# GLM-4.6 vs Qwen2.5 7B Instruct

> GLM-4.6 is the stronger model overall, scoring 40.9 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 3.3× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in math, where GLM-4.6 leads 36.4 to 11.8.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 131K.

## Snapshot

| | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 28.6 |
| Rank | 132 | 314 |
| Context | 205K | 131K |
| Input $/M | $0.60 | $0.17 |
| Output $/M | $2.20 | $0.70 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- Qwen2.5 7B Instruct: 35.9 (#204)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1449 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- Qwen2.5 7B Instruct: 23.5 (#117)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| BALROG | — | 7.8% |

## Reasoning

- GLM-4.6: 23.1 (#168)
- Qwen2.5 7B Instruct: 14.3 (#317)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1440 | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
| Epoch Capabilities Index | — | 118.44 |

## Math

- GLM-4.6: 36.4 (#115)
- Qwen2.5 7B Instruct: 11.8 (#300)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1431 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 39.6 (#118)
- Qwen2.5 7B Instruct: 17.0 (#281)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| Vectara Hallucination Rate | 9.5% | — |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1432 | — |
| MMLU | — | 72.9% |

## Multilingual

- GLM-4.6: 53.6 (#62)
- Qwen2.5 7B Instruct: —

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1500 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1401 | — |
| LMArena Russian | 1418 | — |
| LMArena Spanish | 1436 | — |

## Instruction Following

- GLM-4.6: 74.5 (#93)
- Qwen2.5 7B Instruct: 63.4 (#226)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1410 | — |

## Long Context

- GLM-4.6: 43.4 (#91)
- Qwen2.5 7B Instruct: —

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1423 | — |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- Qwen2.5 7B Instruct: 48.8 (#194)

| Benchmark | GLM-4.6 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is GLM-4.6 better than Qwen2.5 7B Instruct?

GLM-4.6 is the stronger model overall, scoring 40.9 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 3.3× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.

### Is GLM-4.6 or Qwen2.5 7B Instruct better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 35.9 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

### How many benchmarks do GLM-4.6 and Qwen2.5 7B Instruct share?

0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen2.5 7B Instruct has 15.