GPT-5.5 vs o3
Updated October 21, 2026. 76 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 67.8 for GPT-5.5.
Pick GPT-5.5 when the context window is larger, 1.05M against 200K tokens, or when what matters is LiveCodeBench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 76
- GPT-5.5 only
- 56
- o3 only
- 21
- Like-for-like categories
- 8 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 67.8.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 20.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
GPT-5.5
GPT-5.5 leads, 93.2% against 84.1%.
Same board, both measured
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 85.6%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 against 1036.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
o3
GPT-5.5 $0.020, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
o3
GPT-5.5 $1.65, o3 $0.571.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.5 67.8 from 2 benchmarks, o3 70.7 from 2 benchmarks.
- Widest category
- Web design, 25.7 points apart on average over 5 shared benchmarks.
- Widest single gap
- ARC-AGI-2: GPT-5.5 85.0% against o3 6.5%, 78.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.5 $30.00 against o3 $8.00 per million output tokens, 3.8x apart.
- Context window
- GPT-5.5 takes 1.05M 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-5.5 | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 15 | 0 | ||
| Math | 3 | 0 | 1 tie | |
| Knowledge | 11 | 0 | ||
| Web design | 5 | 0 | ||
| General | 29 | 1 | ||
| All shared boards | 74 | 1 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | Not measured | 58.4% | Only o3 measured |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| LiveBench coding | 85.1% | Not measured | Only GPT-5.5 measured |
| LMArena WebDev | 1513 | Not measured | Only GPT-5.5 measured |
| AA Coding Index | 74.9 | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | 85.3% | 83.9% | GPT-5.5 |
| SWE-bench Verified (Vals AI) | 82.6% | Not measured | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | 69.8% | Not measured | Only GPT-5.5 measured |
| Code migration (Vals AI) | 45.2% | Not measured | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 76.4% | Not measured | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | 62.5% | Not measured | Only GPT-5.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 80.8% | Only o3 measured |
| SciCode | 56.1% | Not measured | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 60.6% | 37.1% | GPT-5.5 |
| Terminal-Bench | 84.7% | Not measured | Only GPT-5.5 measured |
| FrontierCode | 43.0% | Not measured | Only GPT-5.5 measured |
| GSO-Bench | 40.2% | 8.8% | GPT-5.5 |
| WeirdML | 84.9% | 52.4% | GPT-5.5 |
| MirrorCode | 10.0% | Not measured | Only GPT-5.5 measured |
| ALE-Bench | 1943.0 | 933.5 | GPT-5.5 |
| LMArena Text: coding | 1494 | 1407 | GPT-5.5 |
| LMArena Text: software and IT | 1493 | 1415 | GPT-5.5 |
| LMArena WebDev: React | 1506 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: HTML | 1543 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: from an image | 1532 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 20.2 | GPT-5.5 |
| Epoch Capabilities Index | 159.7 | 146.7 | GPT-5.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| AA Agentic Index | 36.4 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building web apps | 1117 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | 1089 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | 1143 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | 1169 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | 1173 | Not measured | Only GPT-5.5 measured |
| Design Arena: agents building games | 1179 | Not measured | Only GPT-5.5 measured |
| Design Arena: HTML slides | 1067 | Not measured | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | 1108 | Not measured | Only GPT-5.5 measured |
| LiveBench research | 61.6% | Not measured | Only GPT-5.5 measured |
| LiveBench browser | 48.0% | Not measured | Only GPT-5.5 measured |
| LiveBench long agentic | 44.6% | Not measured | Only GPT-5.5 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 40.4% | Not measured | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | 64.5% | Not measured | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | 8.3% | Not measured | Only GPT-5.5 measured |
| τ²-Bench Telecom | 93.9% | 80.7% | GPT-5.5 |
| APEX-Agents | 55.1% | Not measured | Only GPT-5.5 measured |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| Remote Labor Index | 6.3% | Not measured | Only GPT-5.5 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| OSWorld 2.0 | 13.0% | Not measured | Only GPT-5.5 measured |
| DeepResearch Bench | 54.0% | 45.2% | GPT-5.5 |
| PostTrainBench | 27.2% | Not measured | Only GPT-5.5 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Vending-Bench 2 | $7,524 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 11 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index150
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | 84.1% | GPT-5.5 |
| LiveBench reasoning | 89.7% | Not measured | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 88.3% | 80.4% | GPT-5.5 |
| GPQA Diamond (Artificial Analysis run) | 93.5% | 82.7% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 45.8% | 20.1% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | 90.7% | 81.8% | GPT-5.5 |
| ARC-AGI | 95.0% | 60.8% | GPT-5.5 |
| ARC-AGI-2 | 85.0% | 6.5% | GPT-5.5 |
| SimpleBench | 69.0% | 53.1% | GPT-5.5 |
| Chess puzzles | 63.0% | 37.0% | GPT-5.5 |
