# GLM-5.3-Flash vs GPT-5.2 Codex

> GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-5-2-codex
- Last updated: 2026-10-11
- Shared benchmarks: 2

## Summary

- They share 2 benchmarks with published results for both. GLM-5.3-Flash scores higher in 1 category and GPT-5.2 Codex in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3-Flash leads 53.1 to 45.5.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- 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-5.2 Codex |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 42.6 |
| Rank | 41 | 111 |
| Context | 1M | 400K |
| Input $/M | $0.15 | $1.75 |
| Output $/M | $0.50 | $14 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.1 (#31)
- GPT-5.2 Codex: 45.5 (#71)

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena WebDev | 1609 | 1339 |
| ALE-Bench | 303.55 | 1,300 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| CursorBench | 36.8% | — |
| SWE-bench Multilingual | — | 66.3% |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| LMArena Coding | 1508 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.2 (#47)
- GPT-5.2 Codex: 41.0 (#22)

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| Terminal-Bench | — | 66.5% |
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 48.0 (#42)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| LMArena Hard Prompts | 1491 | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.88 | — |

## Math

- GLM-5.3-Flash: 53.3 (#47)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| LMArena Math | 1500 | — |

## Knowledge

- GLM-5.3-Flash: 58.4 (#36)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| GPQA Diamond | 90.2% | — |
| LMArena Expert | 1513 | — |

## Multimodal

- GLM-5.3-Flash: 42.8 (#27)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Vision | 1296 | — |

## Multilingual

- GLM-5.3-Flash: 56.0 (#25)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1527 | — |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Russian | 1469 | — |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.5 (#20)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Instruction Following | 1478 | — |

## Long Context

- GLM-5.3-Flash: 45.4 (#39)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Longer Query | 1482 | — |

## Writing & Preference

- GLM-5.3-Flash: 65.3 (#50)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.2 Codex |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1442 | — |
| LMArena Multi-Turn | 1467 | — |

## FAQ

### Is GLM-5.3-Flash better than GPT-5.2 Codex?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 42.6 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or GPT-5.2 Codex?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

### Is GLM-5.3-Flash or GPT-5.2 Codex better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 45.5 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-5.2 Codex share?

2 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-5.2 Codex has 5.