# GLM-4.6V vs o3-pro

> o3-pro is the stronger model overall, scoring 42.7 to 40.9 on the Noometry Index. GLM-4.6V costs 78× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-o3-pro
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in long context, where o3-pro leads 72.1 to 41.3.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 128K.
- GLM-4.6V has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.6V | o3-pro |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 42.7 |
| Rank | 133 | 94 |
| Context | 128K | 200K |
| Input $/M | $0.30 | $20 |
| Output $/M | $0.90 | $80 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.6V: 40.2 (#128)
- o3-pro: 55.1 (#22)

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| Aider Polyglot | — | 84.9% |
| WeirdML | — | 58.2% |
| LMArena Coding | 1390 | — |

## Reasoning

- GLM-4.6V: 26.5 (#117)
- o3-pro: 24.1 (#152)

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| ARC-AGI-2 | — | 4.9% |
| Kagi LLM Benchmark | — | 72.1% |
| ARC-AGI-1 | — | 59.3% |
| LMArena Hard Prompts | 1368 | — |
| DTBench | — | 86.9% |
| LMCA | — | 38.5% |
| Epoch Capabilities Index | — | 147.43 |

## Knowledge

- GLM-4.6V: 37.4 (#148)
- o3-pro: 29.0 (#234)

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| Confabulations | — | 14.2% |
| Vectara Hallucination Rate | — | 23.3% |
| LMArena Expert | 1372 | — |

## Multimodal

- GLM-4.6V: 34.2 (#83)
- o3-pro: —

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.8 (#135)
- o3-pro: —

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1426 | — |
| LMArena Russian | 1341 | — |

## Instruction Following

- GLM-4.6V: 71.6 (#145)
- o3-pro: —

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| LMArena Instruction Following | 1351 | — |

## Long Context

- GLM-4.6V: 41.3 (#138)
- o3-pro: 72.1 (#1)

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| LMArena Longer Query | 1358 | — |

## Writing & Preference

- GLM-4.6V: 56.8 (#132)
- o3-pro: 57.1 (#128)

| Benchmark | GLM-4.6V | o3-pro |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| Short-Story Creative Writing | — | 84.4% |
| LMArena Multi-Turn | 1359 | — |

## FAQ

### Is GLM-4.6V better than o3-pro?

o3-pro is the stronger model overall, scoring 42.7 to 40.9 on the Noometry Index. GLM-4.6V costs 78× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6V or o3-pro?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; o3-pro lists at $20 and $80.

### Is GLM-4.6V or o3-pro better for coding?

o3-pro scores higher on coding benchmarks: 55.1 versus 40.2 in the Noometry coding category.

### Which has the bigger context window?

o3-pro does, with 200K tokens against 128K.

### How many benchmarks do GLM-4.6V and o3-pro share?

0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and o3-pro has 12.