# GLM-5.2 vs Qwen2.5 7B Instruct

> GLM-5.2 is the stronger model overall, scoring 50.9 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 7.0× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-2-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 6

## Summary

- They share 6 benchmarks with published results for both. GLM-5.2 scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.4 to 11.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 2.5% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 131K.

## Snapshot

| | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 50.9 | 28.6 |
| Rank | 38 | 314 |
| Context | 1M | 131K |
| Input $/M | $1.40 | $0.17 |
| Output $/M | $4.40 | $0.70 |
| Weights | Open | Open |

## Coding

- GLM-5.2: 50.9 (#37)
- Qwen2.5 7B Instruct: 35.9 (#204)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1484 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,047 | — |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- Qwen2.5 7B Instruct: 23.5 (#117)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| BALROG | — | 7.8% |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- Qwen2.5 7B Instruct: 14.3 (#317)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| DTBench | 93.6% | 47.7% |
| LMCA | 45.8% | 6.4% |
| Epoch Capabilities Index | 151.76 | 118.44 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Mystery Game Puzzles | 19% | — |
| Surface Evolver Bench | 55.6% | — |

## Math

- GLM-5.2: 55.4 (#35)
- Qwen2.5 7B Instruct: 11.8 (#300)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 2.5% |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1482 | — |

## Knowledge

- GLM-5.2: 57.1 (#36)
- Qwen2.5 7B Instruct: 17.0 (#281)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 91.9% | 35.5% |
| SimpleQA Verified | 34.2% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1484 | — |
| MMLU | — | 72.9% |

## Multilingual

- GLM-5.2: 56.0 (#25)
- Qwen2.5 7B Instruct: —

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1518 | — |
| LMArena French | 1476 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1467 | — |
| LMArena Spanish | 1477 | — |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- Qwen2.5 7B Instruct: 63.4 (#226)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1465 | — |

## Long Context

- GLM-5.2: 45.3 (#43)
- Qwen2.5 7B Instruct: —

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1479 | — |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- Qwen2.5 7B Instruct: 48.8 (#194)

| Benchmark | GLM-5.2 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1465 | — |
| EQ-Bench Creative Writing | 1757 | — |
| WildBench | — | 73.1% |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1468 | — |

## FAQ

### Is GLM-5.2 better than Qwen2.5 7B Instruct?

GLM-5.2 is the stronger model overall, scoring 50.9 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 7.0× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.2 or Qwen2.5 7B Instruct?

Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or Qwen2.5 7B Instruct better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 35.9 in the Noometry coding category.

### 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 7B Instruct share?

6 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Qwen2.5 7B Instruct has 15.