Claude Opus 4.1 vs GPT-5.5
GPT-5.5 leads Claude Opus 4.1 on the Artificial Analysis Intelligence Index, 38.4 to 22.8, and GPT-5.5 costs 2.5x less per output token ($30.00 vs $75.00 per million). Updated October 22, 2026; 63 benchmarks have a result for both.
Claude Opus 4.1 or GPT-5.5: which to pick
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Claude Opus 4.1 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 63
- Claude Opus 4.1 only
- 10
- GPT-5.5 only
- 71
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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, six public coding benchmarks
No call
Only GPT-5.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 22.8.
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 76.3%.
Same board, both measured
Math
AIME
No call
Only Claude Opus 4.1 has a AIME result, 78.2%.
Directional only
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 87.9%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 against 1176.
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
GPT-5.5
Claude Opus 4.1 $0.052, 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
- Claude Opus 4.1 not benchmarked, GPT-5.5 71.3 from 5 benchmarks.
- Widest category
- Math, 35.6 points apart on average over 5 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: Claude Opus 4.1 12.6% against GPT-5.5 85.3%, 72.6 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.1 $75.00 against GPT-5.5 $30.00 per million output tokens, 2.5x apart.
- Context window
- Claude Opus 4.1 takes 200K 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 | Claude Opus 4.1 | Bars | GPT-5.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 9 | ||
| Math | 0 | 5 | ||
| Knowledge | 0 | 10 | ||
| Web design | 0 | 8 | ||
| General | 0 | 21 | ||
| All shared boards | 0 | 63 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | Claude Opus 4.1 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 85.1% | Only GPT-5.5 measured |
| LMArena WebDev | 1390 | 1513 | GPT-5.5 |
| AA Coding Index | Not measured | 74.9 | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | 66.5% | 85.3% | GPT-5.5 |
| 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 |
| SciCode | Not measured | 56.1% | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 60.6% | Only GPT-5.5 measured |
| Terminal-Bench | 38.0% | 84.7% | GPT-5.5 |
| FrontierCode | Not measured | 43.0% | Only GPT-5.5 measured |
| GSO-Bench | Not measured | 40.2% | Only GPT-5.5 measured |
| WeirdML | 45.9% | 84.9% | GPT-5.5 |
| MirrorCode | Not measured | 10.0% | Only GPT-5.5 measured |
| Cybench | 42.0% | Not measured | Only Claude Opus 4.1 measured |
| ALE-Bench | Not measured | 1943.0 | Only GPT-5.5 measured |
| LMArena Text: coding | 1479 | 1494 | GPT-5.5 |
| LMArena Text: software and IT | 1456 | 1493 | GPT-5.5 |
| LMArena WebDev: React | Not measured | 1506 | Only GPT-5.5 measured |
| LMArena WebDev: HTML | 1399 | 1543 | GPT-5.5 |
| LMArena WebDev: from an image | Not measured | 1532 | Only GPT-5.5 measured |
| SciCode | 4.6% | Not measured | Only Claude Opus 4.1 measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Claude Opus 4.1 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.8 | 38.4 | GPT-5.5 |
| Epoch Capabilities Index | 144.1 | 159.7 | GPT-5.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Claude Opus 4.1 | 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 | 1142 | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | Not measured | 1164 | 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 | 1017 | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | Not measured | 1084 | 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 | Not measured | 93.9% | Only GPT-5.5 measured |
| APEX-Agents | Not measured | 55.1% | Only GPT-5.5 measured |
| METR time horizons | 66.8% | Not measured | Only Claude Opus 4.1 measured |
| Remote Labor Index | Not measured | 6.3% | Only GPT-5.5 measured |
| GDPval | 43.6% | Not measured | Only Claude Opus 4.1 measured |
| OSWorld 2.0 | Not measured | 13.0% | Only GPT-5.5 measured |
| DeepResearch Bench | 48.3% | 54.0% | GPT-5.5 |
| 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 14 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
| Benchmark | Claude Opus 4.1 | GPT-5.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 76.3% | 93.2% | GPT-5.5 |
| LiveBench reasoning | Not measured | 89.7% | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 77.5% | 88.3% | GPT-5.5 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 93.5% | Only GPT-5.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 45.8% | Only GPT-5.5 measured |
| GPQA Diamond (Epoch AI run) | 77.3% | 90.7% | GPT-5.5 |
| ARC-AGI | Not measured | 95.0% | Only GPT-5.5 measured |
| ARC-AGI-2 | Not measured | 85.0% | Only GPT-5.5 measured |
| SimpleBench | 60.0% | 69.0% | GPT-5.5 |
| Chess puzzles | Not measured | 63.0% | Only GPT-5.5 measured |
| Mystery game puzzles | 21.0% | 56.0% | GPT-5.5 |
| EBR-bench | 7.9% | 34.3% | GPT-5.5 |
| VPCT | 35.0% | Not measured | Only Claude Opus 4.1 measured |
| LMArena Text: hard prompts | 1442 | 1489 | GPT-5.5 |
| LMArena Text: expert | 1438 | 1507 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1451 | 1483 | GPT-5.5 |
| LMArena Vision: diagrams | Not measured | 1325 | Only GPT-5.5 measured |
