Model comparison
GLM-4.6 vs Qwen-14B
GLM-4.6 is the stronger model overall, scoring 41.4 to 31.4 on the Noometry Index.
Last verified . 10 shared benchmarks.
Alibaba (Qwen)Summary
- They share 10 benchmarks with published results for both. GLM-4.6 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-4.6 leads 61.1 to 27.6.
Side by side
| GLM-4.6 | Qwen-14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.4 | 31.4 |
| Released | 2025-09-30 | 2023-09-24 |
| Weights | Open | Open |
| Context window | 205K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.60 | — |
| Output $ / M tokens | $2.20 | — |
| Results tracked | 29 | 18 |
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Category by category
Coding GLM-4.6 leads
GLM-4.6: 40.1 (#148), Qwen-14B: 31.2 (#288)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Coding | 1449 | 1071 |
| 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), Qwen-14B: —
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), Qwen-14B: 19.6 (#257)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1440 | 1027 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| BIG-Bench Hard | — | 55% |
| Epoch Capabilities Index | — | 113.03 |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), Qwen-14B: 31.2 (#227)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Math | 1432 | 1068 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 61.3% |
Knowledge Not comparable
GLM-4.6: 40.2 (#124), Qwen-14B: —
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), Qwen-14B: 27.5 (#275)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1426 | 1041 |
| LMArena Chinese | 1499 | 1077 |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.3 (#98), Qwen-14B: 52.4 (#289)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1031 |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), Qwen-14B: 31.3 (#280)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1422 | 1028 |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), Qwen-14B: 27.6 (#299)
| Benchmark | GLM-4.6 | Qwen-14B |
|---|---|---|
| LMArena Text | 1440 | 1051 |
| LMArena Creative Writing | 1411 | 1028 |
| LMArena Multi-Turn | 1427 | 1022 |
| EQ-Bench Creative Writing | 1411 | — |
Frequently asked questions
Is GLM-4.6 better than Qwen-14B?
GLM-4.6 is the stronger model overall, scoring 41.4 to 31.4 on the Noometry Index.
Is GLM-4.6 or Qwen-14B better for coding?
GLM-4.6 scores higher on coding benchmarks: 40.1 versus 31.2 in the Noometry coding category.
How many benchmarks do GLM-4.6 and Qwen-14B share?
10 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen-14B has 18.