# GPT-4 vs GPT-5.3 Codex

> GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 28.9 on the Noometry Index.

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

## Summary

- They share 3 benchmarks with published results for both. GPT-4 scores higher in 0 categories and GPT-5.3 Codex in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where GPT-5.3 Codex leads 48.5 to 31.2.
- The biggest single-benchmark swing is WeirdML: 12.4% for GPT-4 and 79.3% for GPT-5.3 Codex.
- GPT-5.3 Codex is cheaper at $1.75 / $14 per million input/output tokens, against $30 / $60 for GPT-4.
- GPT-5.3 Codex accepts more context: 400K tokens versus 8K.

## Snapshot

| | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 28.9 | 45.5 |
| Rank | 308 | 61 |
| Context | 8K | 400K |
| Input $/M | $30 | $1.75 |
| Output $/M | $60 | $14 |
| Weights | Proprietary | Proprietary |

## Coding

- GPT-4: 31.2 (#277)
- GPT-5.3 Codex: 48.5 (#51)

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| WeirdML | 12.4% | 79.3% |
| SWE-bench Verified | — | 74.8% |
| LMArena WebDev | — | 1408 |
| BigCodeBench Instruct | 46% | — |
| LMArena Coding | 1254 | — |
| BigCodeBench Complete | 57.2% | — |
| ALE-Bench | — | 1,655 |
| HumanEval+ | 79.3% | — |

## Agentic & Tool Use

- GPT-4: —
- GPT-5.3 Codex: 47.8 (#9)

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| METR Time Horizons | 36.1% | 74.5% |
| Terminal-Bench | — | 78.4% |
| Vending-Bench 2 | — | 5,940 |

## Reasoning

- GPT-4: 17.3 (#285)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 125.88 | 156.84 |
| Chess Puzzles | 4% | — |
| LMArena Hard Prompts | 1241 | — |
| Mystery Game Puzzles | 12% | — |
| DTBench | 62.7% | — |
| LMCA | 17.1% | — |
| BIG-Bench Hard | 75.1% | — |
| ForecastBench | 57.8 | — |
| HellaSwag | 95.3% | — |
| WinoGrande | 87.5% | — |

## Math

- GPT-4: 10.1 (#304)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.1% | — |
| LMArena Math | 1269 | — |
| MATH Level 5 | 23% | — |
| GSM8K | 92% | — |

## Knowledge

- GPT-4: 18.4 (#277)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 35.7% | — |
| LMArena Expert | 1211 | — |
| MMLU | 86.4% | — |
| TriviaQA | 84.8% | — |

## Multilingual

- GPT-4: 40.8 (#210)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1246 | — |
| LMArena Chinese | 1242 | — |
| LMArena French | 1282 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1209 | — |
| LMArena Korean | 1184 | — |
| LMArena Russian | 1251 | — |
| LMArena Spanish | 1261 | — |

## Instruction Following

- GPT-4: 65.5 (#217)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| LMArena Instruction Following | 1241 | — |

## Long Context

- GPT-4: 37.7 (#207)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1244 | — |

## Writing & Preference

- GPT-4: 35.0 (#263)
- GPT-5.3 Codex: —

| Benchmark | GPT-4 | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1263 | — |
| LMArena Creative Writing | 1244 | — |
| EQ-Bench Creative Writing | 752 | — |
| LMArena Multi-Turn | 1257 | — |

## FAQ

### Is GPT-4 better than GPT-5.3 Codex?

GPT-5.3 Codex is the stronger model overall, scoring 45.5 to 28.9 on the Noometry Index.

### Which is cheaper, GPT-4 or GPT-5.3 Codex?

GPT-5.3 Codex is cheaper. It lists at $1.75 per million input tokens and $14 per million output tokens; GPT-4 lists at $30 and $60.

### Is GPT-4 or GPT-5.3 Codex better for coding?

GPT-5.3 Codex scores higher on coding benchmarks: 48.5 versus 31.2 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.3 Codex does, with 400K tokens against 8K.

### How many benchmarks do GPT-4 and GPT-5.3 Codex share?

3 benchmarks have published results for both models. GPT-4 has 38 scored results on Noometry and GPT-5.3 Codex has 8.