Model comparison
GLM-4.5 vs Qwen1.5 4b Chat
GLM-4.5 is the stronger model overall, scoring 41.4 to 28.3 on the Noometry Index.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GLM-4.5 scores higher in 8 categories and Qwen1.5 4b Chat in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.5 leads 57.7 to 23.9.
Side by side
| GLM-4.5 | Qwen1.5 4b Chat | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.4 | 28.3 |
| Released | 2025-07-27 | — |
| Weights | Open | Open |
| Context window | 131K | — |
| Max output | 98K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 27 | 13 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.1 (#121), Qwen1.5 4b Chat: 28.6 (#305)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Coding | 1434 | 999 |
| 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), Qwen1.5 4b Chat: 17.7 (#277)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Hard Prompts | 1430 | 976 |
| Kagi LLM Benchmark | 57.9% | — |
Math GLM-4.5 leads
GLM-4.5: 36.3 (#119), Qwen1.5 4b Chat: 27.9 (#233)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Math | 1427 | 1026 |
Knowledge GLM-4.5 leads
GLM-4.5: 35.5 (#172), Qwen1.5 4b Chat: 26.2 (#252)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Expert | 1433 | 980 |
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
Multilingual GLM-4.5 leads
GLM-4.5: 53.0 (#74), Qwen1.5 4b Chat: 24.2 (#285)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Non-English | 1417 | 979 |
| LMArena Chinese | 1465 | 1024 |
| LMArena German | 1406 | 902 |
| LMArena Russian | 1414 | 952 |
| LMArena French | 1423 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1382 | — |
| LMArena Spanish | 1454 | — |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.3 (#100), Qwen1.5 4b Chat: 49.3 (#295)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Instruction Following | 1405 | 979 |
Long Context GLM-4.5 leads
GLM-4.5: 38.2 (#196), Qwen1.5 4b Chat: 30.1 (#285)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Longer Query | 1413 | 988 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.7 (#121), Qwen1.5 4b Chat: 23.9 (#304)
| Benchmark | GLM-4.5 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Text | 1430 | 997 |
| LMArena Creative Writing | 1395 | 969 |
| LMArena Multi-Turn | 1415 | 977 |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
Frequently asked questions
Is GLM-4.5 better than Qwen1.5 4b Chat?
GLM-4.5 is the stronger model overall, scoring 41.4 to 28.3 on the Noometry Index.
Is GLM-4.5 or Qwen1.5 4b Chat better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.1 versus 28.6 in the Noometry coding category.
How many benchmarks do GLM-4.5 and Qwen1.5 4b Chat share?
13 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen1.5 4b Chat has 13.