# GLM-5.3 vs GPT-5.2 Codex

> GLM-5.3 is the stronger model overall, scoring 54.8 to 42.6 on the Noometry Index.

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

## Summary

- They share 2 benchmarks with published results for both. GLM-5.3 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 leads 59.5 to 45.5.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GLM-5.3 accepts more context: 1M tokens versus 400K.
- GLM-5.3 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 42.6 |
| Rank | 26 | 111 |
| Context | 1M | 400K |
| Input $/M | $1.40 | $1.75 |
| Output $/M | $4.40 | $14 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3: 59.5 (#14)
- GPT-5.2 Codex: 45.5 (#71)

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| LMArena WebDev | 1622 | 1339 |
| ALE-Bench | 1,317 | 1,300 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| CursorBench | 42.6% | — |
| SWE-bench Multilingual | — | 66.3% |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| LMArena Coding | 1496 | — |

## Agentic & Tool Use

- GLM-5.3: 36.4 (#38)
- GPT-5.2 Codex: 41.0 (#22)

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| Terminal-Bench | — | 66.5% |
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |

## Reasoning

- GLM-5.3: 46.1 (#46)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| LMArena Hard Prompts | 1489 | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |

## Math

- GLM-5.3: 62.3 (#33)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
| LMArena Math | 1489 | — |

## Knowledge

- GLM-5.3: 58.3 (#37)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| LMArena Expert | 1516 | — |

## Multilingual

- GLM-5.3: 55.7 (#28)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| LMArena Non-English | 1457 | — |
| LMArena Chinese | 1528 | — |
| LMArena French | 1499 | — |
| LMArena German | 1499 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1472 | — |
| LMArena Russian | 1463 | — |
| LMArena Spanish | 1460 | — |

## Instruction Following

- GLM-5.3: 77.5 (#23)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| LMArena Instruction Following | 1477 | — |

## Long Context

- GLM-5.3: 45.4 (#41)
- GPT-5.2 Codex: —

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

## Writing & Preference

- GLM-5.3: 75.7 (#6)
- GPT-5.2 Codex: —

| Benchmark | GLM-5.3 | GPT-5.2 Codex |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1457 | — |
| EQ-Bench Creative Writing | 2075 | — |
| LMArena Multi-Turn | 1472 | — |

## FAQ

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

GLM-5.3 is the stronger model overall, scoring 54.8 to 42.6 on the Noometry Index.

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

GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

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

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 45.5 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do GLM-5.3 and GPT-5.2 Codex share?

2 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-5.2 Codex has 5.