Model comparison
GLM-5.3 vs Qwen2.5-VL 72B Instruct
GLM-5.3 is the stronger model overall, scoring 54.6 to 29.4 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where GLM-5.3 leads 45.2 to 20.0.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
- GLM-5.3 accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.3 | Qwen2.5-VL 72B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 54.6 | 29.4 |
| Released | 2026-08-14 | 2024-09 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $1.40 | $2.80 |
| Output $ / M tokens | $4.40 | $8.40 |
| Results tracked | 41 | 6 |
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Category by category
Coding Not comparable
GLM-5.3: 59.0 (#12), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| LMArena WebDev | 1623 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| LMArena Coding | 1499 | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.3 (#34), Qwen2.5-VL 72B Instruct: 18.4 (#136)
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| APEX-Agents | 56.6% | — |
| OSWorld | — | 5% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 45.2 (#42), Qwen2.5-VL 72B Instruct: 20.0 (#228)
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Kagi LLM Benchmark | — | 36% |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| LMArena Hard Prompts | 1491 | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.56 | — |
Math Not comparable
GLM-5.3: 62.1 (#27), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| LMArena Math | 1493 | — |
Knowledge Not comparable
GLM-5.3: 58.3 (#34), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| LMArena Expert | 1512 | — |
Multimodal Not comparable
GLM-5.3: —, Qwen2.5-VL 72B Instruct: 32.9 (#92)
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | — | 1107 |
| Video-MME | — | 73.5% |
| GeoBench | — | 62% |
| SpatialViz-Bench | — | 33.3% |
Multilingual Not comparable
GLM-5.3: 55.9 (#27), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1457 | — |
| LMArena Chinese | 1523 | — |
| LMArena French | 1499 | — |
| LMArena German | 1505 | — |
| LMArena Japanese | 1438 | — |
| LMArena Korean | 1478 | — |
| LMArena Russian | 1460 | — |
| LMArena Spanish | 1444 | — |
Instruction Following Not comparable
GLM-5.3: 77.8 (#15), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1481 | — |
Long Context Not comparable
GLM-5.3: 45.5 (#32), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1485 | — |
Writing & Preference Not comparable
GLM-5.3: 75.9 (#6), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Text | 1473 | — |
| LMArena Creative Writing | 1459 | — |
| EQ-Bench Creative Writing | 2075 | — |
| LMArena Multi-Turn | 1473 | — |
Frequently asked questions
Is GLM-5.3 better than Qwen2.5-VL 72B Instruct?
GLM-5.3 is the stronger model overall, scoring 54.6 to 29.4 on the Noometry Index.
Which is cheaper, GLM-5.3 or Qwen2.5-VL 72B Instruct?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 131K.
How many benchmarks do GLM-5.3 and Qwen2.5-VL 72B Instruct share?
0 benchmarks have published results for both models. GLM-5.3 has 41 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.