Model comparison
Claude Opus 4.8 vs GPT-5.5
GPT-5.5 is the stronger model overall, scoring 63.4 to 60.7 on the Noometry Index.
Last verified . 64 shared benchmarks.
Summary
- They share 64 benchmarks with published results for both. Claude Opus 4.8 scores higher in 1 category and GPT-5.5 in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 64.7.
- The biggest single-benchmark swing is Blueprint-Bench 2: 14.5% for Claude Opus 4.8 and 36.2% for GPT-5.5.
- Claude Opus 4.8 is cheaper at $5 / $25 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 1M.
Side by side
| Claude Opus 4.8 | GPT-5.5 | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 60.7 | 63.4 |
| Released | 2026-05-28 | 2026-04-23 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $5 | $5 |
| Output $ / M tokens | $25 | $30 |
| Results tracked | 65 | 71 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), GPT-5.5: 58.2 (#17)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| DeepSWE | 59% | 67% |
| FrontierCode | 46.5% | 43% |
| LMArena WebDev | 1556 | 1513 |
| SciCode | 53.5% | 56.1% |
| GSO | 47.1% | 40.2% |
| WeirdML | 82.9% | 84.9% |
| LMArena Coding | 1490 | 1494 |
| ALE-Bench | 1,564 | 1,943 |
| SWE-bench Verified | — | 80.6% |
| MirrorCode | — | 10% |
Agentic & Tool Use GPT-5.5 leads
Claude Opus 4.8: 47.6 (#11), GPT-5.5: 50.7 (#6)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| APEX-Agents | 48.9% | 55.1% |
| OSWorld 2.0 | 20.6% | 13% |
| Remote Labor Index | 8.3% | 6.3% |
| τ²-bench Banking | 39.7% | 44.6% |
| DeepResearch Bench | 50.2% | 54% |
| PostTrainBench | 33.8% | 27.2% |
| GBAEval | 70.9% | 53.2% |
| GDP.pdf | 24% | 26% |
| LMArena Search | 1204 | 1242 |
| Vending-Bench 2 | 5,787 | 7,524 |
| Terminal-Bench | — | 84.7% |
| ExploitBench | — | 47.4% |
Reasoning GPT-5.5 leads
Claude Opus 4.8: 64.7 (#16), GPT-5.5: 72.8 (#11)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| ARC-AGI-2 | 72.1% | 85% |
| SimpleBench | 64.8% | 69% |
| Kagi LLM Benchmark | 88.8% | 88.8% |
| NYT Connections (extended) | 91.1% | 96.2% |
| ARC-AGI-1 | 92.5% | 95% |
| CritPt | 20.9% | 27.1% |
| Chess Puzzles | 34% | 54% |
| EBR-Bench | 28.6% | 34.3% |
| LMArena Hard Prompts | 1482 | 1489 |
| Mystery Game Puzzles | 36% | 56% |
| DTBench | 94.9% | 96% |
| LMCA | 57.5% | 54.3% |
| Surface Evolver Bench | 87.5% | 88.1% |
| Bench to the Future 3 | 0.14 | 0.14 |
| Epoch Capabilities Index | 158.21 | 159.1 |
| ForecastBench | 59.9 | 60.6 |
| EnigmaEval | 23.5% | — |
Math GPT-5.5 leads
Claude Opus 4.8: 78.4 (#13), GPT-5.5: 81.7 (#11)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 80% | 85.3% |
| FrontierMath Tier 4 | 56.1% | 72.5% |
| MathArena Final-Answer Competitions | 91.8% | 94.3% |
| OTIS Mock AIME 2024-2025 | 98.3% | 100% |
| ProofBench | 69% | 50% |
| LMArena Math | 1487 | 1486 |
| FrontierMath (Feb 2025 set) | 47.2% | 51.7% |
| FrontierMath Tier 4 (v1) | 31.3% | 35.4% |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.5 leads
Claude Opus 4.8: 61.3 (#29), GPT-5.5: 64.4 (#17)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| GPQA Diamond | 91% | 94% |
| SimpleQA Verified | 53% | 63% |
| LMArena Expert | 1502 | 1508 |
| Vectara Hallucination Rate | — | 9.3% |
Multimodal GPT-5.5 leads
Claude Opus 4.8: 42.9 (#26), GPT-5.5: 46.9 (#12)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| LMArena Vision | 1294 | 1297 |
| Blueprint-Bench 2 | 14.5% | 36.2% |
| Furniture Assembly | 42.5% | 44.2% |
| LMArena Document | 1475 | 1486 |
Multilingual GPT-5.5 leads
Claude Opus 4.8: 55.2 (#33), GPT-5.5: 56.4 (#20)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 1450 | 1467 |
| LMArena Chinese | 1507 | 1533 |
| LMArena French | 1481 | 1486 |
| LMArena German | 1472 | 1480 |
| LMArena Japanese | 1440 | 1498 |
| LMArena Korean | 1432 | 1460 |
| LMArena Russian | 1474 | 1473 |
| LMArena Spanish | 1466 | 1468 |
Instruction Following Too close to call
Claude Opus 4.8: 77.4 (#24), GPT-5.5: 77.5 (#18)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 1476 | 1479 |
Long Context GPT-5.5 leads
Claude Opus 4.8: 45.4 (#35), GPT-5.5: 48.3 (#12)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| LMArena Longer Query | 1483 | 1484 |
| CL-bench Life | — | 22.2% |
Writing & Preference Too close to call
Claude Opus 4.8: 72.0 (#16), GPT-5.5: 72.7 (#13)
| Benchmark | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|
| LMArena Text | 1461 | 1472 |
| LMArena Creative Writing | 1454 | 1455 |
| EQ-Bench Creative Writing | 1840 | 1844 |
| EQ-Bench 4 | 1281 | 1315 |
| LMArena Multi-Turn | 1476 | 1476 |
Frequently asked questions
Is Claude Opus 4.8 better than GPT-5.5?
GPT-5.5 is the stronger model overall, scoring 63.4 to 60.7 on the Noometry Index.
Which is cheaper, Claude Opus 4.8 or GPT-5.5?
Claude Opus 4.8 is cheaper. It lists at $5 per million input tokens and $25 per million output tokens; GPT-5.5 lists at $5 and $30.
Is Claude Opus 4.8 or GPT-5.5 better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 58.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 1M.
How many benchmarks do Claude Opus 4.8 and GPT-5.5 share?
64 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GPT-5.5 has 71.