# GLM-5.3-Flash vs GPT-4.5

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 36.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-4-5
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-4.5 in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 32.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 37.8% for GPT-4.5.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 36.9 |
| Rank | 45 | 200 |
| Context | 1M | — |
| Input $/M | $0.15 | — |
| Output $/M | $0.50 | — |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- GPT-4.5: 41.8 (#102)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Coding | 1506 | 1397 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 44.9% |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| WeirdML | — | 39.4% |
| LiveBench Coding | — | 75.2% |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- GPT-4.5: 27.6 (#92)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Cybench | — | 17.5% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- GPT-4.5: 14.1 (#320)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1404 |
| Epoch Capabilities Index | 151.9 | 136.74 |
| ARC-AGI-2 | — | 0.8% |
| SimpleBench | — | 34.5% |
| ARC-AGI-1 | — | 10.3% |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 3.2% |
| LiveBench Reasoning | — | 71.1% |
| Mystery Game Puzzles | 8% | — |
| LiveBench Data Analysis | — | 64.3% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 61.7 |
| LiveBench | — | 69% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- GPT-4.5: 31.0 (#200)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 37.8% |
| LMArena Math | 1499 | 1412 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 69.3% |
| MATH Level 5 | — | 78.6% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- GPT-4.5: 32.4 (#205)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| GPQA Diamond | 90.2% | 68.7% |
| LMArena Expert | 1512 | 1394 |
| Humanity's Last Exam | — | 5.4% |
| Confabulations | — | 13.6% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- GPT-4.5: 37.0 (#68)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Vision | 1304 | 1195 |
| VPCT | — | 45% |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- GPT-4.5: 52.7 (#78)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Non-English | 1462 | 1413 |
| LMArena Chinese | 1529 | 1421 |
| LMArena French | 1495 | 1417 |
| LMArena German | 1457 | 1457 |
| LMArena Japanese | 1420 | 1416 |
| LMArena Korean | 1443 | 1392 |
| LMArena Russian | 1472 | 1419 |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- GPT-4.5: 72.8 (#129)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Instruction Following | 1480 | 1404 |
| LiveBench Instruction Following | — | 72.3% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- GPT-4.5: 40.4 (#150)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Longer Query | 1479 | 1406 |
| Fiction.LiveBench | — | 63.9% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- GPT-4.5: 57.1 (#129)

| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Text | 1471 | 1417 |
| LMArena Creative Writing | 1440 | 1394 |
| LMArena Multi-Turn | 1471 | 1444 |
| Short-Story Creative Writing | — | 75.6% |
| EQ-Bench Creative Writing | — | 1258 |
| LiveBench Language | — | 61.5% |

## FAQ

### Is GLM-5.3-Flash better than GPT-4.5?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 36.9 on the Noometry Index.

### Is GLM-5.3-Flash or GPT-4.5 better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 41.8 in the Noometry coding category.

### How many benchmarks do GLM-5.3-Flash and GPT-4.5 share?

20 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-4.5 has 42.