GPT-5.2 vs o1
Updated October 21, 2026. 61 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick o1 for coding: it scores 61.7 on the Vibe Coding Index against 58.9 for GPT-5.2, though on 1 benchmark against 2.
Pick GPT-5.2 when output tokens cost 4.3x less, or when the context window is larger, 400K against 200K tokens.
- Shared results
- 61
- GPT-5.2 only
- 50
- o1 only
- 7
- Like-for-like categories
- 7 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
o1
o1 scores 61.7 against 58.9.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 15.2.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.2
GPT-5.2 leads, 91.7% against 73.2%.
Same board, both measured
Math
AIME
GPT-5.2
GPT-5.2 leads, 96.9% against 71.5%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.2
GPT-5.2 leads, 86.2% against 83.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.2 has a Design Arena: websites result, 1196.
Directional only
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.2
GPT-5.2 $0.0088, o1 $0.045 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 58.9 from 2 benchmarks, o1 61.7 from 1 benchmark.
- Widest category
- Coding, 23.4 points apart on average over 6 shared benchmarks.
- Widest single gap
- ARC-AGI: GPT-5.2 86.2% against o1 30.7%, 55.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against o1 $60.00 per million output tokens, 4.3x apart.
- Context window
- GPT-5.2 takes 400K tokens, o1 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 | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 12 | 0 | ||
| Math | 6 | 0 | ||
| Knowledge | 8 | 2 | ||
| General | 22 | 1 | ||
| All shared boards | 58 | 3 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LMArena WebDev | 1417 | Not measured | Only GPT-5.2 measured |
| AA Coding Index | Not measured | 39.7 | Only o1 measured |
| LiveCodeBench (Vals AI) | 85.4% | 50.3% | GPT-5.2 |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | 53.5% | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 67.9% | GPT-5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 12.9% | GPT-5.2 |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | 46.1% | GPT-5.2 |
| ALE-Bench | 1293.5 | Not measured | Only GPT-5.2 measured |
| LMArena Text: coding | 1442 | 1367 | GPT-5.2 |
| LMArena Text: software and IT | 1434 | 1366 | GPT-5.2 |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 15.2 | GPT-5.2 |
| Epoch Capabilities Index | 153.7 | 141.7 | GPT-5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1094 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1064 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| τ²-Bench Telecom | 84.8% | 62.6% | GPT-5.2 |
| METR time horizons | 75.3% | 51.1% | GPT-5.2 |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 73.2% | GPT-5.2 |
| MMMU Pro (Vals AI) | 86.7% | 77.4% | GPT-5.2 |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 74.7% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 7.0% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | 91.4% | 76.8% | GPT-5.2 |
| ARC-AGI | 86.2% | 30.7% | GPT-5.2 |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | 40.1% | GPT-5.2 |
| Chess puzzles | 48.0% | 10.0% | GPT-5.2 |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | 37.0% | GPT-5.2 |
| LMArena Text: hard prompts | 1428 | 1371 | GPT-5.2 |
| LMArena Text: expert | 1445 | 1361 | GPT-5.2 |
| LMArena Text: hard prompts in English | 1431 | 1372 | GPT-5.2 |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| AIME | 96.9% | 71.5% | GPT-5.2 |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | 94.0% | 89.3% | GPT-5.2 |
| AA Math Index | 99.0 | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | 73.3% | GPT-5.2 |
| FrontierMath, tiers 1 to 3 | 67.4% | 14.7% | GPT-5.2 |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | 1440 | 1388 | GPT-5.2 |
| LMArena Text: mathematical work | 1442 | 1390 | GPT-5.2 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index82
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 83.5% | GPT-5.2 |
| LegalBench (Vals AI) | 82.8% | 80.4% | GPT-5.2 |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | 74.3% | GPT-5.2 |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | 96.5% | o1 |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | 84.1% | GPT-5.2 |
| SimpleQA Verified | 37.1% | 41.1% | o1 |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1425 | 1369 | GPT-5.2 |
| LMArena Text: medicine and healthcare | 1425 | 1355 | GPT-5.2 |
| LMArena Text: legal and government | 1427 | 1368 | GPT-5.2 |
| LMArena Text: business and finance | 1414 | 1328 | GPT-5.2 |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1196 | Not measured | Only GPT-5.2 measured |
| Design Arena: UI components | 1190 | Not measured | Only GPT-5.2 measured |
| Design Arena: game dev | 1196 | Not measured | Only GPT-5.2 measured |
| Design Arena: data viz | 1202 | Not measured | Only GPT-5.2 measured |
| Design Arena: 3D | 1079 | Not measured | Only GPT-5.2 measured |
| Design Arena: all code categories | 1171 | Not measured | Only GPT-5.2 measured |
| Design Arena: SVG | 1137 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1153 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index221
| Benchmark | GPT-5.2 | o1 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | 70.3% | GPT-5.2 |
| Long context reasoning (AA-LCR) | 82.7% | 65.0% | GPT-5.2 |
| DTBench | 90.9% | 74.7% | GPT-5.2 |
| LMCA | 43.9% | 22.3% | GPT-5.2 |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | 1366 | GPT-5.2 |
| LMArena Text: instruction following | 1409 | 1367 | GPT-5.2 |
| LMArena Text: creative writing | 1376 | 1347 | GPT-5.2 |
| LMArena Vision | 1246 | 1168 | GPT-5.2 |
| LMArena Text: English | 1422 | 1372 | GPT-5.2 |
| LMArena Text: non-English | 1405 | 1358 | GPT-5.2 |
| LMArena Text: Chinese | 1447 | 1394 | GPT-5.2 |
| LMArena Text: German | 1444 | 1337 | GPT-5.2 |
| LMArena Text: Japanese | 1420 | 1346 | GPT-5.2 |
| LMArena Text: Korean | 1374 | 1396 | o1 |
| LMArena Text: French | 1454 | 1344 | GPT-5.2 |
| LMArena Text: Spanish | 1413 | 1345 | GPT-5.2 |
| LMArena Text: Russian | 1415 | 1356 | GPT-5.2 |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | 1355 | GPT-5.2 |
| LMArena Text: longer queries | 1413 | 1378 | GPT-5.2 |
| LMArena Text: writing and language | 1392 | 1360 | GPT-5.2 |
| LMArena Text: entertainment and media | 1378 | 1358 | GPT-5.2 |
| LMArena Vision: English | 1243 | 1162 | GPT-5.2 |
| LMArena Vision: Chinese | 1305 | 1235 | GPT-5.2 |
| LMArena Vision: reading text | 1257 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | o1 | 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-5.2 costs less
Repository review
50K input + 3K output tokens
GPT-5.2 costs less
Agent loop
200K input + 10K output tokens
GPT-5.2 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 | OpenAI o1 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | December 10, 2025 | December 17, 2024 |
| Context window | 400K | 200K |
| Input $/Mtok | $1.75 | $15.00 |
| Output $/Mtok | $14.00 | $60.00 |
| Coding index | 58.9 | 61.7 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than o1 for coding?
Pick o1 for coding: it scores 61.7 on the Vibe Coding Index against 58.9 for GPT-5.2, though on 1 benchmark against 2. Pick GPT-5.2 when output tokens cost 4.3x less, or when the context window is larger, 400K against 200K tokens.
Which is cheaper, GPT-5.2 or o1?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or o1?
GPT-5.2, with 400K tokens against 200K.
How many benchmarks is the GPT-5.2 vs o1 comparison based on?
61 benchmarks have a result for both models as of October 21, 2026. GPT-5.2's coding index rests on 2 independent benchmarks and o1's on 1. Where only one model has a result, the page says so instead of naming a winner.