users report running the model via llama.cpp-rocm with 262,144 context size and 999 GPU layers, achieving stable performance with temperature 0.6 and top-p 0.90 settings. The model's hybrid linear attention architecture enables double the native context length without significant memory overhead, making it particularly effective for research-intensive tasks requiring web scraping and multi-source analysis. This fix positions Qwen3.5-35B-A3B as a serious contender in the open-source research assistant space, offering enterprise-grade capabilities previously dominated by proprietary models.