# GLM-5.3-Flash vs GPT-4.1 mini

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 33.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-4-1-mini
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-4.1 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 23.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 44.7% for GPT-4.1 mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 1M.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 33.5 |
| Rank | 45 | 232 |
| Context | 1M | 1.05M |
| Input $/M | $0.15 | $0.40 |
| Output $/M | $0.50 | $1.60 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- GPT-4.1 mini: 30.4 (#286)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| SciCode | 51.6% | 40.4% |
| LMArena Coding | 1506 | 1367 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 23.9% |
| Aider Polyglot | — | 32.4% |
| LMArena WebDev | 1614 | — |
| WeirdML | — | 37.6% |
| BigCodeBench Instruct | — | 48.9% |
| CadEval | — | 16% |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- GPT-4.1 mini: 33.0 (#52)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 50.5% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- GPT-4.1 mini: 10.9 (#335)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| CritPt | 15.4% | 0% |
| Chess Puzzles | 14% | 7% |
| LMArena Hard Prompts | 1490 | 1349 |
| Mystery Game Puzzles | 8% | 7% |
| Epoch Capabilities Index | 151.9 | 135.01 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 48.6% |
| ARC-AGI-1 | — | 3.5% |
| DTBench | — | 68.8% |
| LMCA | — | 21.1% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- GPT-4.1 mini: 23.1 (#264)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 6.7% |
| OTIS Mock AIME 2024-2025 | 93.9% | 44.7% |
| LMArena Math | 1499 | 1343 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 49.1% |
| MATH Level 5 | — | 87.3% |
| FrontierMath (Feb 2025 set) | — | 4.5% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- GPT-4.1 mini: 34.7 (#183)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| GPQA Diamond | 90.2% | 65.8% |
| LMArena Expert | 1512 | 1338 |
| SimpleQA Verified | — | 12.7% |
| MMLU-Pro | — | 78.3% |
| GPQA (HELM) | — | 61.4% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- GPT-4.1 mini: 35.2 (#77)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Vision | 1304 | 1181 |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- GPT-4.1 mini: 45.9 (#161)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Non-English | 1462 | 1318 |
| LMArena Chinese | 1529 | 1329 |
| LMArena French | 1495 | 1357 |
| LMArena German | 1457 | 1349 |
| LMArena Japanese | 1420 | 1291 |
| LMArena Korean | 1443 | 1298 |
| LMArena Russian | 1472 | 1324 |
| LMArena Spanish | 1471 | 1319 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- GPT-4.1 mini: 73.9 (#113)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Instruction Following | 1480 | 1332 |
| IFEval | — | 90.4% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- GPT-4.1 mini: 31.8 (#270)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Longer Query | 1479 | 1344 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- GPT-4.1 mini: 48.8 (#193)

| Benchmark | GLM-5.3-Flash | GPT-4.1 mini |
|---|---|---|
| LMArena Text | 1471 | 1341 |
| LMArena Creative Writing | 1440 | 1300 |
| LMArena Multi-Turn | 1471 | 1354 |
| EQ-Bench Creative Writing | — | 1147 |
| WildBench | — | 83.8% |

## FAQ

### Is GLM-5.3-Flash better than GPT-4.1 mini?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 33.5 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or GPT-4.1 mini?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-4.1 mini lists at $0.40 and $1.60.

### Is GLM-5.3-Flash or GPT-4.1 mini better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 30.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-4.1 mini does, with 1.05M tokens against 1M.

### How many benchmarks do GLM-5.3-Flash and GPT-4.1 mini share?

26 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-4.1 mini has 47.