| Mystery game puzzles | 56.0% | 29.0% | GPT-5.5 |
| EBR-bench | 34.3% | Not measured | Only GPT-5.5 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | 1489 | 1402 | GPT-5.5 |
| LMArena Text: expert | 1507 | 1402 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1483 | 1411 | GPT-5.5 |
| LMArena Vision: diagrams | 1325 | 1220 | GPT-5.5 |
| LMArena Vision: homework | 1332 | 1234 | GPT-5.5 |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.5 | 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 | 84.4% | 84.4% | Tie |
| FrontierMath, tiers 1 to 3 | 85.3% | 33.3% | GPT-5.5 |
| FrontierMath, tier 4 | 72.5% | Not measured | Only GPT-5.5 measured |
| ProofBench | 50.0% | Not measured | Only GPT-5.5 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | 1487 | 1426 | GPT-5.5 |
| LMArena Text: mathematical work | 1496 | 1423 | GPT-5.5 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 88.1% | 85.6% | GPT-5.5 |
| LegalBench (Vals AI) | 86.5% | 83.8% | GPT-5.5 |
| CaseLaw (Vals AI) | 66.2% | Not measured | Only GPT-5.5 measured |
| CorpFin (Vals AI) | 68.4% | 59.7% | GPT-5.5 |
| TaxEval (Vals AI) | 75.0% | 74.6% | GPT-5.5 |
| MortgageTax (Vals AI) | 68.8% | 65.7% | GPT-5.5 |
| MedQA (Vals AI) | Not measured | 96.1% | Only o3 measured |
| MedCode (Vals AI) | 49.1% | 47.3% | GPT-5.5 |
| MMLU Pro (Artificial Analysis run) | Not measured | 85.3% | Only o3 measured |
| SimpleQA Verified | 63.0% | 49.4% | GPT-5.5 |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | 1485 | 1429 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1465 | 1448 | GPT-5.5 |
| LMArena Text: legal and government | 1491 | 1428 | GPT-5.5 |
| LMArena Text: business and finance | 1478 | 1393 | GPT-5.5 |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | 1036 | GPT-5.5 |
| Design Arena: UI components | 1260 | 1016 | GPT-5.5 |
| Design Arena: game dev | 1294 | 1034 | GPT-5.5 |
| Design Arena: data viz | 1265 | 1200 | GPT-5.5 |
| Design Arena: 3D | 1198 | Not measured | Only GPT-5.5 measured |
| Design Arena: all code categories | 1260 | 1021 | GPT-5.5 |
| Design Arena: SVG | 1234 | Not measured | Only GPT-5.5 measured |
| Design Arena: ASCII art | 1254 | Not measured | Only GPT-5.5 measured |
| LiveBench design | 70.3% | Not measured | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index291
| Benchmark | GPT-5.5 | o3 | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.6% | Not measured | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 60.9% | Not measured | Only GPT-5.5 measured |
| SAGE (Vals AI) | 51.5% | 41.8% | GPT-5.5 |
| MedScribe (Vals AI) | 86.9% | 76.7% | GPT-5.5 |
| Vals Multimodal Index | 68.1% | Not measured | Only GPT-5.5 measured |
| IFBench | 75.9% | 71.4% | GPT-5.5 |
| Long context reasoning (AA-LCR) | 84.3% | 74.7% | GPT-5.5 |
| DTBench | 96.0% | 84.8% | GPT-5.5 |
| LMCA | 54.3% | 39.7% | GPT-5.5 |
| 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 |
| Surface Evolver Bench | 88.1% | Not measured | Only GPT-5.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-5.5 measured |
| ExploitBench | 47.4% | Not measured | Only GPT-5.5 measured |
| CL-bench Life | 22.2% | Not measured | Only GPT-5.5 measured |
| LMArena Text | 1471 | 1410 | GPT-5.5 |
| LMArena Text: instruction following | 1478 | 1368 | GPT-5.5 |
| LMArena Text: creative writing | 1455 | 1359 | GPT-5.5 |
| LMArena Vision | 1297 | 1214 | GPT-5.5 |
| LMArena Text: English | 1470 | 1417 | GPT-5.5 |
| LMArena Text: non-English | 1467 | 1401 | GPT-5.5 |
| LMArena Text: Chinese | 1534 | 1437 | GPT-5.5 |
| LMArena Text: German | 1480 | 1420 | GPT-5.5 |
| LMArena Text: Japanese | 1499 | 1403 | GPT-5.5 |
| LMArena Text: Korean | 1460 | 1370 | GPT-5.5 |
| LMArena Text: French | 1485 | 1430 | GPT-5.5 |
| LMArena Text: Spanish | 1468 | 1395 | GPT-5.5 |
| LMArena Text: Russian | 1473 | 1406 | GPT-5.5 |
| LMArena Text: Polish | 1465 | 1438 | GPT-5.5 |
| LMArena Text: multi-turn | 1476 | 1405 | GPT-5.5 |
| LMArena Text: longer queries | 1484 | 1372 | GPT-5.5 |
| LMArena Text: writing and language | 1466 | 1372 | GPT-5.5 |
| LMArena Text: entertainment and media | 1449 | 1373 | GPT-5.5 |
| LMArena Vision: English | 1292 | 1221 | GPT-5.5 |
| LMArena Vision: Chinese | 1360 | 1237 | GPT-5.5 |
| LMArena Vision: reading text | 1306 | 1218 | GPT-5.5 |
| LMArena Vision: humor | 1293 | 1222 | GPT-5.5 |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | 1231 | 1234 | o3 |
| LMArena Vision: creative writing | 1301 | 1201 | GPT-5.5 |
| LMArena Document | 1486 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.5 | 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-5.5 | OpenAI o3 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 24, 2026 | April 16, 2025 |
| Context window | 1.05M | 200K |
| Input $/Mtok | $5.00 | $2.00 |
| Output $/Mtok | $30.00 | $8.00 |
| Coding index | 67.8 | 70.7 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $1.65 | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 better than o3 for coding?
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 67.8 for GPT-5.5. Pick GPT-5.5 when the context window is larger, 1.05M against 200K tokens, or when what matters is LiveCodeBench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, GPT-5.5 or o3?
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, GPT-5.5 costs $1.65 and o3 $0.571.
Which has the bigger context window, GPT-5.5 or o3?
GPT-5.5, with 1.05M tokens against 200K.
How many benchmarks is the GPT-5.5 vs o3 comparison based on?
76 benchmarks have a result for both models as of October 21, 2026. GPT-5.5'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.