GPT-4o vs GPT-5.5
Updated October 21, 2026. 16 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 16 shared
Decision reading
Pick GPT-5.5 for coding: it scores 67.8 on the Vibe Coding Index against 38.8 for GPT-4o.
Pick GPT-4o when output tokens cost 3.0x less.
Model AGPT-4oOpenAI38.8/1001 independent benchmarkNo cost per solved taskModel BGPT-5.5OpenAI67.8/1002 independent benchmarks$1.65 per solved task
- Shared results
- 16
- GPT-4o only
- 10
- GPT-5.5 only
- 116
- 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-5.5
GPT-5.5 scores 67.8 against 38.8.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.5 has a AA Intelligence Index result, 38.4.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.5 has a GPQA Diamond result, 93.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.5 has a MMLU Pro result, 88.1%.
Directional only
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 against 831.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o
GPT-4o $0.0075, GPT-5.5 $0.020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.5 has the number, $1.65.
Directional only
At a glance
- Coding index
- GPT-4o 38.8 from 1 benchmark, GPT-5.5 67.8 from 2 benchmarks.
- Widest category
- Agentic work, 68.7 points apart on average over 1 shared benchmark.
- Widest single gap
- ARC-AGI: GPT-4o 4.5% against GPT-5.5 95.0%, 90.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against GPT-5.5 $30.00 per million output tokens, 3.0x apart.
- Context window
- GPT-4o takes 128K tokens, GPT-5.5 1.05M.
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-5.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 16 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o | GPT-5.5 | 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 |
| LiveBench coding | Not measured | 85.1% | Only GPT-5.5 measured |
| LMArena WebDev | Not measured | 1513 | Only GPT-5.5 measured |
| AA Coding Index | Not measured | 74.9 | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | Not measured | 85.3% | Only GPT-5.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.6% | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 69.8% | Only GPT-5.5 measured |
| Code migration (Vals AI) | Not measured | 45.2% | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 76.4% | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | Not measured | 62.5% | Only GPT-5.5 measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| SciCode | Not measured | 56.1% | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | 60.6% | GPT-5.5 |
| Terminal-Bench | Not measured | 84.7% | Only GPT-5.5 measured |
| FrontierCode | Not measured | 43.0% | Only GPT-5.5 measured |
| GSO-Bench | Not measured | 40.2% | Only GPT-5.5 measured |
| WeirdML | Not measured | 84.9% | Only GPT-5.5 measured |
| MirrorCode | Not measured | 10.0% | Only GPT-5.5 measured |
| ALE-Bench | Not measured | 1943.0 | Only GPT-5.5 measured |
| LMArena Text: coding | Not measured | 1494 | Only GPT-5.5 measured |
| LMArena Text: software and IT | Not measured | 1493 | Only GPT-5.5 measured |
| LMArena WebDev: React | Not measured | 1506 | Only GPT-5.5 measured |
| LMArena WebDev: HTML | Not measured | 1543 | Only GPT-5.5 measured |
| LMArena WebDev: from an image | Not measured | 1532 | Only GPT-5.5 measured |
| Neither model has a result on the other 10 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | GPT-5.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 38.4 | Only GPT-5.5 measured |
| Epoch Capabilities Index | Not measured | 159.7 | Only GPT-5.5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | GPT-5.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 36.4 | Only GPT-5.5 measured |
| Design Arena: agents building web apps | Not measured | 1117 | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | Not measured | 1089 | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | Not measured | 1143 | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | Not measured | 1169 | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | Not measured | 1173 | Only GPT-5.5 measured |
| Design Arena: agents building games | Not measured | 1179 | Only GPT-5.5 measured |
| Design Arena: HTML slides | Not measured | 1067 | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | Not measured | 1108 | Only GPT-5.5 measured |
