Model comparison
GLM-4.7-Flash vs Qwen1.5-110B
GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 33.6 on the Noometry Index.
Last verified . 16 shared benchmarks.
Alibaba (Qwen)Summary
- They share 16 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and Qwen1.5-110B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where GLM-4.7-Flash leads 46.8 to 33.8.
Side by side
| GLM-4.7-Flash | Qwen1.5-110B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.4 | 33.6 |
| Released | 2026-01-19 | 2024-04-25 |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 20 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.0 (#133), Qwen1.5-110B: 32.4 (#260)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Coding | 1383 | 1184 |
| BigCodeBench Instruct | — | 35% |
| BigCodeBench Complete | — | 44.4% |
Reasoning Qwen1.5-110B leads
GLM-4.7-Flash: 20.2 (#224), Qwen1.5-110B: 21.7 (#192)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1168 |
| Chess Puzzles | 0% | — |
| ForecastBench | — | 57.7 |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 34.4 (#159), Qwen1.5-110B: 31.1 (#199)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Math | 1354 | 1185 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.4 (#177), Qwen1.5-110B: 30.7 (#217)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Expert | 1357 | 1144 |
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.8 (#152), Qwen1.5-110B: 33.8 (#245)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Non-English | 1331 | 1142 |
| LMArena Chinese | 1403 | 1206 |
| LMArena French | 1332 | 1150 |
| LMArena German | 1336 | 1123 |
| LMArena Korean | 1284 | 1044 |
| LMArena Russian | 1333 | 1118 |
| LMArena Spanish | 1351 | 1141 |
| LMArena Japanese | — | 1074 |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.3 (#162), Qwen1.5-110B: 60.6 (#247)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1158 |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#143), Qwen1.5-110B: 35.0 (#237)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Longer Query | 1345 | 1157 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.7 (#206), Qwen1.5-110B: 38.1 (#250)
| Benchmark | GLM-4.7-Flash | Qwen1.5-110B |
|---|---|---|
| LMArena Text | 1351 | 1175 |
| LMArena Creative Writing | 1297 | 1148 |
| LMArena Multi-Turn | 1342 | 1160 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen1.5-110B?
GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 33.6 on the Noometry Index.
Is GLM-4.7-Flash or Qwen1.5-110B better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 32.4 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Qwen1.5-110B share?
16 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen1.5-110B has 20.