Model comparison
GLM-5.2 vs Qwen2.5-VL 72B Instruct
GLM-5.2 is the stronger model overall, scoring 51.1 to 29.9 on the Noometry Index.
Last verified . 1 shared benchmarks.
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. GLM-5.2 scores higher in 2 categories and Qwen2.5-VL 72B Instruct in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.3 to 20.7.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 62.6% for GLM-5.2 and 36% for Qwen2.5-VL 72B Instruct.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
- GLM-5.2 accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.2 | Qwen2.5-VL 72B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.1 | 29.9 |
| Released | 2026-06-13 | 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 | 51 | 6 |
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Category by category
Coding Not comparable
GLM-5.2: 51.3 (#41), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| LMArena Coding | 1485 | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), Qwen2.5-VL 72B Instruct: 18.6 (#144)
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| OSWorld | — | 5% |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Qwen2.5-VL 72B Instruct: 20.7 (#233)
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 62.6% | 36% |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |
Math Not comparable
GLM-5.2: 55.7 (#43), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
| LMArena Math | 1482 | — |
Knowledge Not comparable
GLM-5.2: 57.1 (#40), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| LMArena Expert | 1486 | — |
Multimodal Not comparable
GLM-5.2: —, Qwen2.5-VL 72B Instruct: 33.5 (#97)
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | — | 1107 |
| Video-MME | — | 73.5% |
| GeoBench | — | 62% |
| SpatialViz-Bench | — | 33.3% |
Multilingual Not comparable
GLM-5.2: 55.8 (#26), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1519 | — |
| LMArena French | 1479 | — |
| LMArena German | 1468 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1466 | — |
| LMArena Spanish | 1477 | — |
Instruction Following Not comparable
GLM-5.2: 76.9 (#34), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1465 | — |
Long Context Not comparable
GLM-5.2: 45.3 (#43), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | — |
Writing & Preference Not comparable
GLM-5.2: 70.4 (#21), Qwen2.5-VL 72B Instruct: —
| Benchmark | GLM-5.2 | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1462 | — |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1469 | — |
Frequently asked questions
Is GLM-5.2 better than Qwen2.5-VL 72B Instruct?
GLM-5.2 is the stronger model overall, scoring 51.1 to 29.9 on the Noometry Index.
Which is cheaper, GLM-5.2 or Qwen2.5-VL 72B Instruct?
GLM-5.2 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-5.2 does, with 1M tokens against 131K.
How many benchmarks do GLM-5.2 and Qwen2.5-VL 72B Instruct share?
1 benchmark has published results for both models. GLM-5.2 has 51 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.