# GLM-5.3-Flash vs GPT-5.3 Codex

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 45.5 on the Noometry Index.

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

## Summary

- They share 3 benchmarks with published results for both. GLM-5.3-Flash scores higher in 1 category and GPT-5.3 Codex in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 47.8 to 34.3.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.75 / $14 for GPT-5.3 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-5.3 Codex |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 45.5 |
| Rank | 45 | 61 |
| Context | 1M | 400K |
| Input $/M | $0.15 | $1.75 |
| Output $/M | $0.50 | $14 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- GPT-5.3 Codex: 48.5 (#51)

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| LMArena WebDev | 1614 | 1408 |
| ALE-Bench | 303.55 | 1,655 |
| SWE-bench Verified | — | 74.8% |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 79.3% |
| LMArena Coding | 1506 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- GPT-5.3 Codex: 47.8 (#9)

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | — | 78.4% |
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
| METR Time Horizons | — | 74.5% |
| Vending-Bench 2 | — | 5,940 |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 151.9 | 156.84 |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| LMArena Hard Prompts | 1490 | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| LMArena Math | 1499 | — |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 90.2% | — |
| LMArena Expert | 1512 | — |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1529 | — |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Russian | 1472 | — |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| LMArena Instruction Following | 1480 | — |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1479 | — |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- GPT-5.3 Codex: —

| Benchmark | GLM-5.3-Flash | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1440 | — |
| LMArena Multi-Turn | 1471 | — |

## FAQ

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

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 45.5 on the Noometry Index.

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

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

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

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 48.5 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-5.3 Codex share?

3 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-5.3 Codex has 8.