# GLM-4.5-Air vs o3-pro

> o3-pro is the stronger model overall, scoring 42.7 to 38.7 on the Noometry Index. GLM-4.5-Air costs 82× 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-4-5-air-vs-o3-pro
- Last updated: 2026-10-10
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. GLM-4.5-Air scores higher in 1 category and o3-pro in 4 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.1 to 41.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 43% for GLM-4.5-Air and 72.1% for o3-pro.
- GLM-4.5-Air is cheaper at $0.20 / $1.10 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 131K.
- GLM-4.5-Air has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.5-Air | o3-pro |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.7 | 42.7 |
| Rank | 169 | 94 |
| Context | 131K | 200K |
| Input $/M | $0.20 | $20 |
| Output $/M | $1.10 | $80 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.5-Air: 34.5 (#225)
- o3-pro: 55.1 (#22)

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| Aider Polyglot | — | 84.9% |
| GSO | 2.9% | — |
| WeirdML | — | 58.2% |
| LMArena Coding | 1397 | — |

## Reasoning

- GLM-4.5-Air: 23.3 (#166)
- o3-pro: 24.1 (#152)

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| Kagi LLM Benchmark | 43% | 72.1% |
| ARC-AGI-2 | — | 4.9% |
| ARC-AGI-1 | — | 59.3% |
| LMArena Hard Prompts | 1379 | — |
| DTBench | — | 86.9% |
| LMCA | — | 38.5% |
| Epoch Capabilities Index | — | 147.43 |
| ForecastBench | 59.2 | — |

## Math

- GLM-4.5-Air: 33.8 (#166)
- o3-pro: —

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| Omni-MATH | 39.1% | — |
| LMArena Math | 1396 | — |

## Knowledge

- GLM-4.5-Air: 34.6 (#184)
- o3-pro: 29.0 (#234)

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| Vectara Hallucination Rate | 9.3% | 23.3% |
| Humanity's Last Exam | 8.1% | — |
| MMLU-Pro | 76.2% | — |
| Confabulations | — | 14.2% |
| GPQA (HELM) | 59.4% | — |
| LMArena Expert | 1370 | — |

## Multilingual

- GLM-4.5-Air: 49.4 (#130)
- o3-pro: —

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| LMArena Non-English | 1366 | — |
| LMArena Chinese | 1427 | — |
| LMArena French | 1399 | — |
| LMArena German | 1376 | — |
| LMArena Japanese | 1348 | — |
| LMArena Korean | 1306 | — |
| LMArena Russian | 1373 | — |
| LMArena Spanish | 1385 | — |

## Instruction Following

- GLM-4.5-Air: 69.8 (#165)
- o3-pro: —

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| IFEval | 81.2% | — |
| LMArena Instruction Following | 1353 | — |

## Long Context

- GLM-4.5-Air: 41.6 (#130)
- o3-pro: 72.1 (#1)

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| LMArena Longer Query | 1367 | — |

## Writing & Preference

- GLM-4.5-Air: 56.1 (#134)
- o3-pro: 57.1 (#128)

| Benchmark | GLM-4.5-Air | o3-pro |
|---|---|---|
| LMArena Text | 1384 | — |
| LMArena Creative Writing | 1343 | — |
| Short-Story Creative Writing | — | 84.4% |
| WildBench | 78.9% | — |
| LMArena Multi-Turn | 1371 | — |

## FAQ

### Is GLM-4.5-Air better than o3-pro?

o3-pro is the stronger model overall, scoring 42.7 to 38.7 on the Noometry Index. GLM-4.5-Air costs 82× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.5-Air or o3-pro?

GLM-4.5-Air is cheaper. It lists at $0.20 per million input tokens and $1.10 per million output tokens; o3-pro lists at $20 and $80.

### Is GLM-4.5-Air or o3-pro better for coding?

o3-pro scores higher on coding benchmarks: 55.1 versus 34.5 in the Noometry coding category.

### Which has the bigger context window?

o3-pro does, with 200K tokens against 131K.

### How many benchmarks do GLM-4.5-Air and o3-pro share?

2 benchmarks have published results for both models. GLM-4.5-Air has 27 scored results on Noometry and o3-pro has 12.