Model comparison
Claude Opus 4.1 vs GPT-5.6 Luna
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 41.0 on the Noometry Index.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. Claude Opus 4.1 scores higher in 2 categories and GPT-5.6 Luna in 8 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 22.3.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 12.6% for Claude Opus 4.1 and 82.1% for GPT-5.6 Luna.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $15 / $75 for Claude Opus 4.1.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 200K.
Side by side
| Claude Opus 4.1 | GPT-5.6 Luna | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 41.0 | 54.6 |
| Released | 2025-08-05 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 32K | 128K |
| Input $ / M tokens | $15 | $0.20 |
| Output $ / M tokens | $75 | $1.20 |
| Results tracked | 48 | 52 |
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Category by category
Coding GPT-5.6 Luna leads
Claude Opus 4.1: 44.4 (#73), GPT-5.6 Luna: 54.5 (#28)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| LMArena WebDev | 1390 | 1519 |
| WeirdML | 45.9% | 60.9% |
| LMArena Coding | 1479 | 1466 |
| ALE-Bench | 674.77 | 1,667 |
| SWE-bench Verified | 73.3% | — |
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| SciCode | — | 53.6% |
| AlgoTune | 1.34 | — |
Agentic & Tool Use Too close to call
Claude Opus 4.1: 35.0 (#41), GPT-5.6 Luna: 34.4 (#45)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| Terminal-Bench | 38% | — |
| APEX-Agents | — | 43% |
| GDPval | 43.6% | — |
| Cybench | 42% | — |
| DeepResearch Bench | 48.3% | — |
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| LMArena Search | 1148 | — |
| METR Time Horizons | 66.8% | — |
| Vending-Bench 2 | — | 4,095 |
Reasoning GPT-5.6 Luna leads
Claude Opus 4.1: 32.2 (#76), GPT-5.6 Luna: 47.6 (#43)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| SimpleBench | 60% | 46.8% |
| Chess Puzzles | 7% | 40% |
| LMArena Hard Prompts | 1443 | 1451 |
| Mystery Game Puzzles | 21% | 21% |
| DTBench | 80% | 89.1% |
| LMCA | 37.1% | 48.5% |
| Epoch Capabilities Index | 144.12 | 156.39 |
| ARC-AGI-2 | — | 59.5% |
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| EnigmaEval | 7.2% | — |
| EBR-Bench | 7.9% | — |
| Surface Evolver Bench | — | 61.9% |
| ForecastBench | 62 | — |
Math GPT-5.6 Luna leads
Claude Opus 4.1: 22.3 (#277), GPT-5.6 Luna: 77.7 (#14)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 12.6% | 82.1% |
| FrontierMath Tier 4 | 2.4% | 61% |
| OTIS Mock AIME 2024-2025 | 68.9% | 98.3% |
| LMArena Math | 1431 | 1458 |
| ProofBench | — | 60% |
| FrontierMath (Feb 2025 set) | 7.2% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GPT-5.6 Luna leads
Claude Opus 4.1: 42.0 (#101), GPT-5.6 Luna: 58.5 (#34)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | 77.3% | 91.6% |
| LMArena Expert | 1439 | 1478 |
| Humanity's Last Exam | 11.5% | — |
| SimpleQA Verified | — | 41% |
| Confabulations | 17.1% | — |
| Vectara Hallucination Rate | 11.8% | — |
Multimodal GPT-5.6 Luna leads
Claude Opus 4.1: 26.8 (#119), GPT-5.6 Luna: 42.7 (#28)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1258 |
| VPCT | 35% | — |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual Too close to call
Claude Opus 4.1: 52.0 (#95), GPT-5.6 Luna: 52.8 (#78)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| LMArena Non-English | 1405 | 1417 |
| LMArena Chinese | 1427 | 1470 |
| LMArena French | 1431 | 1456 |
| LMArena German | 1413 | 1454 |
| LMArena Japanese | 1378 | 1411 |
| LMArena Korean | 1380 | 1415 |
| LMArena Russian | 1422 | 1428 |
| LMArena Spanish | 1448 | 1448 |
Instruction Following Too close to call
Claude Opus 4.1: 75.6 (#58), GPT-5.6 Luna: 75.6 (#57)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | 1435 | 1437 |
Long Context Too close to call
Claude Opus 4.1: 44.5 (#63), GPT-5.6 Luna: 43.9 (#82)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | 1455 | 1436 |
Writing & Preference GPT-5.6 Luna leads
Claude Opus 4.1: 62.4 (#74), GPT-5.6 Luna: 68.0 (#29)
| Benchmark | Claude Opus 4.1 | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | 1419 | 1431 |
| LMArena Creative Writing | 1412 | 1396 |
| LMArena Multi-Turn | 1444 | 1434 |
| Short-Story Creative Writing | 84.7% | — |
| EQ-Bench Creative Writing | — | 1829 |
| EQ-Bench 4 | — | 1156 |
Frequently asked questions
Is Claude Opus 4.1 better than GPT-5.6 Luna?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 41.0 on the Noometry Index.
Which is cheaper, Claude Opus 4.1 or GPT-5.6 Luna?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; Claude Opus 4.1 lists at $15 and $75.
Is Claude Opus 4.1 or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 44.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 200K.
How many benchmarks do Claude Opus 4.1 and GPT-5.6 Luna share?
30 benchmarks have published results for both models. Claude Opus 4.1 has 48 scored results on Noometry and GPT-5.6 Luna has 52.