Model comparison
GLM-4.6V vs Qwen1.5-72B
GLM-4.6V is the stronger model overall, scoring 41.3 to 30.8 on the Noometry Index.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Qwen1.5-72B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.6V leads 38.0 to 11.5.
Side by side
| GLM-4.6V | Qwen1.5-72B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.3 | 30.8 |
| Released | 2025-12-08 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 33K | — |
| Input $ / M tokens | $0.30 | — |
| Output $ / M tokens | $0.90 | — |
| Results tracked | 12 | 22 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Qwen1.5-72B: 31.9 (#277)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Coding | 1390 | 1165 |
| BigCodeBench Instruct | — | 33.2% |
| BigCodeBench Complete | — | 40.3% |
| HumanEval+ | — | 59.1% |
| MBPP+ | — | 61.6% |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Qwen1.5-72B: 22.2 (#203)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1148 |
Math Not comparable
GLM-4.6V: —, Qwen1.5-72B: 33.2 (#205)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Math | — | 1164 |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Qwen1.5-72B: 11.5 (#300)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Expert | 1371 | 1136 |
| GPQA Diamond | — | 28.8% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Qwen1.5-72B: —
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.6 (#141), Qwen1.5-72B: 33.2 (#253)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Non-English | 1359 | 1135 |
| LMArena Chinese | 1425 | 1186 |
| LMArena Russian | 1340 | 1104 |
| LMArena French | — | 1159 |
| LMArena German | — | 1084 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1050 |
| LMArena Spanish | — | 1110 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Qwen1.5-72B: 59.3 (#256)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Instruction Following | 1352 | 1141 |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#143), Qwen1.5-72B: 35.1 (#243)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Longer Query | 1358 | 1157 |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Qwen1.5-72B: 37.3 (#258)
| Benchmark | GLM-4.6V | Qwen1.5-72B |
|---|---|---|
| LMArena Text | 1377 | 1166 |
| LMArena Creative Writing | 1347 | 1137 |
| LMArena Multi-Turn | 1360 | 1160 |
Frequently asked questions
Is GLM-4.6V better than Qwen1.5-72B?
GLM-4.6V is the stronger model overall, scoring 41.3 to 30.8 on the Noometry Index.
Is GLM-4.6V or Qwen1.5-72B better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 31.9 in the Noometry coding category.
How many benchmarks do GLM-4.6V and Qwen1.5-72B share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen1.5-72B has 22.