# GLM-4.6 vs Laguna M.1

> GLM-4.6 is the stronger model overall, scoring 40.9 to 31.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-laguna-m-1
- Last updated: 2026-10-10
- Shared benchmarks: 1

## Summary

- They share 1 benchmark with published results for both. GLM-4.6 scores higher in 3 categories and Laguna M.1 in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.6 leads 36.4 to 20.3.
- Laguna M.1 accepts more context: 262K tokens versus 205K.

## Snapshot

| | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Poolside |
| Noometry Index | 40.9 | 31.9 |
| Rank | 132 | 252 |
| Context | 205K | 262K |
| Input $/M | $0.60 | — |
| Output $/M | $2.20 | — |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- Laguna M.1: 36.4 (#195)

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena WebDev | 1339 | 1348 |
| SWE-bench Verified (bash only) | 55.4% | — |
| SciCode | 38.4% | — |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- Laguna M.1: —

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |

## Reasoning

- GLM-4.6: 23.1 (#168)
- Laguna M.1: 21.9 (#187)

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| LMArena Hard Prompts | 1440 | — |
| Surface Evolver Bench | — | 15.6% |

## Math

- GLM-4.6: 36.4 (#115)
- Laguna M.1: 20.3 (#276)

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| ProofBench | — | 0% |
| LMArena Math | 1431 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 39.6 (#118)
- Laguna M.1: —

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1432 | — |

## Multilingual

- GLM-4.6: 53.6 (#62)
- Laguna M.1: —

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| 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)
- Laguna M.1: —

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Instruction Following | 1410 | — |

## Long Context

- GLM-4.6: 43.4 (#91)
- Laguna M.1: —

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Longer Query | 1423 | — |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- Laguna M.1: —

| Benchmark | GLM-4.6 | Laguna M.1 |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is GLM-4.6 better than Laguna M.1?

GLM-4.6 is the stronger model overall, scoring 40.9 to 31.9 on the Noometry Index.

### Is GLM-4.6 or Laguna M.1 better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 36.4 in the Noometry coding category.

### Which has the bigger context window?

Laguna M.1 does, with 262K tokens against 205K.

### How many benchmarks do GLM-4.6 and Laguna M.1 share?

1 benchmark has published results for both models. GLM-4.6 has 29 scored results on Noometry and Laguna M.1 has 3.