Model comparison
GLM-4.5 vs Qwen1.5-72B
GLM-4.5 is the stronger model overall, scoring 42.0 to 30.8 on the Noometry Index.
Last verified . 17 shared benchmarks.
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. GLM-4.5 scores higher in 8 categories and Qwen1.5-72B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.5 leads 35.9 to 11.5.
Side by side
| GLM-4.5 | Qwen1.5-72B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 30.8 |
| Released | 2025-07-27 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 131K | — |
| Max output | 98K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 27 | 22 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.4 (#125), Qwen1.5-72B: 31.9 (#277)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Coding | 1434 | 1165 |
| SWE-bench Verified (bash only) | 54.2% | — |
| WeirdML | 40.6% | — |
| BigCodeBench Instruct | — | 33.2% |
| BigCodeBench Complete | — | 40.3% |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
| HumanEval+ | — | 59.1% |
| MBPP+ | — | 61.6% |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Qwen1.5-72B: 22.2 (#203)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Hard Prompts | 1429 | 1148 |
| Kagi LLM Benchmark | 57.9% | — |
Math GLM-4.5 leads
GLM-4.5: 39.0 (#116), Qwen1.5-72B: 33.2 (#205)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Math | 1427 | 1164 |
Knowledge GLM-4.5 leads
GLM-4.5: 35.9 (#179), Qwen1.5-72B: 11.5 (#300)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Expert | 1433 | 1136 |
| GPQA Diamond | — | 28.8% |
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
Multilingual GLM-4.5 leads
GLM-4.5: 52.8 (#77), Qwen1.5-72B: 33.2 (#253)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Non-English | 1417 | 1135 |
| LMArena Chinese | 1465 | 1186 |
| LMArena French | 1418 | 1159 |
| LMArena German | 1407 | 1084 |
| LMArena Japanese | 1415 | 1061 |
| LMArena Korean | 1380 | 1050 |
| LMArena Russian | 1414 | 1104 |
| LMArena Spanish | 1454 | 1110 |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.1 (#104), Qwen1.5-72B: 59.3 (#256)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Instruction Following | 1404 | 1141 |
Long Context GLM-4.5 leads
GLM-4.5: 38.2 (#201), Qwen1.5-72B: 35.1 (#243)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Longer Query | 1412 | 1157 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.5 (#127), Qwen1.5-72B: 37.3 (#258)
| Benchmark | GLM-4.5 | Qwen1.5-72B |
|---|---|---|
| LMArena Text | 1430 | 1166 |
| LMArena Creative Writing | 1395 | 1137 |
| LMArena Multi-Turn | 1415 | 1160 |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
Frequently asked questions
Is GLM-4.5 better than Qwen1.5-72B?
GLM-4.5 is the stronger model overall, scoring 42.0 to 30.8 on the Noometry Index.
Is GLM-4.5 or Qwen1.5-72B better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 31.9 in the Noometry coding category.
How many benchmarks do GLM-4.5 and Qwen1.5-72B share?
17 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen1.5-72B has 22.