Model comparison
GLM-5.3 vs Qwen2-72B
GLM-5.3 is the stronger model overall, scoring 54.8 to 30.0 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Qwen2-72B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3 leads 58.3 to 21.2.
- The biggest single-benchmark swing is WeirdML: 75.4% for GLM-5.3 and 11.3% for Qwen2-72B.
Side by side
| GLM-5.3 | Qwen2-72B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 54.8 | 30.0 |
| Released | 2026-08-14 | 2024-06-07 |
| Weights | Open | Open |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $1.40 | — |
| Output $ / M tokens | $4.40 | — |
| Results tracked | 42 | 26 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Qwen2-72B: 29.1 (#310)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| WeirdML | 75.4% | 11.3% |
| LMArena Coding | 1496 | 1196 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| BigCodeBench Instruct | — | 38.5% |
| BigCodeBench Complete | — | 54% |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), Qwen2-72B: 17.0 (#146)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| APEX-Agents | 56.6% | — |
| TheAgentCompany | — | 1.1% |
| METR Time Horizons | — | 29.9% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Qwen2-72B: 23.2 (#181)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| LMArena Hard Prompts | 1489 | 1191 |
| Epoch Capabilities Index | 155.61 | 125.28 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Qwen2-72B: 30.2 (#236)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| LMArena Math | 1489 | 1235 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| MATH Level 5 | — | 39.1% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Qwen2-72B: 21.2 (#275)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| GPQA Diamond | 90.9% | 40.8% |
| LMArena Expert | 1516 | 1171 |
| SimpleQA Verified | 41% | — |
| MMLU | — | 82.4% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), Qwen2-72B: 35.9 (#244)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| LMArena Non-English | 1457 | 1176 |
| LMArena Chinese | 1528 | 1240 |
| LMArena French | 1499 | 1170 |
| LMArena German | 1499 | 1151 |
| LMArena Japanese | 1453 | 1111 |
| LMArena Korean | 1472 | 1083 |
| LMArena Russian | 1463 | 1169 |
| LMArena Spanish | 1460 | 1169 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Qwen2-72B: 61.7 (#241)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| LMArena Instruction Following | 1477 | 1181 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), Qwen2-72B: 36.1 (#235)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| LMArena Longer Query | 1482 | 1192 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Qwen2-72B: 40.8 (#241)
| Benchmark | GLM-5.3 | Qwen2-72B |
|---|---|---|
| LMArena Text | 1471 | 1203 |
| LMArena Creative Writing | 1457 | 1181 |
| LMArena Multi-Turn | 1472 | 1196 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than Qwen2-72B?
GLM-5.3 is the stronger model overall, scoring 54.8 to 30.0 on the Noometry Index.
Is GLM-5.3 or Qwen2-72B better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 29.1 in the Noometry coding category.
How many benchmarks do GLM-5.3 and Qwen2-72B share?
20 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Qwen2-72B has 26.