Model comparison
GLM-4.6 vs Qwen1.5-14B
GLM-4.6 is the stronger model overall, scoring 41.4 to 32.7 on the Noometry Index.
Last verified . 16 shared benchmarks.
Alibaba (Qwen)Summary
- They share 16 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and Qwen1.5-14B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 33.6.
Side by side
| GLM-4.6 | Qwen1.5-14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.4 | 32.7 |
| Released | 2025-09-30 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 29 | 17 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), Qwen1.5-14B: 33.1 (#263)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Coding | 1449 | 1138 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), Qwen1.5-14B: —
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), Qwen1.5-14B: 21.4 (#223)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | 1440 | 1113 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), Qwen1.5-14B: 32.4 (#215)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Math | 1432 | 1125 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 40.2 (#124), Qwen1.5-14B: 29.8 (#232)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | 1431 | 1094 |
| Vectara Hallucination Rate | 9.5% | — |
| MMLU | — | 68.6% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), Qwen1.5-14B: 30.7 (#262)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | 1426 | 1095 |
| LMArena Chinese | 1499 | 1147 |
| LMArena French | 1459 | 1116 |
| LMArena German | 1447 | 1043 |
| LMArena Japanese | 1393 | 1019 |
| LMArena Russian | 1419 | 1046 |
| LMArena Spanish | 1436 | 1085 |
| LMArena Korean | 1400 | — |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), Qwen1.5-14B: 56.8 (#271)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1102 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), Qwen1.5-14B: 33.7 (#257)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | 1422 | 1113 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), Qwen1.5-14B: 33.6 (#276)
| Benchmark | GLM-4.6 | Qwen1.5-14B |
|---|---|---|
| LMArena Text | 1440 | 1128 |
| LMArena Creative Writing | 1411 | 1091 |
| LMArena Multi-Turn | 1427 | 1110 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Qwen1.5-14B?
GLM-4.6 is the stronger model overall, scoring 41.4 to 32.7 on the Noometry Index.
Is GLM-4.6 or Qwen1.5-14B better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 33.1 in the Noometry coding category.
How many benchmarks do GLM-4.6 and Qwen1.5-14B share?
16 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen1.5-14B has 17.