| LiveBench research | Not measured | 61.6% | Only GPT-5.5 measured |
| LiveBench browser | Not measured | 48.0% | Only GPT-5.5 measured |
| LiveBench long agentic | Not measured | 44.6% | Only GPT-5.5 measured |
| SREBench (Vals AI) | Not measured | 3.8% | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | Not measured | 51.8% | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 40.4% | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 64.5% | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | Not measured | 8.3% | Only GPT-5.5 measured |
| τ²-Bench Telecom | 25.1% | 93.9% | GPT-5.5 |
| APEX-Agents | Not measured | 55.1% | Only GPT-5.5 measured |
| Remote Labor Index | Not measured | 6.3% | Only GPT-5.5 measured |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| OSWorld 2.0 | Not measured | 13.0% | Only GPT-5.5 measured |
| DeepResearch Bench | Not measured | 54.0% | Only GPT-5.5 measured |
| PostTrainBench | Not measured | 27.2% | Only GPT-5.5 measured |
| Vending-Bench 2 | Not measured | $7,524 | Only GPT-5.5 measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o | GPT-5.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 93.2% | Only GPT-5.5 measured |
| LiveBench reasoning | Not measured | 89.7% | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | Not measured | 88.3% | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | 54.3% | 93.5% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | 45.8% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | Not measured | 90.7% | Only GPT-5.5 measured |
| ARC-AGI | 4.5% | 95.0% | GPT-5.5 |
| ARC-AGI-2 | 0.0% | 85.0% | GPT-5.5 |
| SimpleBench | Not measured | 69.0% | Only GPT-5.5 measured |
| Chess puzzles | Not measured | 63.0% | Only GPT-5.5 measured |
| Mystery game puzzles | Not measured | 56.0% | Only GPT-5.5 measured |
| EBR-bench | Not measured | 34.3% | Only GPT-5.5 measured |
| LMArena Text: hard prompts | Not measured | 1489 | Only GPT-5.5 measured |
| LMArena Text: expert | Not measured | 1507 | Only GPT-5.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1483 | Only GPT-5.5 measured |
| LMArena Vision: diagrams | Not measured | 1325 | Only GPT-5.5 measured |
| LMArena Vision: homework | Not measured | 1332 | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4o | GPT-5.5 | 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 |
| OTIS Mock AIME | Not measured | 84.4% | Only GPT-5.5 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 85.3% | Only GPT-5.5 measured |
| FrontierMath, tier 4 | Not measured | 72.5% | Only GPT-5.5 measured |
| ProofBench | Not measured | 50.0% | Only GPT-5.5 measured |
| LMArena Text: math | Not measured | 1487 | Only GPT-5.5 measured |
| LMArena Text: mathematical work | Not measured | 1496 | Only GPT-5.5 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-4o | GPT-5.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.1% | Only GPT-5.5 measured |
| LegalBench (Vals AI) | Not measured | 86.5% | Only GPT-5.5 measured |
| CaseLaw (Vals AI) | Not measured | 66.2% | Only GPT-5.5 measured |
| CorpFin (Vals AI) | Not measured | 68.4% | Only GPT-5.5 measured |
| TaxEval (Vals AI) | Not measured | 75.0% | Only GPT-5.5 measured |
| MortgageTax (Vals AI) | Not measured | 68.8% | Only GPT-5.5 measured |
| MedCode (Vals AI) | Not measured | 49.1% | Only GPT-5.5 measured |
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| SimpleQA Verified | Not measured | 63.0% | Only GPT-5.5 measured |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| LMArena Text: science | Not measured | 1485 | Only GPT-5.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1465 | Only GPT-5.5 measured |
| LMArena Text: legal and government | Not measured | 1491 | Only GPT-5.5 measured |
| LMArena Text: business and finance | Not measured | 1478 | Only GPT-5.5 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-4o | GPT-5.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | 1256 | GPT-5.5 |
| Design Arena: UI components | 885 | 1260 | GPT-5.5 |
| Design Arena: game dev | 906 | 1294 | GPT-5.5 |
| Design Arena: data viz | 858 | 1265 | GPT-5.5 |
| Design Arena: 3D | 869 | 1198 | GPT-5.5 |
| Design Arena: all code categories | 861 | 1260 | GPT-5.5 |
| Design Arena: SVG | Not measured | 1234 | Only GPT-5.5 measured |
