# GLM-4.6 vs Hunyuan Large 2025 02 10

> GLM-4.6 is the stronger model overall, scoring 41.4 to 38.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-hunyuan-large
- Last updated: 2026-10-10
- Shared benchmarks: 12

## Summary

- They share 12 benchmarks with published results for both. GLM-4.6 scores higher in 7 categories and Hunyuan Large 2025 02 10 in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 48.7.
- GLM-4.6 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Tencent |
| Noometry Index | 41.4 | 38.6 |
| Rank | 135 | 184 |
| Context | 205K | — |
| Input $/M | $0.60 | — |
| Output $/M | $2.20 | — |
| Weights | Open | Proprietary |

## Coding

- GLM-4.6: 40.1 (#148)
- Hunyuan Large 2025 02 10: 38.2 (#181)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Coding | 1449 | 1307 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.3 (#66)
- Hunyuan Large 2025 02 10: —

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |

## Reasoning

- GLM-4.6: 23.7 (#172)
- Hunyuan Large 2025 02 10: 25.5 (#148)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Hard Prompts | 1440 | 1286 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |

## Math

- GLM-4.6: 39.1 (#111)
- Hunyuan Large 2025 02 10: 35.8 (#178)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Math | 1432 | 1281 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 40.2 (#124)
- Hunyuan Large 2025 02 10: 35.1 (#188)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Expert | 1431 | 1276 |
| Vectara Hallucination Rate | 9.5% | — |

## Multilingual

- GLM-4.6: 53.5 (#66)
- Hunyuan Large 2025 02 10: 42.0 (#200)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Non-English | 1426 | 1265 |
| LMArena Chinese | 1499 | 1346 |
| LMArena Russian | 1419 | 1266 |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Spanish | 1436 | — |

## Instruction Following

- GLM-4.6: 74.3 (#98)
- Hunyuan Large 2025 02 10: 67.3 (#197)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1277 |

## Long Context

- GLM-4.6: 43.4 (#94)
- Hunyuan Large 2025 02 10: 40.8 (#149)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Longer Query | 1422 | 1341 |

## Writing & Preference

- GLM-4.6: 61.1 (#90)
- Hunyuan Large 2025 02 10: 48.7 (#197)

| Benchmark | GLM-4.6 | Hunyuan Large 2025 02 10 |
|---|---|---|
| LMArena Text | 1440 | 1288 |
| LMArena Creative Writing | 1411 | 1264 |
| LMArena Multi-Turn | 1427 | 1284 |
| EQ-Bench Creative Writing | 1411 | — |

## FAQ

### Is GLM-4.6 better than Hunyuan Large 2025 02 10?

GLM-4.6 is the stronger model overall, scoring 41.4 to 38.6 on the Noometry Index.

### Is GLM-4.6 or Hunyuan Large 2025 02 10 better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 38.2 in the Noometry coding category.

### How many benchmarks do GLM-4.6 and Hunyuan Large 2025 02 10 share?

12 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Hunyuan Large 2025 02 10 has 12.