# GLM-5.3-Flash vs GPT-4o mini

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 25.4 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-4o-mini
- 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 mini 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 9.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 6.9% for GPT-4o mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.15 / $0.60 for GPT-4o mini.
- 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 mini |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 25.4 |
| Rank | 45 | 337 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.15 |
| Output $/M | $0.50 | $0.60 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- GPT-4o mini: 21.6 (#330)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Coding | 1506 | 1290 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 3.6% |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| WeirdML | — | 11.8% |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | — | 43.1% |
| BigCodeBench Complete | — | 57.4% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 83.5% |
| MBPP+ | — | 72.2% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- GPT-4o mini: 27.2 (#95)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| BALROG | — | 17.4% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- GPT-4o mini: 8.7 (#343)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1490 | 1267 |
| Mystery Game Puzzles | 8% | 12% |
| Epoch Capabilities Index | 151.9 | 126.56 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 10.7% |
| Kagi LLM Benchmark | — | 28.8% |
| CritPt | 15.4% | — |
| LiveBench Reasoning | — | 32.8% |
| DTBench | — | 54.4% |
| LiveBench Data Analysis | — | 50% |
| LMCA | — | 10.4% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| LiveBench | — | 41.3% |
| PIQA | — | 88.7% |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- GPT-4o mini: 9.9 (#307)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 0.7% |
| OTIS Mock AIME 2024-2025 | 93.9% | 6.9% |
| LMArena Math | 1499 | 1267 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 28% |
| LiveBench Math | — | 36.3% |
| MATH Level 5 | — | 52.6% |
| GSM8K | — | 91.3% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- GPT-4o mini: 17.7 (#279)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| GPQA Diamond | 90.2% | 37.7% |
| LMArena Expert | 1512 | 1235 |
| SimpleQA Verified | — | 8.3% |
| MMLU-Pro | — | 60.3% |
| Confabulations | — | 37.2% |
| GPQA (HELM) | — | 36.8% |
| BoolQ | — | 88.7% |
| MMLU | — | 81.8% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- GPT-4o mini: 25.3 (#117)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Vision | 1304 | 1066 |
| Video-MME | — | 64.8% |
| GeoBench | — | 64% |
| VPCT | — | 34% |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- GPT-4o mini: 42.2 (#194)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Non-English | 1462 | 1266 |
| LMArena Chinese | 1529 | 1264 |
| LMArena French | 1495 | 1296 |
| LMArena German | 1457 | 1272 |
| LMArena Japanese | 1420 | 1216 |
| LMArena Korean | 1443 | 1195 |
| LMArena Russian | 1472 | 1275 |
| LMArena Spanish | 1471 | 1276 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- GPT-4o mini: 62.1 (#234)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Instruction Following | 1480 | 1259 |
| LiveBench Instruction Following | — | 56.8% |
| IFEval | — | 78.2% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- GPT-4o mini: 39.1 (#181)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Longer Query | 1479 | 1289 |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- GPT-4o mini: 39.7 (#243)

| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Text | 1471 | 1286 |
| LMArena Creative Writing | 1440 | 1268 |
| LMArena Multi-Turn | 1471 | 1285 |
| Short-Story Creative Writing | — | 67.2% |
| EQ-Bench Creative Writing | — | 873 |
| WildBench | — | 79.1% |
| LiveBench Language | — | 28.6% |

## FAQ

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

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 25.4 on the Noometry Index.

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

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-4o mini lists at $0.15 and $0.60.

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

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 21.6 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 mini share?

24 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-4o mini has 60.