Verdict
GLM-5.2 leads 6–0 across 6 shared benchmarks.
DeepSeek-R1 0 · GLM-5.2 6 · higher isn't always better — check the caveats below.
Reported scores — protocols may differ. How we compare
Reasoning: GLM-5.2 leads 2–0 across 2 shared reasoning benchmarks (largest gap: ARC-AGI, 77 vs 15.8 — single evaluator).
Vendor claims — compare against the measured scores above. A claim is what a developer says about its own model, not an independent measurement.
Reasoning
“After these steps, we obtained a checkpoint referred to as DeepSeek-R1, which achieves performance on par with OpenAI-o1-1217.”
“For education-oriented knowledge benchmarks such as MMLU, MMLU-Pro, and GPQA Diamond, DeepSeek-R1 demonstrates superior performance compared to DeepSeek-V3.”
Coding
“Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency”
Context Handling
“We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9x at a 1M context length.”
General
“Pure Open: An MIT open-source license - no regional limits, technical access without borders”
GLM-5.2 leads 6–0 across 6 shared benchmarks. DeepSeek-R1 leads 0 benchmarks and GLM-5.2 leads 6. Higher isn't always better — see the integrity caveats on each benchmark.
DeepSeek-R1 and GLM-5.2 have 6 benchmarks in common in our data — those are the only rows where a direct, apples-to-apples comparison is drawn.
GLM-5.2 leads reasoning 2–0.