GPT-4o vs GPT-5.3-Codex
Updated October 21, 2026. 12 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-4o for coding: it scores 38.8 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex.
Pick GPT-5.3-Codex when the context window is larger, 400K against 128K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
- Shared results
- 12
- GPT-4o only
- 14
- GPT-5.3-Codex only
- 22
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
GPT-4o
GPT-4o scores 38.8 against 29.5.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only GPT-5.3-Codex has a AA Intelligence Index result, 32.5.
Directional only
Web design
Design Arena: websites
GPT-5.3-Codex
GPT-5.3-Codex leads, 1163 against 831.
Same board, both measured
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o
GPT-4o $0.0075, GPT-5.3-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-4o 38.8 from 1 benchmark, GPT-5.3-Codex 29.5 from 1 benchmark.
- Widest category
- Agentic work, 60.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-4o 25.1% against GPT-5.3-Codex 86.0%, 60.8 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against GPT-5.3-Codex $14.00 per million output tokens, 1.4x apart.
- Context window
- GPT-4o takes 128K tokens, GPT-5.3-Codex 400K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-4o | Bars | GPT-5.3-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 6 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 12 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| LMArena WebDev | Not measured | 1407 | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 87.3% | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 78.0% | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 61.8% | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | Not measured | 53.8% | Only GPT-5.3-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | 53.0% | GPT-5.3-Codex |
| Terminal-Bench | Not measured | 78.4% | Only GPT-5.3-Codex measured |
| WeirdML | Not measured | 79.3% | Only GPT-5.3-Codex measured |
| ALE-Bench | Not measured | 1655.2 | Only GPT-5.3-Codex measured |
| LMArena WebDev: React | Not measured | 1395 | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | Not measured | 1422 | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | Not measured | 1443 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 32.5 | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | Not measured | 157.1 | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1033 | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 983 | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1054 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1084 | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1124 | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 25.1% | 86.0% | GPT-5.3-Codex |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| Vending-Bench 2 | Not measured | $5,940 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | 91.5% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | 42.5% | GPT-5.3-Codex |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | 1163 | GPT-5.3-Codex |
| Design Arena: UI components | 885 | 1137 | GPT-5.3-Codex |
| Design Arena: game dev | 906 | 1162 | GPT-5.3-Codex |
| Design Arena: data viz | 858 | 1164 | GPT-5.3-Codex |
| Design Arena: 3D | 869 | 1008 | GPT-5.3-Codex |
| Design Arena: all code categories | 861 | 1145 | GPT-5.3-Codex |
| Design Arena: SVG | Not measured | 1129 | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | Not measured | 1151 | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| IFBench | 34.3% | 75.4% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 49.3% | 83.3% | GPT-5.3-Codex |
| DTBench | 64.5% | Not measured | Only GPT-4o measured |
| LMCA | 16.6% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-4o costs less
Repository review
50K input + 3K output tokens
GPT-5.3-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.3-Codex costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-4o | OpenAI GPT-5.3-Codex |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 13, 2024 | February 24, 2026 |
| Context window | 128K | 400K |
| Input $/Mtok | $2.50 | $1.75 |
| Output $/Mtok | $10.00 | $14.00 |
| Coding index | 38.8 | 29.5 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o better than GPT-5.3-Codex for coding?
Pick GPT-4o for coding: it scores 38.8 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex. Pick GPT-5.3-Codex when the context window is larger, 400K against 128K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
Which is cheaper, GPT-4o or GPT-5.3-Codex?
GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-4o or GPT-5.3-Codex?
GPT-5.3-Codex, with 400K tokens against 128K.
How many benchmarks is the GPT-4o vs GPT-5.3-Codex comparison based on?
12 benchmarks have a result for both models as of October 21, 2026. GPT-4o's coding index rests on 1 independent benchmark and GPT-5.3-Codex's on 1. Where only one model has a result, the page says so instead of naming a winner.