# GLM-5.3-Flash vs GPT-4o

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 28.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-4o
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-4o in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.1 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 6.4% for GPT-4o.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 28.5 |
| Rank | 45 | 316 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $2.50 |
| Output $/M | $0.50 | $10 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- GPT-4o: 25.1 (#323)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| LMArena Coding | 1506 | 1297 |
| SWE-bench Verified | — | 31% |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 21.6% |
| Aider Polyglot | — | 45.3% |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| GSO | — | 0% |
| WeirdML | — | 25.1% |
| BigCodeBench Instruct | — | 51.1% |
| LiveBench Coding | — | 51.4% |
| BigCodeBench Complete | — | 61.1% |
| CadEval | — | 26% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 72.2% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- GPT-4o: 20.8 (#133)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDPval | — | 9.9% |
| TheAgentCompany | — | 8.6% |
| Cybench | — | 12.5% |
| BALROG | — | 32.3% |
| GDP.pdf | 14% | — |
| LMArena Search | — | 1006 |
| METR Time Horizons | — | 40.8% |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- GPT-4o: 9.4 (#338)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| CritPt | 15.4% | 0% |
| Chess Puzzles | 14% | 13% |
| LMArena Hard Prompts | 1490 | 1281 |
| Epoch Capabilities Index | 151.9 | 128.97 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 17.8% |
| ARC-AGI-1 | — | 4.5% |
| EnigmaEval | — | 0.8% |
| LiveBench Reasoning | — | 55.8% |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 64.5% |
| LiveBench Data Analysis | — | 60.9% |
| LMCA | — | 16.6% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 57.7 |
| LiveBench | — | 55.3% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- GPT-4o: 10.2 (#303)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 0.4% |
| OTIS Mock AIME 2024-2025 | 93.9% | 6.4% |
| LMArena Math | 1499 | 1285 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 29.3% |
| LiveBench Math | — | 49.5% |
| MATH Level 5 | — | 53.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- GPT-4o: 28.8 (#235)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| GPQA Diamond | 90.2% | 49.2% |
| LMArena Expert | 1512 | 1250 |
| Humanity's Last Exam | — | 2.7% |
| SimpleQA Verified | — | 26% |
| MMLU-Pro | — | 71.3% |
| Confabulations | — | 15.3% |
| Vectara Hallucination Rate | — | 9.6% |
| GPQA (HELM) | — | 52% |
| MMLU | — | 88.1% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- GPT-4o: 33.9 (#85)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| LMArena Vision | 1304 | 1137 |
| Video-MME | — | 71.9% |
| GeoBench | — | 71% |
| VPCT | — | 40% |
| ScienceQA | — | 88.5% |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- GPT-4o: 43.4 (#181)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| LMArena Non-English | 1462 | 1283 |
| LMArena Chinese | 1529 | 1277 |
| LMArena French | 1495 | 1303 |
| LMArena German | 1457 | 1282 |
| LMArena Japanese | 1420 | 1257 |
| LMArena Korean | 1443 | 1234 |
| LMArena Russian | 1472 | 1286 |
| LMArena Spanish | 1471 | 1292 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- GPT-4o: 66.8 (#202)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| LMArena Instruction Following | 1480 | 1278 |
| LiveBench Instruction Following | — | 68.6% |
| IFEval | — | 81.7% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- GPT-4o: 39.4 (#174)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| LMArena Longer Query | 1479 | 1289 |
| Fiction.LiveBench | — | 66.7% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- GPT-4o: 52.8 (#160)

| Benchmark | GLM-5.3-Flash | GPT-4o |
|---|---|---|
| LMArena Text | 1471 | 1300 |
| LMArena Creative Writing | 1440 | 1291 |
| LMArena Multi-Turn | 1471 | 1302 |
| Short-Story Creative Writing | — | 81.8% |
| WildBench | — | 82.8% |
| LiveBench Language | — | 47.6% |

## FAQ

### Is GLM-5.3-Flash better than GPT-4o?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 28.5 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or GPT-4o?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-4o lists at $2.50 and $10.

### Is GLM-5.3-Flash or GPT-4o better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 25.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 128K.

### How many benchmarks do GLM-5.3-Flash and GPT-4o share?

24 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-4o has 72.