DeepSeek V4-Flash vs GLM-5.2: The 1.7-Point Win Collapses When You Swap the Harness

DeepSeek's July 31, 2026 release of DeepSeek-V4-Flash-0731, a 284-billion-parameter model with 13 billion active parameters, claims a 1.7-point lead over Z.ai's GLM-5.2 on Terminal-Bench 2.1 (82.7 vs 81.0), but the score was produced using an 'upcoming' DeepSeek Harness (minimal mode) framework that had not shipped, and documented harness-only effects as small as 2.1 points can erase that margin.

Member-only story DeepSeek V4-Flash vs GLM-5.2: The 1.7-Point Win Collapses When You Swap the Harness DeepSeek’s own chart says 82.7 on Terminal-Bench 2.1. Artificial Analysis measured 79. That 3.7-point gap is 2.2x the margin the 82.7 was defending. On July 31, 2026, DeepSeek shipped DeepSeek-V4-Flash-0731 and published a nine-row agent benchmark chart. The chart is genuinely impressive: a 284-billion-parameter model with 13 billion active at inference beats Z.ai's GLM-5.2 on every single row where both were measured, and comes within a few points of Claude Opus 4.8 on several. Then I read the footnote under the table. For public Code Agent tasks, V4-Flash was tested using our upcoming DeepSeek Harness minimal mode framework. Settings: max tier, topp=0.95, temperature=1.0. Upcoming. The framework that produced every number on that chart had not shipped on the day the chart shipped. No one outside DeepSeek could run it. That would be a footnote-sized problem if agent benchmark scores were harness-independent. They are not. The margin DeepSeek is claiming on Terminal-Bench 2.1 is 1.7 points 82.7 vs GLM-5.2’s 81.0 . The smallest harness-only effect I could find documented anywhere in the 2026 literature — adding one search subagent to an otherwise identical scaffold — is 2.1 points . The floor of the…