# GLM-4.7 vs Qwen2.5 72B Instruct

> GLM-4.7 is the stronger model overall, scoring 41.7 to 31.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-7-vs-qwen2-5-72b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Qwen2.5 72B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 26.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 8.1% for Qwen2.5 72B Instruct.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- GLM-4.7 accepts more context: 205K tokens versus 131K.

## Snapshot

| | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.7 | 31.5 |
| Rank | 117 | 259 |
| Context | 205K | 131K |
| Input $/M | $0.60 | $1.40 |
| Output $/M | $2.20 | $5.60 |
| Weights | Open | Open |

## Coding

- GLM-4.7: 43.6 (#74)
- Qwen2.5 72B Instruct: 32.8 (#256)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Coding | 1454 | 1293 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| WeirdML | — | 16% |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
| ALE-Bench | 399.48 | — |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- Qwen2.5 72B Instruct: 22.0 (#124)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
| Vending-Bench 2 | 2,377 | — |

## Reasoning

- GLM-4.7: 23.7 (#161)
- Qwen2.5 72B Instruct: 21.4 (#197)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1271 |
| Epoch Capabilities Index | 143.53 | 129.01 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| DTBench | — | 62.9% |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| ForecastBench | — | 57.5 |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |

## Math

- GLM-4.7: 37.7 (#92)
- Qwen2.5 72B Instruct: 18.1 (#281)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 8.1% |
| LMArena Math | 1423 | 1283 |
| ProofBench | 6% | — |
| Omni-MATH | — | 33% |
| MATH Level 5 | — | 63.2% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- Qwen2.5 72B Instruct: 26.9 (#247)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 83.3% | 49.1% |
| LMArena Expert | 1424 | 1245 |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 63.1% |
| Confabulations | — | 19.1% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |

## Multilingual

- GLM-4.7: 53.0 (#75)
- Qwen2.5 72B Instruct: 41.2 (#208)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1252 |
| LMArena Chinese | 1495 | 1272 |
| LMArena French | 1432 | 1279 |
| LMArena German | 1426 | 1234 |
| LMArena Japanese | 1439 | 1180 |
| LMArena Korean | 1401 | 1188 |
| LMArena Russian | 1423 | 1264 |
| LMArena Spanish | 1433 | 1255 |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- Qwen2.5 72B Instruct: 65.7 (#216)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1254 |
| IFEval | — | 80.6% |

## Long Context

- GLM-4.7: 42.8 (#111)
- Qwen2.5 72B Instruct: 38.9 (#183)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1282 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- Qwen2.5 72B Instruct: 46.9 (#210)

| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1436 | 1269 |
| LMArena Creative Writing | 1401 | 1221 |
| LMArena Multi-Turn | 1446 | 1272 |
| EQ-Bench Creative Writing | 1413 | — |
| WildBench | — | 80.2% |

## FAQ

### Is GLM-4.7 better than Qwen2.5 72B Instruct?

GLM-4.7 is the stronger model overall, scoring 41.7 to 31.5 on the Noometry Index.

### Which is cheaper, GLM-4.7 or Qwen2.5 72B Instruct?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.

### Is GLM-4.7 or Qwen2.5 72B Instruct better for coding?

GLM-4.7 scores higher on coding benchmarks: 43.6 versus 32.8 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 131K.

### How many benchmarks do GLM-4.7 and Qwen2.5 72B Instruct share?

20 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen2.5 72B Instruct has 43.