Model comparison
GLM-4.7-Flash vs Qwen1.5-14B
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 32.7 on the Noometry Index.
Last verified . 15 shared benchmarks.
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and Qwen1.5-14B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GLM-4.7-Flash leads 46.5 to 30.7.
Side by side
| GLM-4.7-Flash | Qwen1.5-14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.8 | 32.7 |
| Released | 2026-01-19 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 17 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.6 (#135), Qwen1.5-14B: 33.1 (#263)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Coding | 1383 | 1138 |
Reasoning Too close to call
GLM-4.7-Flash: 20.9 (#229), Qwen1.5-14B: 21.4 (#223)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1113 |
| Chess Puzzles | 0% | — |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 36.1 (#173), Qwen1.5-14B: 32.4 (#215)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Math | 1355 | 1125 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.5 (#184), Qwen1.5-14B: 29.8 (#232)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Expert | 1357 | 1094 |
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
| MMLU | — | 68.6% |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.5 (#158), Qwen1.5-14B: 30.7 (#262)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Non-English | 1330 | 1095 |
| LMArena Chinese | 1403 | 1147 |
| LMArena French | 1332 | 1116 |
| LMArena German | 1337 | 1043 |
| LMArena Russian | 1332 | 1046 |
| LMArena Spanish | 1350 | 1085 |
| LMArena Japanese | — | 1019 |
| LMArena Korean | 1283 | — |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.1 (#167), Qwen1.5-14B: 56.8 (#271)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1102 |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#148), Qwen1.5-14B: 33.7 (#257)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Longer Query | 1345 | 1113 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.4 (#210), Qwen1.5-14B: 33.6 (#276)
| Benchmark | GLM-4.7-Flash | Qwen1.5-14B |
|---|---|---|
| LMArena Text | 1351 | 1128 |
| LMArena Creative Writing | 1297 | 1091 |
| LMArena Multi-Turn | 1342 | 1110 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen1.5-14B?
GLM-4.7-Flash is the stronger model overall, scoring 38.8 to 32.7 on the Noometry Index.
Is GLM-4.7-Flash or Qwen1.5-14B better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 33.1 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Qwen1.5-14B share?
15 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen1.5-14B has 17.