Model comparison
Claude Opus 4.5 vs GPT-6 Luna
GPT-6 Luna is the stronger model overall, scoring 53.3 to 50.5 on the Noometry Index.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. Claude Opus 4.5 scores higher in 5 categories and GPT-6 Luna in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 38.6.
- The biggest single-benchmark swing is FrontierMath Tier 4: 4.9% for Claude Opus 4.5 and 56.1% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- GPT-6 Luna accepts more context: 1.05M tokens versus 200K.
Side by side
| Claude Opus 4.5 | GPT-6 Luna | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 50.5 | 53.3 |
| Released | 2025-11-01 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 64K | 128K |
| Input $ / M tokens | $5 | $0.10 |
| Output $ / M tokens | $25 | $0.50 |
| Results tracked | 69 | 42 |
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Category by category
Coding Too close to call
Claude Opus 4.5: 54.8 (#27), GPT-6 Luna: 55.5 (#25)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| LMArena WebDev | 1494 | 1581 |
| LMArena Coding | 1504 | 1439 |
| ALE-Bench | 1,025 | 1,577 |
| SWE-bench Verified | 76.7% | — |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 76.8% | — |
| SWE-bench Multilingual | 70.7% | — |
| SciCode | — | 54.6% |
| GSO | 26.5% | — |
| WeirdML | 63.7% | — |
| AlgoTune | 1.77 | — |
Agentic & Tool Use Claude Opus 4.5 leads
Claude Opus 4.5: 47.3 (#12), GPT-6 Luna: 33.3 (#54)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| Terminal-Bench | 63.1% | — |
| APEX-Agents | — | 44.3% |
| Berkeley Function Calling Leaderboard | 77.5% | — |
| GDPval | 45.5% | — |
| Remote Labor Index | 3.8% | — |
| τ²-bench Airline | 84% | — |
| τ²-bench Banking | 24.7% | — |
| τ²-bench Retail | 79.6% | — |
| τ²-bench Telecom | 92.3% | — |
| Cybench | 82% | — |
| DeepResearch Bench | 54.8% | — |
| OSWorld | 66.3% | — |
| BALROG | 43.5% | — |
| GDP.pdf | — | 23% |
| LMArena Search | 1180 | — |
| METR Time Horizons | 75% | — |
| Vending-Bench 2 | 4,967 | — |
Reasoning GPT-6 Luna leads
Claude Opus 4.5: 42.6 (#51), GPT-6 Luna: 48.2 (#41)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| ARC-AGI-2 | 37.6% | 59.3% |
| NYT Connections (extended) | 52.5% | 68.7% |
| ARC-AGI-1 | 80% | 86.7% |
| Chess Puzzles | 12% | 31% |
| LMArena Hard Prompts | 1476 | 1411 |
| Mystery Game Puzzles | 22% | 7% |
| DTBench | 89.9% | 90.1% |
| LMCA | 44.5% | 44.5% |
| Epoch Capabilities Index | 150.09 | 156.28 |
| SimpleBench | 62% | — |
| Kagi LLM Benchmark | 80.2% | — |
| CritPt | — | 19.4% |
| EnigmaEval | 11.9% | — |
| EBR-Bench | 14.3% | — |
| ForecastBench | 60.7 | — |
Math GPT-6 Luna leads
Claude Opus 4.5: 38.6 (#132), GPT-6 Luna: 76.1 (#15)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 34.4% | 78.9% |
| FrontierMath Tier 4 | 4.9% | 56.1% |
| OTIS Mock AIME 2024-2025 | 86.1% | 98.9% |
| ProofBench | 36% | 64% |
| LMArena Math | 1463 | 1416 |
| FrontierMath (Feb 2025 set) | 20.7% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Too close to call
Claude Opus 4.5: 56.5 (#44), GPT-6 Luna: 57.0 (#41)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| GPQA Diamond | 86% | 90.5% |
| SimpleQA Verified | 45.7% | 41.4% |
| LMArena Expert | 1487 | 1444 |
| Humanity's Last Exam | 25.2% | — |
| Vectara Hallucination Rate | 10.9% | — |
Multimodal GPT-6 Luna leads
Claude Opus 4.5: 31.4 (#107), GPT-6 Luna: 42.4 (#30)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| Furniture Assembly | 28.3% | 44.2% |
| LMArena Vision | — | 1217 |
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Blueprint-Bench 2 | — | 31.2% |
| LMArena Document | 1462 | — |
Multilingual Claude Opus 4.5 leads
Claude Opus 4.5: 54.3 (#47), GPT-6 Luna: 50.5 (#117)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1438 | 1386 |
| LMArena Chinese | 1470 | 1433 |
| LMArena French | 1471 | 1420 |
| LMArena German | 1449 | 1369 |
| LMArena Japanese | 1416 | 1369 |
| LMArena Korean | 1424 | 1360 |
| LMArena Russian | 1447 | 1394 |
| LMArena Spanish | 1458 | 1393 |
Instruction Following Claude Opus 4.5 leads
Claude Opus 4.5: 77.5 (#19), GPT-6 Luna: 74.3 (#99)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1478 | 1409 |
Long Context Claude Opus 4.5 leads
Claude Opus 4.5: 46.5 (#22), GPT-6 Luna: 43.0 (#111)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1480 | 1409 |
| CL-bench | 21.1% | — |
Writing & Preference Claude Opus 4.5 leads
Claude Opus 4.5: 68.1 (#28), GPT-6 Luna: 58.3 (#119)
| Benchmark | Claude Opus 4.5 | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1451 | 1391 |
| LMArena Creative Writing | 1445 | 1363 |
| LMArena Multi-Turn | 1466 | 1396 |
| EQ-Bench Creative Writing | 1687 | — |
Frequently asked questions
Is Claude Opus 4.5 better than GPT-6 Luna?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 50.5 on the Noometry Index.
Which is cheaper, Claude Opus 4.5 or GPT-6 Luna?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; Claude Opus 4.5 lists at $5 and $25.
Is Claude Opus 4.5 or GPT-6 Luna better for coding?
They score almost the same on coding (54.8 vs 55.5); test both on your own repository before choosing.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 200K.
How many benchmarks do Claude Opus 4.5 and GPT-6 Luna share?
34 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GPT-6 Luna has 42.