Model comparison
GLM-4.6 vs Qwen1.5 4b Chat
GLM-4.6 is the stronger model overall, scoring 40.9 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-4.6 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-4.6 leads 61.3 to 23.9.
Side by side
| GLM-4.6 | Qwen1.5 4b Chat | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 28.3 |
| Released | 2025-09-30 | — |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 29 | 13 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 39.9 (#137), Qwen1.5 4b Chat: 28.6 (#305)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Coding | 1449 | 999 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.1 (#60), Qwen1.5 4b Chat: —
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.1 (#168), Qwen1.5 4b Chat: 17.7 (#277)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Hard Prompts | 1440 | 976 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
Math GLM-4.6 leads
GLM-4.6: 36.4 (#115), Qwen1.5 4b Chat: 27.9 (#233)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Math | 1431 | 1026 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 39.6 (#118), Qwen1.5 4b Chat: 26.2 (#252)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Expert | 1432 | 980 |
| Vectara Hallucination Rate | 9.5% | — |
Multilingual GLM-4.6 leads
GLM-4.6: 53.6 (#62), Qwen1.5 4b Chat: 24.2 (#285)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Non-English | 1426 | 979 |
| LMArena Chinese | 1500 | 1024 |
| LMArena German | 1447 | 902 |
| LMArena Russian | 1418 | 952 |
| LMArena French | 1459 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1401 | — |
| LMArena Spanish | 1436 | — |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.5 (#93), Qwen1.5 4b Chat: 49.3 (#295)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Instruction Following | 1410 | 979 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#91), Qwen1.5 4b Chat: 30.1 (#285)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Longer Query | 1423 | 988 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.3 (#86), Qwen1.5 4b Chat: 23.9 (#304)
| Benchmark | GLM-4.6 | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Text | 1440 | 997 |
| LMArena Creative Writing | 1411 | 969 |
| LMArena Multi-Turn | 1427 | 977 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Qwen1.5 4b Chat?
GLM-4.6 is the stronger model overall, scoring 40.9 to 28.3 on the Noometry Index.
Is GLM-4.6 or Qwen1.5 4b Chat better for coding?
GLM-4.6 scores higher on coding benchmarks: 39.9 versus 28.6 in the Noometry coding category.
How many benchmarks do GLM-4.6 and Qwen1.5 4b Chat share?
13 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen1.5 4b Chat has 13.