Model comparison
GLM-4.7-Flash vs Qwen1.5 4b Chat
GLM-4.7-Flash is the stronger model overall, scoring 38.4 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-4.7-Flash 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-4.7-Flash leads 47.7 to 23.9.
Side by side
| GLM-4.7-Flash | Qwen1.5 4b Chat | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.4 | 28.3 |
| Released | 2026-01-19 | — |
| Weights | Open | Open |
| Context window | 200K | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.06 | — |
| Output $ / M tokens | $0.40 | — |
| Results tracked | 21 | 13 |
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Category by category
Coding GLM-4.7-Flash leads
GLM-4.7-Flash: 40.0 (#133), Qwen1.5 4b Chat: 28.6 (#305)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Coding | 1383 | 999 |
Reasoning GLM-4.7-Flash leads
GLM-4.7-Flash: 20.2 (#224), Qwen1.5 4b Chat: 17.7 (#277)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Hard Prompts | 1356 | 976 |
| Chess Puzzles | 0% | — |
Math GLM-4.7-Flash leads
GLM-4.7-Flash: 34.4 (#159), Qwen1.5 4b Chat: 27.9 (#233)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Math | 1354 | 1026 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
Knowledge GLM-4.7-Flash leads
GLM-4.7-Flash: 35.4 (#177), Qwen1.5 4b Chat: 26.2 (#252)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Expert | 1357 | 980 |
| GPQA Diamond | 60.5% | — |
| Vectara Hallucination Rate | 9.3% | — |
Multilingual GLM-4.7-Flash leads
GLM-4.7-Flash: 46.8 (#152), Qwen1.5 4b Chat: 24.2 (#285)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Non-English | 1331 | 979 |
| LMArena Chinese | 1403 | 1024 |
| LMArena German | 1336 | 902 |
| LMArena Russian | 1333 | 952 |
| LMArena French | 1332 | — |
| LMArena Korean | 1284 | — |
| LMArena Spanish | 1351 | — |
Instruction Following GLM-4.7-Flash leads
GLM-4.7-Flash: 70.3 (#162), Qwen1.5 4b Chat: 49.3 (#295)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Instruction Following | 1327 | 979 |
Long Context GLM-4.7-Flash leads
GLM-4.7-Flash: 40.9 (#143), Qwen1.5 4b Chat: 30.1 (#285)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Longer Query | 1345 | 988 |
Writing & Preference GLM-4.7-Flash leads
GLM-4.7-Flash: 47.7 (#206), Qwen1.5 4b Chat: 23.9 (#304)
| Benchmark | GLM-4.7-Flash | Qwen1.5 4b Chat |
|---|---|---|
| LMArena Text | 1351 | 997 |
| LMArena Creative Writing | 1297 | 969 |
| LMArena Multi-Turn | 1342 | 977 |
| EQ-Bench Creative Writing | 1125 | — |
Frequently asked questions
Is GLM-4.7-Flash better than Qwen1.5 4b Chat?
GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 28.3 on the Noometry Index.
Is GLM-4.7-Flash or Qwen1.5 4b Chat better for coding?
GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 28.6 in the Noometry coding category.
How many benchmarks do GLM-4.7-Flash and Qwen1.5 4b Chat share?
13 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen1.5 4b Chat has 13.