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

> Source: <https://pub.towardsai.net/deepseek-v4-flash-vs-glm-5-2-the-1-7-point-win-collapses-when-you-swap-the-harness-aa3327c87e26?source=rss----98111c9905da---4>
> Published: 2026-08-02 16:07:28+00:00

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# 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…