# GLM-4.6 vs o1-pro

> GLM-4.6 is the stronger model overall, scoring 41.4 to 31.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-o1-pro
- Last updated: 2026-10-12
- Shared benchmarks: 0

## Summary

- The widest gap is in knowledge, where GLM-4.6 leads 40.2 to 29.7.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $150 / $600 for o1-pro.
- GLM-4.6 accepts more context: 205K tokens versus 200K.
- GLM-4.6 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.6 | o1-pro |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 31.5 |
| Rank | 135 | 271 |
| Context | 205K | 200K |
| Input $/M | $0.60 | $150 |
| Output $/M | $2.20 | $600 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.6: 40.1 (#148)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.3 (#66)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |

## Reasoning

- GLM-4.6: 23.7 (#172)
- o1-pro: 20.4 (#239)

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| ARC-AGI-1 | — | 23.3% |
| CritPt | 1.1% | — |
| EnigmaEval | — | 6.1% |
| LMArena Hard Prompts | 1440 | — |

## Math

- GLM-4.6: 39.1 (#111)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Math | 1432 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 40.2 (#124)
- o1-pro: 29.7 (#234)

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| Humanity's Last Exam | — | 8.1% |
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |

## Multilingual

- GLM-4.6: 53.5 (#66)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |

## Instruction Following

- GLM-4.6: 74.3 (#98)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Instruction Following | 1410 | — |

## Long Context

- GLM-4.6: 43.4 (#94)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Longer Query | 1422 | — |

## Writing & Preference

- GLM-4.6: 61.1 (#90)
- o1-pro: —

| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is GLM-4.6 better than o1-pro?

GLM-4.6 is the stronger model overall, scoring 41.4 to 31.5 on the Noometry Index.

### Which is cheaper, GLM-4.6 or o1-pro?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o1-pro lists at $150 and $600.

### Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 200K.

### How many benchmarks do GLM-4.6 and o1-pro share?

0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and o1-pro has 3.