speed of GLM-4.7-Flash vs Qwen3.5-35B-A3B

A new performance comparison reveals Zhipu AI's GLM-4.7-Flash model significantly outperforms Qwen's 3.5-35B-A3B in processing speed for long-context agentic coding tasks. The benchmark, conducted using llama.cpp on a 3×3090 GPU configuration with CUDA backend, specifically targets the demanding 50,000-token context windows common in extended coding sessions. This head-to-head test, shared by a developer seeking community feedback, highlights the critical importance of inference speed for practical AI coding assistants, where latency directly impacts developer workflow and iteration cycles. The results position GLM-4.7-Flash as a strong contender in the race for efficient, large-context language models suited for complex, multi-step programming assistance.

Technical analysis shows the speed advantage is most pronounced in the long-context regime essential for agentic workflows, where models must maintain coherence across thousands of lines of code. While a supplementary plot indicates Qwen's model may compete in zero-context scenarios, the primary benchmark underscores GLM-4.7-Flash's optimization for real-world coding applications. The community-driven testing approach, with promises of more detailed multi-model benchmarks in March, reflects the rapidly evolving and competitive landscape of open-source coding LLMs. For developers and enterprises, this speed differential could translate to tangible productivity gains, reducing wait times for code generation, explanation, and debugging within integrated development environments.