Model comparison
Claude Opus 4.6 vs GPT-6 Luna
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 53.3 on the Noometry Index. GPT-6 Luna costs 50× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.
Last verified . 37 shared benchmarks.
Summary
- They share 37 benchmarks with published results for both. Claude Opus 4.6 scores higher in 8 categories and GPT-6 Luna in 2 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Claude Opus 4.6 leads 51.1 to 33.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 26.8% for Claude Opus 4.6 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 4.6.
- GPT-6 Luna accepts more context: 1.05M tokens versus 1M.
Side by side
| Claude Opus 4.6 | GPT-6 Luna | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 58.2 | 53.3 |
| Released | 2026-02-04 | 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 | 68 | 42 |
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Category by category
Coding Claude Opus 4.6 leads
Claude Opus 4.6: 57.2 (#20), GPT-6 Luna: 55.5 (#25)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| FrontierCode | 26.6% | 42.4% |
| LMArena WebDev | 1547 | 1581 |
| LMArena Coding | 1536 | 1439 |
| ALE-Bench | 996.5 | 1,577 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | — | 66.6% |
| SWE-bench Verified (bash only) | 75.6% | — |
| SWE-bench Multilingual | 72% | — |
| SciCode | — | 54.6% |
| GSO | 41.2% | — |
| WeirdML | 78% | — |
| AlgoTune | 1.47 | — |
Agentic & Tool Use Claude Opus 4.6 leads
Claude Opus 4.6: 51.1 (#4), GPT-6 Luna: 33.3 (#54)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| APEX-Agents | 46.3% | 44.3% |
| Terminal-Bench | 79.8% | — |
| Remote Labor Index | 4.2% | — |
| τ²-bench Banking | 27.3% | — |
| Cybench | 93% | — |
| DeepResearch Bench | 55.3% | — |
| GBAEval | 44.1% | — |
| GDP.pdf | — | 23% |
| LMArena Search | 1253 | — |
| METR Time Horizons | 78.9% | — |
| Vending-Bench 2 | 8,018 | — |
Reasoning Claude Opus 4.6 leads
Claude Opus 4.6: 57.8 (#23), GPT-6 Luna: 48.2 (#41)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| ARC-AGI-2 | 69.2% | 59.3% |
| NYT Connections (extended) | 92.1% | 68.7% |
| ARC-AGI-1 | 94% | 86.7% |
| Chess Puzzles | 17% | 31% |
| LMArena Hard Prompts | 1527 | 1411 |
| Mystery Game Puzzles | 25% | 7% |
| DTBench | 91.2% | 90.1% |
| LMCA | 55.8% | 44.5% |
| Epoch Capabilities Index | 155.24 | 156.28 |
| SimpleBench | 67.6% | — |
| Kagi LLM Benchmark | 83.6% | — |
| CritPt | — | 19.4% |
| EnigmaEval | 7.6% | — |
| Thematic Generalization | 80.6% | — |
| EBR-Bench | 12.7% | — |
| ForecastBench | 60 | — |
Math GPT-6 Luna leads
Claude Opus 4.6: 63.0 (#31), GPT-6 Luna: 76.1 (#15)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 66% | 78.9% |
| FrontierMath Tier 4 | 26.8% | 56.1% |
| OTIS Mock AIME 2024-2025 | 94.4% | 98.9% |
| ProofBench | 50% | 64% |
| LMArena Math | 1519 | 1416 |
| MathArena Final-Answer Competitions | 78.5% | — |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Claude Opus 4.6 leads
Claude Opus 4.6: 61.9 (#26), GPT-6 Luna: 57.0 (#41)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| GPQA Diamond | 90.5% | 90.5% |
| SimpleQA Verified | 47% | 41.4% |
| LMArena Expert | 1546 | 1444 |
| Humanity's Last Exam | 34.4% | — |
| Vectara Hallucination Rate | 12.2% | — |
Multimodal GPT-6 Luna leads
Claude Opus 4.6: 37.3 (#74), GPT-6 Luna: 42.4 (#30)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Vision | 1316 | 1217 |
| Furniture Assembly | 28.3% | 44.2% |
| Blueprint-Bench 2 | — | 31.2% |
| LMArena Document | 1507 | — |
Multilingual Claude Opus 4.6 leads
Claude Opus 4.6: 57.9 (#6), GPT-6 Luna: 50.5 (#117)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1489 | 1386 |
| LMArena Chinese | 1551 | 1433 |
| LMArena French | 1513 | 1420 |
| LMArena German | 1502 | 1369 |
| LMArena Japanese | 1484 | 1369 |
| LMArena Korean | 1464 | 1360 |
| LMArena Russian | 1497 | 1394 |
| LMArena Spanish | 1510 | 1393 |
Instruction Following Claude Opus 4.6 leads
Claude Opus 4.6: 79.5 (#4), GPT-6 Luna: 74.3 (#99)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1523 | 1409 |
Long Context Claude Opus 4.6 leads
Claude Opus 4.6: 48.1 (#13), GPT-6 Luna: 43.0 (#111)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1520 | 1409 |
| CL-bench | 20.7% | — |
| CL-bench Life | 17% | — |
Writing & Preference Claude Opus 4.6 leads
Claude Opus 4.6: 73.5 (#10), GPT-6 Luna: 58.3 (#119)
| Benchmark | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1503 | 1391 |
| LMArena Creative Writing | 1505 | 1363 |
| LMArena Multi-Turn | 1513 | 1396 |
| EQ-Bench Creative Writing | 1809 | — |
| EQ-Bench 4 | 1223 | — |
Frequently asked questions
Is Claude Opus 4.6 better than GPT-6 Luna?
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 53.3 on the Noometry Index. GPT-6 Luna costs 50× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.6 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 4.6 lists at $5 and $25.
Is Claude Opus 4.6 or GPT-6 Luna better for coding?
Claude Opus 4.6 scores higher on coding benchmarks: 57.2 versus 55.5 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 4.6 and GPT-6 Luna share?
37 benchmarks have published results for both models. Claude Opus 4.6 has 68 scored results on Noometry and GPT-6 Luna has 42.