GLM-5.2 vs GLM-5.3-Flash
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Winner
GLM-5.3-Flash wins this comparison with an overall score of 85 vs 55 for GLM-5.2, leading 6 of 6 shared benchmarks.
Based on 6 of 37 benchmarks scored by all 2 selected models.
Benchmark Scores Comparison
GLM-5.2
753.0B params
Score: 100.0
GLM-5.3-Flash
320.0B params
Score: 100.0
Coverage matrix
2 models × 37 benchmarks — hover a benchmark name for its category
| Benchmark | GLM-5.2 Zhipu AI | GLM-5.3-Flash Zhipu AI |
|---|---|---|
| GPQA Diamond | 91.2 | — |
| Automation-Bench | 12.9 | 48.8 |
| SWE-bench Pro | 62.1 | — |
| AIME 26 | 99.2 | — |
| Agents' Last Exam | 23.8 | 26.3 |
| Toolathlon Verified | 59.9 | — |
| WildClawBench | 54.2 | — |
| NL2Repo | 48.9 | — |
| PostTrainBench | 34.3 | — |
| DSBench-FullStack | 61.8 | — |
| Terminal-Bench 3.0 | 4.6 | — |
| HMMT Nov 25 | 94.4 | — |
| ExploitGym (6h) | 39 | — |
| HellaSwag | — | 87.1 |
| HLE (with tools) | 54.7 | 55.3 |
| FrontierSWE | 74.4 | — |
| CritPt (no tools) | 20.9 | — |
| MMLU | — | 88.1 |
| IMOAnswerBench | 91 | — |
| Humanity's Last Exam | 40.5 | — |
| DSBench-Hard | 54.5 | — |
| BIG-Bench Hard | — | 86.6 |
| ExploitGym (2h) | 29 | — |
| Terminal-Bench 2.1 (Terminus-2) | 81 | — |
| DeepSWE | 46.2 | — |
| ExploitBench | 24.4 | — |
| HMMT Feb 26 | 92.5 | — |
| Terminal Bench 2.1 | 81 | 84.3 |
| Artificial Analysis Intelligence Index | — | 57 |
| Tool Decathlon | 48.2 | — |
| ProgramBench | 63.7 | — |
| MCP-Atlas | 76.8 | — |
| SWE-Marathon | 13 | — |
| Terminal-Bench 2.1 (Best Reported Harness) | 82.7 | — |
| Cybergym | 77.2 | — |
| DeepSWE 1.1 | 46.2 | 63.4 |
| GDPVal-AA v2 | 1504 | 1773 |
| Covered | 33/37 | 10/37 |
Scorecard
Overall 0–100 score — category strengths (50%), shared-benchmark wins (30%), coverage (20%)
| Model | Overall score | Shared benchmark wins | Coverage | Strongest at | Weakest at |
|---|---|---|---|---|---|
| GLM-5.3-Flash Winner | 85.4 | 6 of 6 | 10 of 37 (27.0%) | STEM & Reasoning | Agentic Coding |
| GLM-5.2 | 54.7 | 0 of 6 | 33 of 37 (89.0%) | STEM & Reasoning | General Agents |
Category strengths
Where each model stands, per benchmark category — score relative to the best model, adjusted for benchmark coverage. This also ranks models when they share few benchmarks.
| Category | GLM-5.3-Flash | GLM-5.2 |
|---|---|---|
| Agentic Coding 15 benchmarks | 100 | 84.4 |
| STEM & Reasoning 8 benchmarks | 100 | 98.6 |
| General Agents 7 benchmarks | 100 | 58.1 |
Fully comparable benchmarks
Scored by all 2 selected models (6)
| Benchmark | Category | GLM-5.2 | GLM-5.3-Flash |
|---|---|---|---|
| Automation-Bench | general_agent | 12.9 | 48.8 |
| Agents' Last Exam | general_agent | 23.8 | 26.3 |
| HLE (with tools) | stem_reasoning | 54.7 | 55.3 |
| Terminal Bench 2.1 | coding_agent | 81 | 84.3 |
| DeepSWE 1.1 | coding_agent | 46.2 | 63.4 |
| GDPVal-AA v2 | general_agent | 1504 | 1773 |
Partial coverage benchmarks
Scored by at least one but not all selected models (31) — missing scores show as —
| Benchmark | Category | GLM-5.2 | GLM-5.3-Flash |
|---|---|---|---|
| GPQA Diamond 1/2 models | stem_reasoning | 91.2 | — |
| SWE-bench Pro 1/2 models | coding_agent | 62.1 | — |
| AIME 26 1/2 models | stem_reasoning | 99.2 | — |
| Toolathlon Verified 1/2 models | general_agent | 59.9 | — |
| WildClawBench 1/2 models | coding_agent | 54.2 | — |
| NL2Repo 1/2 models | coding_agent | 48.9 | — |
| PostTrainBench 1/2 models | coding_agent | 34.3 | — |
| DSBench-FullStack 1/2 models | coding_agent | 61.8 | — |
| Terminal-Bench 3.0 1/2 models | coding_agent | 4.6 | — |
| HMMT Nov 25 1/2 models | stem_reasoning | 94.4 | — |
| ExploitGym (6h) 1/2 models | cybersecurity | 39 | — |
| HellaSwag 1/2 models | reasoning | — | 87.1 |
| FrontierSWE 1/2 models | coding_agent | 74.4 | — |
| CritPt (no tools) 1/2 models | stem_reasoning | 20.9 | — |
| MMLU 1/2 models | knowledge | — | 88.1 |
| IMOAnswerBench 1/2 models | stem_reasoning | 91 | — |
| Humanity's Last Exam 1/2 models | stem_reasoning | 40.5 | — |
| DSBench-Hard 1/2 models | coding_agent | 54.5 | — |
| BIG-Bench Hard 1/2 models | reasoning | — | 86.6 |
| ExploitGym (2h) 1/2 models | cybersecurity | 29 | — |
| Terminal-Bench 2.1 (Terminus-2) 1/2 models | coding_agent | 81 | — |
| DeepSWE 1/2 models | coding_agent | 46.2 | — |
| ExploitBench 1/2 models | cybersecurity | 24.4 | — |
| HMMT Feb 26 1/2 models | stem_reasoning | 92.5 | — |
| Artificial Analysis Intelligence Index 1/2 models | composite | — | 57 |
| Tool Decathlon 1/2 models | general_agent | 48.2 | — |
| ProgramBench 1/2 models | coding_agent | 63.7 | — |
| MCP-Atlas 1/2 models | general_agent | 76.8 | — |
| SWE-Marathon 1/2 models | coding_agent | 13 | — |
| Terminal-Bench 2.1 (Best Reported Harness) 1/2 models | coding_agent | 82.7 | — |
| Cybergym 1/2 models | general_agent | 77.2 | — |
Comparison FAQ
Which model is better, GLM-5.3-Flash or GLM-5.2?
GLM-5.3-Flash wins this comparison with an overall score of 85 vs 55 for GLM-5.2, leading 6 of 6 shared benchmarks.
Which model is better at General Agents, GLM-5.3-Flash or GLM-5.2?
GLM-5.3-Flash leads the General Agents category (strength score 100/100 across 3 comparable benchmarks).
How reliable is this comparison verdict?
Based on 6 of 37 benchmarks scored by all 2 selected models.