| Design Arena: ASCII art | Not measured | 1254 | Only GPT-5.5 measured |
| LiveBench design | Not measured | 70.3% | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.6% | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | Not measured | 60.9% | Only GPT-5.5 measured |
| SAGE (Vals AI) | Not measured | 51.5% | Only GPT-5.5 measured |
| MedScribe (Vals AI) | Not measured | 86.9% | Only GPT-5.5 measured |
| Vals Multimodal Index | Not measured | 68.1% | Only GPT-5.5 measured |
| IFBench | 34.3% | 75.9% | GPT-5.5 |
| Long context reasoning (AA-LCR) | 49.3% | 84.3% | GPT-5.5 |
| DTBench | 64.5% | 96.0% | GPT-5.5 |
| LMCA | 16.6% | 54.3% | GPT-5.5 |
| Surface Evolver Bench | Not measured | 88.1% | Only GPT-5.5 measured |
| Furniture assembly | Not measured | 44.2% | Only GPT-5.5 measured |
| ExploitBench | Not measured | 47.4% | Only GPT-5.5 measured |
| CL-bench Life | Not measured | 22.2% | Only GPT-5.5 measured |
| LMArena Text | Not measured | 1471 | Only GPT-5.5 measured |
| LMArena Text: instruction following | Not measured | 1478 | Only GPT-5.5 measured |
| LMArena Text: creative writing | Not measured | 1455 | Only GPT-5.5 measured |
| LMArena Vision | Not measured | 1297 | Only GPT-5.5 measured |
| LMArena Text: English | Not measured | 1470 | Only GPT-5.5 measured |
| LMArena Text: non-English | Not measured | 1467 | Only GPT-5.5 measured |
| LMArena Text: Chinese | Not measured | 1534 | Only GPT-5.5 measured |
| LMArena Text: German | Not measured | 1480 | Only GPT-5.5 measured |
| LMArena Text: Japanese | Not measured | 1499 | Only GPT-5.5 measured |
| LMArena Text: Korean | Not measured | 1460 | Only GPT-5.5 measured |
| LMArena Text: French | Not measured | 1485 | Only GPT-5.5 measured |
| LMArena Text: Spanish | Not measured | 1468 | Only GPT-5.5 measured |
| LMArena Text: Russian | Not measured | 1473 | Only GPT-5.5 measured |
| LMArena Text: Polish | Not measured | 1465 | Only GPT-5.5 measured |
| LMArena Text: multi-turn | Not measured | 1476 | Only GPT-5.5 measured |
| LMArena Text: longer queries | Not measured | 1484 | Only GPT-5.5 measured |
| LMArena Text: writing and language | Not measured | 1466 | Only GPT-5.5 measured |
| LMArena Text: entertainment and media | Not measured | 1449 | Only GPT-5.5 measured |
| LMArena Vision: English | Not measured | 1292 | Only GPT-5.5 measured |
| LMArena Vision: Chinese | Not measured | 1360 | Only GPT-5.5 measured |
| LMArena Vision: reading text | Not measured | 1306 | Only GPT-5.5 measured |
| LMArena Vision: humor | Not measured | 1293 | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | Not measured | 1231 | Only GPT-5.5 measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-5.5 measured |
| LMArena Document | Not measured | 1486 | Only GPT-5.5 measured |
| Neither model has a result on the other 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | GPT-5.5 | 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$0.0075Fits in one request
GPT-5.5$0.020Fits in one request
GPT-4o costs less
Repository review
50K input + 3K output tokens
GPT-4o$0.155Fits in one request
GPT-5.5$0.340Fits in one request
GPT-4o costs less
Agent loop
200K input + 10K output tokens
GPT-4o$0.600Exceeds the context window
GPT-5.5$1.30Fits in one request
GPT-4o 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-5.5 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | May 13, 2024 | April 24, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $2.50 | $5.00 |
| Output $/Mtok | $10.00 | $30.00 |
| Coding index | 38.8 | 67.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $1.65 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o better than GPT-5.5 for coding?
Pick GPT-5.5 for coding: it scores 67.8 on the Vibe Coding Index against 38.8 for GPT-4o. Pick GPT-4o when output tokens cost 3.0x less.
Which is cheaper, GPT-4o or GPT-5.5?
GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens; GPT-5.5 costs $5.00 and $30.00. Cost per solved task is known for GPT-5.5 only.
Which has the bigger context window, GPT-4o or GPT-5.5?
GPT-5.5, with 1.05M tokens against 128K.
How many benchmarks is the GPT-4o vs GPT-5.5 comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. GPT-4o's coding index rests on 1 independent benchmark and GPT-5.5's on 2. Where only one model has a result, the page says so instead of naming a winner.