GPT-5.2-Codex vs o3
Updated October 21, 2026. 10 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 55.1 for GPT-5.2-Codex.
Pick GPT-5.2-Codex when a solved task costs $0.43 against $0.571, or when the context window is larger, 400K against 200K tokens.
- Shared results
- 10
- GPT-5.2-Codex only
- 12
- o3 only
- 87
- 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
o3
o3 scores 70.7 against 55.1.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 20.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o3 has a GPQA Diamond result, 84.1%.
Directional only
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
No call
Only o3 has a MMLU Pro result, 85.6%.
Directional only
Web design
Design Arena: websites
No call
Only o3 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
o3
GPT-5.2-Codex $0.0088, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.2-Codex
GPT-5.2-Codex $0.43, o3 $0.571.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.2-Codex 55.1 from 2 benchmarks, o3 70.7 from 2 benchmarks.
- Widest category
- Agentic work, 11.4 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): GPT-5.2-Codex 35.7% against o3 20.1%, 15.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against o3 $8.00 per million output tokens, 1.8x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, o3 200K.
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-5.2-Codex | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | 1 tie | |
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 9 | 0 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index30
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | 72.8% | 58.4% | GPT-5.2-Codex |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 88.0% | 83.9% | GPT-5.2-Codex |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.8% | Only o3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 37.1% | Tie |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | 1299.9 | 933.5 | GPT-5.2-Codex |
| LMArena Text: coding | Not measured | 1407 | Only o3 measured |
| LMArena Text: software and IT | Not measured | 1415 | Only o3 measured |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 20.2 | GPT-5.2-Codex |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | 80.7% | GPT-5.2-Codex |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.1% | Only o3 measured |
| MMMU Pro (Vals AI) | Not measured | 80.4% | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 89.9% | 82.7% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 20.1% | GPT-5.2-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 81.8% | Only o3 measured |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | Not measured | 1402 | Only o3 measured |
| LMArena Text: expert | Not measured | 1402 | Only o3 measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only o3 measured |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| AIME | Not measured | 85.3% | Only o3 measured |
| MATH 500 (Vals AI) | Not measured | 94.6% | Only o3 measured |
| MGSM (Vals AI) | Not measured | 91.8% | Only o3 measured |
| AA Math Index | Not measured | 88.3 | Only o3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 88.3% | Only o3 measured |
| OTIS Mock AIME | Not measured | 84.4% | Only o3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | Not measured | 1426 | Only o3 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 85.6% | Only o3 measured |
| LegalBench (Vals AI) | Not measured | 83.8% | Only o3 measured |
| CorpFin (Vals AI) | Not measured | 59.7% | Only o3 measured |
| TaxEval (Vals AI) | Not measured | 74.6% | Only o3 measured |
| MortgageTax (Vals AI) | Not measured | 65.7% | Only o3 measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only o3 measured |
| MedCode (Vals AI) | Not measured | 47.3% | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.3% | Only o3 measured |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | Not measured | 1429 | Only o3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1448 | Only o3 measured |
| LMArena Text: legal and government | Not measured | 1428 | Only o3 measured |
| LMArena Text: business and finance | Not measured | 1393 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1036 | Only o3 measured |
| Design Arena: UI components | Not measured | 1016 | Only o3 measured |
| Design Arena: game dev | Not measured | 1034 | Only o3 measured |
| Design Arena: data viz | Not measured | 1200 | Only o3 measured |
| Design Arena: all code categories | Not measured | 1021 | Only o3 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | o3 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.8% | Only o3 measured |
| MedScribe (Vals AI) | Not measured | 76.7% | Only o3 measured |
| IFBench | 77.6% | 71.4% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 82.3% | 74.7% | GPT-5.2-Codex |
| DTBench | Not measured | 84.8% | Only o3 measured |
| LMCA | Not measured | 39.7% | Only o3 measured |
| Fiction.LiveBench | Not measured | 88.9% | Only o3 measured |
| Creative writing (Lech Mazur) | Not measured | 83.9% | Only o3 measured |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | Not measured | 1410 | Only o3 measured |
| LMArena Text: instruction following | Not measured | 1368 | Only o3 measured |
| LMArena Text: creative writing | Not measured | 1359 | Only o3 measured |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | Not measured | 1417 | Only o3 measured |
| LMArena Text: non-English | Not measured | 1401 | Only o3 measured |
| LMArena Text: Chinese | Not measured | 1437 | Only o3 measured |
| LMArena Text: German | Not measured | 1420 | Only o3 measured |
| LMArena Text: Japanese | Not measured | 1403 | Only o3 measured |
| LMArena Text: Korean | Not measured | 1370 | Only o3 measured |
| LMArena Text: French | Not measured | 1430 | Only o3 measured |
| LMArena Text: Spanish | Not measured | 1395 | Only o3 measured |
| LMArena Text: Russian | Not measured | 1406 | Only o3 measured |
| LMArena Text: Polish | Not measured | 1438 | Only o3 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only o3 measured |
| LMArena Text: longer queries | Not measured | 1372 | Only o3 measured |
| LMArena Text: writing and language | Not measured | 1372 | Only o3 measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only o3 measured |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | o3 | 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
o3 costs less
Repository review
50K input + 3K output tokens
o3 costs less
Agent loop
200K input + 10K output tokens
o3 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-5.2-Codex | OpenAI o3 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | January 14, 2026 | April 16, 2025 |
| Context window | 400K | 200K |
| Input $/Mtok | $1.75 | $2.00 |
| Output $/Mtok | $14.00 | $8.00 |
| Coding index | 55.1 | 70.7 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.43 | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than o3 for coding?
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 55.1 for GPT-5.2-Codex. Pick GPT-5.2-Codex when a solved task costs $0.43 against $0.571, or when the context window is larger, 400K against 200K tokens.
Which is cheaper, GPT-5.2-Codex or o3?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, GPT-5.2-Codex costs $0.43 and o3 $0.571.
Which has the bigger context window, GPT-5.2-Codex or o3?
GPT-5.2-Codex, with 400K tokens against 200K.
How many benchmarks is the GPT-5.2-Codex vs o3 comparison based on?
10 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex's coding index rests on 2 independent benchmarks and o3's on 2. Where only one model has a result, the page says so instead of naming a winner.