Model comparison
Claude Opus 4.7 vs GPT-5.5
GPT-5.5 is the stronger model overall, scoring 63.4 to 58.3 on the Noometry Index.
Last verified . 64 shared benchmarks.
Summary
- They share 64 benchmarks with published results for both. Claude Opus 4.7 scores higher in 4 categories and GPT-5.5 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 53.8.
- The biggest single-benchmark swing is NYT Connections (extended): 39% for Claude Opus 4.7 and 96.2% for GPT-5.5.
- Claude Opus 4.7 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 4.7 | GPT-5.5 | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 58.3 | 63.4 |
| Released | 2026-04-14 | 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 | 66 | 71 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Claude Opus 4.7 leads
Claude Opus 4.7: 59.6 (#13), GPT-5.5: 58.2 (#17)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| SWE-bench Verified | 83.5% | 80.6% |
| FrontierCode | 38.5% | 43% |
| LMArena WebDev | 1558 | 1513 |
| SciCode | 54.5% | 56.1% |
| GSO | 44.1% | 40.2% |
| WeirdML | 76.4% | 84.9% |
| LMArena Coding | 1518 | 1494 |
| MirrorCode | 31.1% | 10% |
| ALE-Bench | 1,323 | 1,943 |
| DeepSWE | — | 67% |
Agentic & Tool Use GPT-5.5 leads
Claude Opus 4.7: 47.9 (#10), GPT-5.5: 50.7 (#6)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| Terminal-Bench | 80.2% | 84.7% |
| APEX-Agents | 49.2% | 55.1% |
| OSWorld 2.0 | 18.2% | 13% |
| τ²-bench Banking | 40.2% | 44.6% |
| PostTrainBench | 28.6% | 27.2% |
| ExploitBench | 26.5% | 47.4% |
| GBAEval | 43.8% | 53.2% |
| GDP.pdf | 21% | 26% |
| LMArena Search | 1233 | 1242 |
| Vending-Bench 2 | 10,937 | 7,524 |
| Remote Labor Index | — | 6.3% |
| DeepResearch Bench | — | 54% |
Reasoning GPT-5.5 leads
Claude Opus 4.7: 53.8 (#29), GPT-5.5: 72.8 (#11)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| ARC-AGI-2 | 75.8% | 85% |
| SimpleBench | 61.7% | 69% |
| Kagi LLM Benchmark | 80.7% | 88.8% |
| NYT Connections (extended) | 39% | 96.2% |
| ARC-AGI-1 | 93.5% | 95% |
| CritPt | 12% | 27.1% |
| Chess Puzzles | 30% | 54% |
| EBR-Bench | 19% | 34.3% |
| LMArena Hard Prompts | 1506 | 1489 |
| Mystery Game Puzzles | 28% | 56% |
| DTBench | 94.7% | 96% |
| LMCA | 52.2% | 54.3% |
| Epoch Capabilities Index | 156.25 | 159.1 |
| ForecastBench | 60.3 | 60.6 |
| Thematic Generalization | 72.8% | — |
| Surface Evolver Bench | — | 88.1% |
| Bench to the Future 3 | — | 0.14 |
Math GPT-5.5 leads
Claude Opus 4.7: 66.7 (#26), GPT-5.5: 81.7 (#11)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 70.2% | 85.3% |
| FrontierMath Tier 4 | 31.7% | 72.5% |
| MathArena Final-Answer Competitions | 73.6% | 94.3% |
| OTIS Mock AIME 2024-2025 | 97.8% | 100% |
| ProofBench | 54% | 50% |
| LMArena Math | 1499 | 1486 |
| FrontierMath (Feb 2025 set) | 43.8% | 51.7% |
| FrontierMath Tier 4 (v1) | 22.9% | 35.4% |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.5 leads
Claude Opus 4.7: 62.6 (#23), GPT-5.5: 64.4 (#17)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| GPQA Diamond | 90.2% | 94% |
| SimpleQA Verified | 51.7% | 63% |
| Vectara Hallucination Rate | 12% | 9.3% |
| LMArena Expert | 1521 | 1508 |
| Humanity's Last Exam | 36.2% | — |
Multimodal GPT-5.5 leads
Claude Opus 4.7: 41.2 (#38), GPT-5.5: 46.9 (#12)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| LMArena Vision | 1316 | 1297 |
| Blueprint-Bench 2 | 24.5% | 36.2% |
| Furniture Assembly | 33.3% | 44.2% |
| LMArena Document | 1495 | 1486 |
Multilingual Too close to call
Claude Opus 4.7: 57.3 (#10), GPT-5.5: 56.4 (#20)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 1480 | 1467 |
| LMArena Chinese | 1531 | 1533 |
| LMArena French | 1503 | 1486 |
| LMArena German | 1495 | 1480 |
| LMArena Japanese | 1472 | 1498 |
| LMArena Korean | 1464 | 1460 |
| LMArena Russian | 1494 | 1473 |
| LMArena Spanish | 1495 | 1468 |
Instruction Following Too close to call
Claude Opus 4.7: 78.4 (#10), GPT-5.5: 77.5 (#18)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 1498 | 1479 |
Long Context GPT-5.5 leads
Claude Opus 4.7: 46.2 (#25), GPT-5.5: 48.3 (#12)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| LMArena Longer Query | 1505 | 1484 |
| CL-bench Life | — | 22.2% |
Writing & Preference Claude Opus 4.7 leads
Claude Opus 4.7: 75.1 (#8), GPT-5.5: 72.7 (#13)
| Benchmark | Claude Opus 4.7 | GPT-5.5 |
|---|---|---|
| LMArena Text | 1490 | 1472 |
| LMArena Creative Writing | 1486 | 1455 |
| EQ-Bench Creative Writing | 1914 | 1844 |
| EQ-Bench 4 | 1311 | 1315 |
| LMArena Multi-Turn | 1505 | 1476 |
Frequently asked questions
Is Claude Opus 4.7 better than GPT-5.5?
GPT-5.5 is the stronger model overall, scoring 63.4 to 58.3 on the Noometry Index.
Which is cheaper, Claude Opus 4.7 or GPT-5.5?
Claude Opus 4.7 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 4.7 or GPT-5.5 better for coding?
Claude Opus 4.7 scores higher on coding benchmarks: 59.6 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 4.7 and GPT-5.5 share?
64 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and GPT-5.5 has 71.