# GLM-5.3-Flash vs GPT-5.4 mini

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 44.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-5-4-mini
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-5.4 mini in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3-Flash leads 53.7 to 45.0.
- The biggest single-benchmark swing is Chess Puzzles: 14% for GLM-5.3-Flash and 24% for GPT-5.4 mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.75 / $4.50 for GPT-5.4 mini.
- GLM-5.3-Flash accepts more context: 1M tokens versus 400K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 44.8 |
| Rank | 45 | 69 |
| Context | 1M | 400K |
| Input $/M | $0.15 | $0.75 |
| Output $/M | $0.50 | $4.50 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- GPT-5.4 mini: 45.0 (#67)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| FrontierCode | 31.8% | 27% |
| LMArena WebDev | 1614 | 1397 |
| SciCode | 51.6% | 49.9% |
| LMArena Coding | 1506 | 1438 |
| ALE-Bench | 303.55 | 1,189 |
| DeepSWE | 63.4% | — |
| WeirdML | — | 60.3% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- GPT-5.4 mini: 29.6 (#74)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| DeepResearch Bench | — | 36.3% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- GPT-5.4 mini: 30.3 (#77)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| CritPt | 15.4% | 10% |
| Chess Puzzles | 14% | 24% |
| LMArena Hard Prompts | 1490 | 1424 |
| Mystery Game Puzzles | 8% | 11% |
| Epoch Capabilities Index | 151.9 | 148.83 |
| ARC-AGI-2 | — | 18.9% |
| Kagi LLM Benchmark | — | 37.9% |
| NYT Connections (extended) | — | 61.8% |
| ARC-AGI-1 | — | 63.7% |
| Thematic Generalization | — | 61.7% |
| DTBench | — | 80% |
| LMCA | — | 40.8% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 57 |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- GPT-5.4 mini: 45.4 (#63)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 51.2% |
| FrontierMath Tier 4 | 17.1% | 9.8% |
| OTIS Mock AIME 2024-2025 | 93.9% | 88.9% |
| ProofBench | 21% | 21% |
| LMArena Math | 1499 | 1418 |
| FrontierMath (Feb 2025 set) | — | 28.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- GPT-5.4 mini: 51.4 (#62)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| GPQA Diamond | 90.2% | 86.9% |
| LMArena Expert | 1512 | 1434 |
| SimpleQA Verified | — | 29.4% |
| Vectara Hallucination Rate | — | 5.5% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- GPT-5.4 mini: 39.0 (#51)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Vision | 1304 | 1245 |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- GPT-5.4 mini: 52.1 (#94)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Non-English | 1462 | 1405 |
| LMArena Chinese | 1529 | 1446 |
| LMArena French | 1495 | 1440 |
| LMArena German | 1457 | 1407 |
| LMArena Japanese | 1420 | 1375 |
| LMArena Korean | 1443 | 1369 |
| LMArena Russian | 1472 | 1417 |
| LMArena Spanish | 1471 | 1403 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- GPT-5.4 mini: 74.3 (#99)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Instruction Following | 1480 | 1405 |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- GPT-5.4 mini: 42.9 (#107)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Longer Query | 1479 | 1407 |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- GPT-5.4 mini: 64.2 (#56)

| Benchmark | GLM-5.3-Flash | GPT-5.4 mini |
|---|---|---|
| LMArena Text | 1471 | 1411 |
| LMArena Creative Writing | 1440 | 1370 |
| LMArena Multi-Turn | 1471 | 1429 |
| EQ-Bench Creative Writing | — | 1665 |

## FAQ

### Is GLM-5.3-Flash better than GPT-5.4 mini?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 44.8 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or GPT-5.4 mini?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-5.4 mini lists at $0.75 and $4.50.

### Is GLM-5.3-Flash or GPT-5.4 mini better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 45.0 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 400K.

### How many benchmarks do GLM-5.3-Flash and GPT-5.4 mini share?

31 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-5.4 mini has 46.