Blown Away By Qwen 3.5 35b A3B
In rigorous testing against 8 models, this open-source model outperformed on long-context accuracy despite slower speed.
Alibaba's Qwen 3.5 35B A3B has emerged as a dark horse contender in the local LLM space, outperforming established models in rigorous companion-focused testing. A detailed evaluation against eight models including GLM 4.7 and Anthropic's Claude Sonnet revealed Qwen's superior ability to handle long-context tasks with fewer errors, despite testers initially settling on GLM 4.7 before discovering Qwen's capabilities. The testing methodology involved five multi-stage questions designed to assess context referencing from system prompts, with Claude Sonnet 4.6 providing comparative analysis that ultimately crowned Qwen as "far and away the winner."
The Qwen 3.5 35B A3B model demonstrates that parameter efficiency and specialized training can overcome raw speed advantages. While noticeably slower than GLM 4.7 and other competitors, its accuracy in maintaining context and avoiding the "little mistakes" that plagued other local models made it the preferred choice for practical companion applications. This development signals a maturation of open-source models that can now compete with cloud offerings like Gemini 3 Pro for specific use cases, particularly for users prioritizing accuracy over latency in local deployments. The success with 64GB Mac setups suggests accessible hardware requirements for high-performance local AI.
- Outperformed 8 models including GLM 4.7 and Claude Sonnet in multi-stage context testing
- Excels at long-context companion tasks with accurate system prompt referencing
- Runs effectively on 64GB Mac setups despite being slower than competitors
Why It Matters
Provides enterprise-grade local AI companion capabilities without cloud dependency, enabling private, accurate long-context applications.