# GLM-4.5 vs GPT-5 Pro

> GPT-5 Pro is the stronger model overall, scoring 46.4 to 42.0 on the Noometry Index. GLM-4.5 costs 41× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-5-vs-gpt-5-pro
- Last updated: 2026-10-10
- Shared benchmarks: 4

## Summary

- They share 4 benchmarks with published results for both. GLM-4.5 scores higher in 0 categories and GPT-5 Pro in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Pro leads 56.7 to 35.9.
- The biggest single-benchmark swing is Humanity's Last Exam: 8.3% for GLM-4.5 and 31.6% for GPT-5 Pro.
- GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $15 / $120 for GPT-5 Pro.
- GPT-5 Pro accepts more context: 400K tokens versus 131K.
- GLM-4.5 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 46.4 |
| Rank | 122 | 64 |
| Context | 131K | 400K |
| Input $/M | $0.60 | $15 |
| Output $/M | $2.20 | $120 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.5: 41.4 (#125)
- GPT-5 Pro: 44.0 (#80)

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| WeirdML | 40.6% | 60.4% |
| AlgoTune | 1.52 | 1.31 |
| SWE-bench Verified (bash only) | 54.2% | — |
| LMArena Coding | 1434 | — |
| ALE-Bench | 344.82 | — |

## Reasoning

- GLM-4.5: 28.6 (#100)
- GPT-5 Pro: 38.9 (#62)

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| Kagi LLM Benchmark | 57.9% | 76.8% |
| ARC-AGI-2 | — | 18.3% |
| SimpleBench | — | 61.6% |
| ARC-AGI-1 | — | 70.2% |
| EnigmaEval | — | 18.8% |
| LMArena Hard Prompts | 1429 | — |
| Epoch Capabilities Index | — | 150.28 |

## Math

- GLM-4.5: 39.0 (#116)
- GPT-5 Pro: 48.5 (#63)

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 19.5% |
| LMArena Math | 1427 | — |
| FrontierMath Tier 4 (v1) | — | 14.6% |

## Knowledge

- GLM-4.5: 35.9 (#179)
- GPT-5 Pro: 56.7 (#42)

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| Humanity's Last Exam | 8.3% | 31.6% |
| Confabulations | 11.3% | — |
| LMArena Expert | 1433 | — |

## Multilingual

- GLM-4.5: 52.8 (#77)
- GPT-5 Pro: —

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1465 | — |
| LMArena French | 1418 | — |
| LMArena German | 1407 | — |
| LMArena Japanese | 1415 | — |
| LMArena Korean | 1380 | — |
| LMArena Russian | 1414 | — |
| LMArena Spanish | 1454 | — |

## Instruction Following

- GLM-4.5: 74.1 (#104)
- GPT-5 Pro: —

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| LMArena Instruction Following | 1404 | — |

## Long Context

- GLM-4.5: 38.2 (#201)
- GPT-5 Pro: —

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| Fiction.LiveBench | 58.3% | — |
| LMArena Longer Query | 1412 | — |

## Writing & Preference

- GLM-4.5: 57.5 (#127)
- GPT-5 Pro: —

| Benchmark | GLM-4.5 | GPT-5 Pro |
|---|---|---|
| LMArena Text | 1430 | — |
| LMArena Creative Writing | 1395 | — |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
| LMArena Multi-Turn | 1415 | — |

## FAQ

### Is GLM-4.5 better than GPT-5 Pro?

GPT-5 Pro is the stronger model overall, scoring 46.4 to 42.0 on the Noometry Index. GLM-4.5 costs 41× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.5 or GPT-5 Pro?

GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; GPT-5 Pro lists at $15 and $120.

### Is GLM-4.5 or GPT-5 Pro better for coding?

GPT-5 Pro scores higher on coding benchmarks: 44.0 versus 41.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5 Pro does, with 400K tokens against 131K.

### How many benchmarks do GLM-4.5 and GPT-5 Pro share?

4 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and GPT-5 Pro has 12.