| LMArena Vision: homework | Not measured | 1332 | Only GPT-5.5 measured |
| Kagi LLM Benchmark | Not measured | 88.8% | Only GPT-5.5 measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Claude Opus 4.1 | GPT-5.5 | Leader |
|---|---|---|---|
| AIME | 78.2% | Not measured | Only Claude Opus 4.1 measured |
| MATH 500 (Vals AI) | 95.4% | Not measured | Only Claude Opus 4.1 measured |
| MGSM (Vals AI) | 94.4% | Not measured | Only Claude Opus 4.1 measured |
| OTIS Mock AIME | 68.9% | 84.4% | GPT-5.5 |
| FrontierMath, tiers 1 to 3 | 12.6% | 85.3% | GPT-5.5 |
| FrontierMath, tier 4 | 2.4% | 72.5% | GPT-5.5 |
| ProofBench | Not measured | 50.0% | Only GPT-5.5 measured |
| LMArena Text: math | 1431 | 1487 | GPT-5.5 |
| LMArena Text: mathematical work | 1434 | 1496 | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010
| Benchmark | Claude Opus 4.1 | GPT-5.5 | Leader |
|---|---|---|---|
| MMLU Pro | 87.9% | 88.1% | GPT-5.5 |
| LegalBench (Vals AI) | 83.5% | 86.5% | GPT-5.5 |
| 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) | 73.7% | 75.0% | GPT-5.5 |
| MortgageTax (Vals AI) | 61.1% | 68.8% | GPT-5.5 |
| MedQA (Vals AI) | 93.6% | Not measured | Only Claude Opus 4.1 measured |
| MedCode (Vals AI) | 47.2% | 49.1% | GPT-5.5 |
| SimpleQA Verified | Not measured | 63.0% | Only GPT-5.5 measured |
| LMArena Text: science | 1422 | 1485 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1416 | 1465 | GPT-5.5 |
| LMArena Text: legal and government | 1421 | 1491 | GPT-5.5 |
| LMArena Text: business and finance | 1406 | 1478 | GPT-5.5 |
| FACTS Benchmark Suite | 40.4% | 66.7% | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | Claude Opus 4.1 | GPT-5.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1176 | 1256 | GPT-5.5 |
| Design Arena: UI components | 1162 | 1257 | GPT-5.5 |
| Design Arena: game dev | 1171 | 1293 | GPT-5.5 |
| Design Arena: data viz | 1166 | 1264 | GPT-5.5 |
| Design Arena: 3D | 1152 | 1197 | GPT-5.5 |
| Design Arena: all code categories | 1170 | 1260 | GPT-5.5 |
| Design Arena: SVG | 1152 | 1234 | GPT-5.5 |
| Design Arena: ASCII art | 1167 | 1255 | GPT-5.5 |
| LiveBench design | Not measured | 70.3% | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
| Benchmark | Claude Opus 4.1 | 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) | 31.4% | 51.5% | GPT-5.5 |
| MedScribe (Vals AI) | 73.9% | 86.9% | GPT-5.5 |
| Vals Multimodal Index | Not measured | 68.1% | Only GPT-5.5 measured |
| IFBench | Not measured | 75.9% | Only GPT-5.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 84.3% | Only GPT-5.5 measured |
| DTBench | 80.0% | 96.0% | GPT-5.5 |
| LMCA | 37.1% | 54.3% | GPT-5.5 |
| Creative writing (Lech Mazur) | 84.7% | Not measured | Only Claude Opus 4.1 measured |
| 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 | 1419 | 1471 | GPT-5.5 |
| LMArena Text: instruction following | 1435 | 1478 | GPT-5.5 |
| LMArena Text: creative writing | 1412 | 1455 | GPT-5.5 |
| LMArena Vision | Not measured | 1297 | Only GPT-5.5 measured |
| LMArena Text: English | 1433 | 1470 | GPT-5.5 |
| LMArena Text: non-English | 1405 | 1467 | GPT-5.5 |
| LMArena Text: Chinese | 1427 | 1534 | GPT-5.5 |
| LMArena Text: German | 1413 | 1480 | GPT-5.5 |
| LMArena Text: Japanese | 1377 | 1499 | GPT-5.5 |
| LMArena Text: Korean | 1381 | 1460 | GPT-5.5 |
| LMArena Text: French | 1433 | 1485 | GPT-5.5 |
| LMArena Text: Spanish | 1448 | 1468 | GPT-5.5 |
| LMArena Text: Russian | 1422 | 1473 | GPT-5.5 |
| LMArena Text: Polish | 1413 | 1465 | GPT-5.5 |
| LMArena Text: multi-turn | 1444 | 1476 | GPT-5.5 |
| LMArena Text: longer queries | 1455 | 1484 | GPT-5.5 |
| LMArena Text: writing and language | 1418 | 1466 | GPT-5.5 |
| LMArena Text: entertainment and media | 1398 | 1449 | GPT-5.5 |
| 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 7 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.1 | 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-5.5 costs less
Repository review
50K input + 3K output tokens
GPT-5.5 costs less
Agent loop
200K input + 10K output tokens
GPT-5.5 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 | Anthropic Claude Opus 4.1 | OpenAI GPT-5.5 |
|---|---|---|
| Maker | Anthropic | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 5, 2025 | April 24, 2026 |
| Context window | 200K | 1.05M |
| Input $/Mtok | $15.00 | $5.00 |
| Output $/Mtok | $75.00 | $30.00 |
| Coding index | Not benchmarked | 71.3 |
| Independent measurements behind it | 1 | 5 |
| Cost per solved task | Not computable | $1.65 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.1 better than GPT-5.5 for coding?
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; Claude Opus 4.1 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Claude Opus 4.1 or GPT-5.5?
Claude Opus 4.1 costs $15.00 per million input tokens and $75.00 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, Claude Opus 4.1 or GPT-5.5?
GPT-5.5, with 1.05M tokens against 200K.
How many benchmarks is the Claude Opus 4.1 vs GPT-5.5 comparison based on?
63 benchmarks have a result for both models as of October 22, 2026. Claude Opus 4.1's coding index rests on 1 independent benchmark and GPT-5.5's on 5. Where only one model has a result, the page says so instead of naming a winner.