Model comparison
GLM-5.3-Flash vs Qwen1.5-7B
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.8 on the Noometry Index.
Last verified . 12 shared benchmarks.
Alibaba (Qwen)Summary
- They share 12 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen1.5-7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3-Flash leads 65.4 to 29.8.
Side by side
| GLM-5.3-Flash | Qwen1.5-7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 49.6 | 30.8 |
| Released | 2026-08-20 | 2024-02-04 |
| Weights | Open | Open |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 36 | 13 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.7 (#26), Qwen1.5-7B: 31.6 (#271)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Coding | 1506 | 1107 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.3 (#41), Qwen1.5-7B: —
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 31.7 (#69), Qwen1.5-7B: 19.5 (#237)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1066 |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.9 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#38), Qwen1.5-7B: 29.0 (#222)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Math | 1499 | 1080 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#33), Qwen1.5-7B: 28.2 (#239)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Expert | 1512 | 1055 |
| GPQA Diamond | 90.2% | — |
| MMLU | — | 62.6% |
Multimodal Not comparable
GLM-5.3-Flash: 42.6 (#25), Qwen1.5-7B: —
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Vision | 1304 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.2 (#24), Qwen1.5-7B: 28.6 (#266)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Non-English | 1462 | 1058 |
| LMArena Chinese | 1529 | 1140 |
| LMArena Russian | 1472 | 1006 |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Spanish | 1471 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.8 (#17), Qwen1.5-7B: 54.4 (#276)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1058 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.3 (#41), Qwen1.5-7B: 33.1 (#261)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Longer Query | 1479 | 1090 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.4 (#49), Qwen1.5-7B: 29.8 (#288)
| Benchmark | GLM-5.3-Flash | Qwen1.5-7B |
|---|---|---|
| LMArena Text | 1471 | 1084 |
| LMArena Creative Writing | 1440 | 1035 |
| LMArena Multi-Turn | 1471 | 1061 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen1.5-7B?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.8 on the Noometry Index.
Is GLM-5.3-Flash or Qwen1.5-7B better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 31.6 in the Noometry coding category.
How many benchmarks do GLM-5.3-Flash and Qwen1.5-7B share?
12 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Qwen1.5-7B has 13.