Model comparison
GLM-4.5 vs Qwen-14B
GLM-4.5 is the stronger model overall, scoring 41.4 to 30.8 on the Noometry Index.
Last verified . 10 shared benchmarks.
Alibaba (Qwen)Summary
- They share 10 benchmarks with published results for both. GLM-4.5 scores higher in 7 categories and Qwen-14B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.5 leads 57.7 to 27.8.
Side by side
| GLM-4.5 | Qwen-14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.4 | 30.8 |
| Released | 2025-07-27 | 2023-09-24 |
| Weights | Open | Open |
| Context window | 131K | — |
| Max output | 98K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 27 | 18 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.1 (#121), Qwen-14B: 30.6 (#283)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Coding | 1434 | 1072 |
| SWE-bench Verified (bash only) | 54.2% | — |
| WeirdML | 40.6% | — |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Reasoning GLM-4.5 leads
GLM-4.5: 27.7 (#99), Qwen-14B: 18.7 (#255)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1430 | 1027 |
| Kagi LLM Benchmark | 57.9% | — |
| BIG-Bench Hard | — | 55% |
| Epoch Capabilities Index | — | 112.8 |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |
Math GLM-4.5 leads
GLM-4.5: 36.3 (#119), Qwen-14B: 28.7 (#224)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Math | 1427 | 1068 |
| GSM8K | — | 61.3% |
Knowledge Not comparable
GLM-4.5: 35.5 (#172), Qwen-14B: —
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
| LMArena Expert | 1433 | — |
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |
Multilingual GLM-4.5 leads
GLM-4.5: 53.0 (#74), Qwen-14B: 27.7 (#270)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1417 | 1042 |
| LMArena Chinese | 1465 | 1077 |
| LMArena French | 1423 | — |
| LMArena German | 1406 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1382 | — |
| LMArena Russian | 1414 | — |
| LMArena Spanish | 1454 | — |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.3 (#100), Qwen-14B: 52.6 (#284)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1405 | 1031 |
Long Context GLM-4.5 leads
GLM-4.5: 38.2 (#196), Qwen-14B: 31.3 (#276)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1413 | 1029 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.7 (#121), Qwen-14B: 27.8 (#294)
| Benchmark | GLM-4.5 | Qwen-14B |
|---|---|---|
| LMArena Text | 1430 | 1051 |
| LMArena Creative Writing | 1395 | 1028 |
| LMArena Multi-Turn | 1415 | 1022 |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
Frequently asked questions
Is GLM-4.5 better than Qwen-14B?
GLM-4.5 is the stronger model overall, scoring 41.4 to 30.8 on the Noometry Index.
Is GLM-4.5 or Qwen-14B better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.1 versus 30.6 in the Noometry coding category.
How many benchmarks do GLM-4.5 and Qwen-14B share?
10 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen-14B has 18.