Model comparison
Claude Opus 4.7 vs GPT-6 Luna
Claude Opus 4.7 is the stronger model overall, scoring 58.4 to 45.1 on the Noometry Index. GPT-6 Luna costs 50× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. Claude Opus 4.7 scores higher in 8 categories and GPT-6 Luna in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.7 leads 66.5 to 35.6.
- The biggest single-benchmark swing is NYT Connections (extended): 39% for Claude Opus 4.7 and 68.7% 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 4.7.
- GPT-6 Luna accepts more context: 1.05M tokens versus 1M.
Side by side
| Claude Opus 4.7 | GPT-6 Luna | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 58.4 | 45.1 |
| Released | 2026-04-14 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $5 | $0.10 |
| Output $ / M tokens | $25 | $0.50 |
| Results tracked | 66 | 18 |
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Category by category
Coding Claude Opus 4.7 leads
Claude Opus 4.7: 59.9 (#10), GPT-6 Luna: 54.7 (#24)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| FrontierCode | 38.5% | 42.4% |
| LMArena WebDev | 1557 | 1583 |
| LMArena Coding | 1519 | 1448 |
| SWE-bench Verified | 83.5% | — |
| DeepSWE | — | 66.6% |
| SciCode | 54.5% | — |
| GSO | 44.1% | — |
| WeirdML | 76.4% | — |
| MirrorCode | 31.1% | — |
| ALE-Bench | 1,323 | — |
Agentic & Tool Use Not comparable
Claude Opus 4.7: 47.9 (#8), GPT-6 Luna: —
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| Terminal-Bench | 80.2% | — |
| APEX-Agents | 49.2% | — |
| OSWorld 2.0 | 18.2% | — |
| τ²-bench Banking | 40.2% | — |
| PostTrainBench | 28.6% | — |
| ExploitBench | 26.5% | — |
| GBAEval | 43.8% | — |
| GDP.pdf | 21% | — |
| LMArena Search | 1233 | — |
| Vending-Bench 2 | 10,937 | — |
Reasoning Claude Opus 4.7 leads
Claude Opus 4.7: 53.7 (#24), GPT-6 Luna: 31.7 (#68)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| NYT Connections (extended) | 39% | 68.7% |
| LMArena Hard Prompts | 1507 | 1415 |
| ARC-AGI-2 | 75.8% | — |
| SimpleBench | 61.7% | — |
| Kagi LLM Benchmark | 80.7% | — |
| ARC-AGI-1 | 93.5% | — |
| CritPt | 12% | — |
| Chess Puzzles | 30% | — |
| Thematic Generalization | 72.8% | — |
| EBR-Bench | 19% | — |
| Mystery Game Puzzles | 28% | — |
| DTBench | 94.7% | — |
| LMCA | 52.2% | — |
| Epoch Capabilities Index | 156.39 | — |
| ForecastBench | 60.3 | — |
Math Claude Opus 4.7 leads
Claude Opus 4.7: 66.5 (#20), GPT-6 Luna: 35.6 (#140)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| LMArena Math | 1499 | 1396 |
| FrontierMath (Tiers 1-3) | 70.2% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 73.6% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 54% | — |
| FrontierMath (Feb 2025 set) | 43.8% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Claude Opus 4.7 leads
Claude Opus 4.7: 62.6 (#20), GPT-6 Luna: 40.1 (#110)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| LMArena Expert | 1521 | 1458 |
| GPQA Diamond | 90.2% | — |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 51.7% | — |
| Vectara Hallucination Rate | 12% | — |
Multimodal Too close to call
Claude Opus 4.7: 41.5 (#29), GPT-6 Luna: 42.0 (#27)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| Furniture Assembly | 33.3% | 44.2% |
| LMArena Vision | 1317 | — |
| Blueprint-Bench 2 | 24.5% | — |
| LMArena Document | 1495 | — |
Multilingual Claude Opus 4.7 leads
Claude Opus 4.7: 57.5 (#9), GPT-6 Luna: 51.0 (#111)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1481 | 1390 |
| LMArena Chinese | 1530 | 1425 |
| LMArena Russian | 1494 | 1392 |
| LMArena Spanish | 1497 | 1416 |
| LMArena French | 1505 | — |
| LMArena German | 1494 | — |
| LMArena Japanese | 1472 | — |
| LMArena Korean | 1466 | — |
Instruction Following Claude Opus 4.7 leads
Claude Opus 4.7: 78.6 (#9), GPT-6 Luna: 74.7 (#85)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1498 | 1414 |
Long Context Claude Opus 4.7 leads
Claude Opus 4.7: 46.1 (#24), GPT-6 Luna: 43.3 (#95)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1505 | 1417 |
Writing & Preference Claude Opus 4.7 leads
Claude Opus 4.7: 75.3 (#8), GPT-6 Luna: 58.4 (#115)
| Benchmark | Claude Opus 4.7 | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1491 | 1392 |
| LMArena Creative Writing | 1486 | 1357 |
| LMArena Multi-Turn | 1506 | 1400 |
| EQ-Bench Creative Writing | 1914 | — |
| EQ-Bench 4 | 1311 | — |
Frequently asked questions
Is Claude Opus 4.7 better than GPT-6 Luna?
Claude Opus 4.7 is the stronger model overall, scoring 58.4 to 45.1 on the Noometry Index. GPT-6 Luna costs 50× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.7 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 4.7 lists at $5 and $25.
Is Claude Opus 4.7 or GPT-6 Luna better for coding?
Claude Opus 4.7 scores higher on coding benchmarks: 59.9 versus 54.7 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 1M.
How many benchmarks do Claude Opus 4.7 and GPT-6 Luna share?
17 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and GPT-6 Luna has 18.