Wësche(@WescheNex1q):GLM-5.3-Flash vs Qwen3.8 Flash-Next: Both on 2× DGX Spark, same 80-test Spark-Bench: -GLM 84.8 -Qwen 79.4 (vLLM) / 77.9 (TensorFold) GLM wins most categories. The biggest gap is long context. Where each one wins • Long context: 70.8 vs 21.6–27.9. GLM found the planted fact on the hard needle test, Qwen missed it on both engines. • Code: 90.2 vs 85.7–87.1 • Visual: 79.8 vs 71.4–75.0 • Tool use: 78.2 vs 58.2–75.6 • Agentic: 78.5 vs 67.1–75.1 Takeaway: Qwen follows instructions and formats more precisely. GLM is the stronger generalist: it codes, builds and retrieves better. Same 80 tests, uncapped, 2 repeats.

2026.10.08 16:17

GLM-5.3-Flash vs Qwen3.8 Flash-Next: Both on 2× DGX Spark, same 80-test Spark-Bench: -GLM 84.8 -Qwen 79.4 (vLLM) / 77.9 (TensorFold) GLM wins most categories. The biggest gap is long context. Where each one wins • Long context: 70.8 vs 21.6–27.9. GLM found the planted fact on the hard needle test, Qwen missed it on both engines. • Code: 90.2 vs 85.7–87.1 • Visual: 79.8 vs 71.4–75.0 • Tool use: 78.2 vs 58.2–75.6 • Agentic: 78.5 vs 67.1–75.1 Takeaway: Qwen follows instructions and formats more precisely. GLM is the stronger generalist: it codes, builds and retrieves better. Same 80 tests, uncapped, 2 repeats.