GPT-4o (2024-11-20) vs GPT-5.2-Codex
Updated October 21, 2026. 2 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-5.2-Codex for coding: it scores 55.1 on the Vibe Coding Index against 18.2 for GPT-4o (2024-11-20).
Pick GPT-4o (2024-11-20) when output tokens cost 1.4x less.
- Shared results
- 2
- GPT-4o (2024-11-20) only
- 22
- GPT-5.2-Codex only
- 20
- Like-for-like categories
- 2 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-5.2-Codex
GPT-5.2-Codex scores 55.1 against 18.2.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 8.4.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-11-20) has a GPQA Diamond result, 53.8%.
Directional only
Math
AIME
No call
Only GPT-4o (2024-11-20) has a AIME result, 11.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-11-20) has a MMLU Pro result, 72.6%.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o (2024-11-20)
GPT-4o (2024-11-20) $0.0075, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-4o (2024-11-20) 18.2 from 1 benchmark, GPT-5.2-Codex 55.1 from 2 benchmarks.
- Widest category
- Coding, 44.5 points apart on average over 1 shared benchmark.
- Widest single gap
- LiveCodeBench (Vals AI): GPT-4o (2024-11-20) 43.4% against GPT-5.2-Codex 88.0%, 44.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-11-20) $10.00 against GPT-5.2-Codex $14.00 per million output tokens, 1.4x apart.
- Context window
- GPT-4o (2024-11-20) takes 128K tokens, GPT-5.2-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 (2024-11-20) | Bars | GPT-5.2-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 2 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Aider polyglot | 18.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | Not measured | 66.3% | Only GPT-5.2-Codex measured |
| LMArena WebDev | Not measured | 1339 | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 43.4% | 88.0% | GPT-5.2-Codex |
| SWE-bench Verified (Vals AI) | Not measured | 72.4% | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 37.9% | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 37.1% | Only GPT-5.2-Codex measured |
| Terminal-Bench | Not measured | 66.5% | Only GPT-5.2-Codex measured |
| GSO-Bench | 0.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| WeirdML | 25.1% | Not measured | Only GPT-4o (2024-11-20) measured |
| Cybench | 12.5% | Not measured | Only GPT-4o (2024-11-20) measured |
| ALE-Bench | Not measured | 1299.9 | Only GPT-5.2-Codex measured |
| LMArena WebDev: React | Not measured | 1329 | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | Not measured | 1348 | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 8.4 | 28.5 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1051 | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 993 | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1090 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1176 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | Not measured | 92.1% | Only GPT-5.2-Codex measured |
| TheAgentCompany | 8.6% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 53.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| MMMU Pro (Vals AI) | 62.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.9% | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.7% | Only GPT-5.2-Codex measured |
| GPQA Diamond (Epoch AI run) | 47.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| VPCT | 40.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AIME | 11.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| MATH 500 (Vals AI) | 74.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| MGSM (Vals AI) | 90.4% | Not measured | Only GPT-4o (2024-11-20) measured |
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-11-20) measured |
| MATH level 5 | 49.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 72.6% | Not measured | Only GPT-4o (2024-11-20) measured |
| LegalBench (Vals AI) | 82.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| CaseLaw (Vals AI) | 59.7% | Not measured | Only GPT-4o (2024-11-20) measured |
| CorpFin (Vals AI) | 45.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| TaxEval (Vals AI) | 74.5% | Not measured | Only GPT-4o (2024-11-20) measured |
| MortgageTax (Vals AI) | 57.4% | Not measured | Only GPT-4o (2024-11-20) measured |
| GeoBench | 71.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 77.6% | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 82.3% | Only GPT-5.2-Codex measured |
| Creative writing (Lech Mazur) | 81.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | GPT-5.2-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 (2024-11-20) costs less
Repository review
50K input + 3K output tokens
GPT-5.2-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.2-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 (2024-11-20) | OpenAI GPT-5.2-Codex |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | November 20, 2024 | January 14, 2026 |
| Context window | 128K | 400K |
| Input $/Mtok | $2.50 | $1.75 |
| Output $/Mtok | $10.00 | $14.00 |
| Coding index | 18.2 | 55.1 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o (2024-11-20) better than GPT-5.2-Codex for coding?
Pick GPT-5.2-Codex for coding: it scores 55.1 on the Vibe Coding Index against 18.2 for GPT-4o (2024-11-20). Pick GPT-4o (2024-11-20) when output tokens cost 1.4x less.
Which is cheaper, GPT-4o (2024-11-20) or GPT-5.2-Codex?
GPT-4o (2024-11-20) costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-4o (2024-11-20) or GPT-5.2-Codex?
GPT-5.2-Codex, with 400K tokens against 128K.
How many benchmarks is the GPT-4o (2024-11-20) vs GPT-5.2-Codex comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. GPT-4o (2024-11-20)'s coding index rests on 1 independent benchmark and GPT-5.2-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.