Model comparison
GPT-5 vs GPT-6 Luna
GPT-6 Luna is the stronger model overall, scoring 53.3 to 50.9 on the Noometry Index.
Last verified . 35 shared benchmarks.
Summary
- They share 35 benchmarks with published results for both. GPT-5 scores higher in 4 categories and GPT-6 Luna in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 43.0.
- The biggest single-benchmark swing is ARC-AGI-2: 9.9% for GPT-5 and 59.3% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-6 Luna accepts more context: 1.05M tokens versus 400K.
Side by side
| GPT-5 | GPT-6 Luna | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 50.9 | 53.3 |
| Released | 2025-08-07 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $1.25 | $0.10 |
| Output $ / M tokens | $10 | $0.50 |
| Results tracked | 69 | 42 |
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Category by category
Coding GPT-6 Luna leads
GPT-5: 50.3 (#47), GPT-6 Luna: 55.5 (#25)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| LMArena WebDev | 1418 | 1581 |
| SciCode | 42.9% | 54.6% |
| LMArena Coding | 1436 | 1439 |
| ALE-Bench | 1,162 | 1,577 |
| SWE-bench Verified | 73.6% | — |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use Too close to call
GPT-5: 33.1 (#56), GPT-6 Luna: 33.3 (#54)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| APEX-Agents | — | 44.3% |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| GDP.pdf | — | 23% |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
Reasoning GPT-6 Luna leads
GPT-5: 38.3 (#64), GPT-6 Luna: 48.2 (#41)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| ARC-AGI-2 | 9.9% | 59.3% |
| ARC-AGI-1 | 65.7% | 86.7% |
| CritPt | 12.6% | 19.4% |
| Chess Puzzles | 37% | 31% |
| LMArena Hard Prompts | 1416 | 1411 |
| Mystery Game Puzzles | 23% | 7% |
| DTBench | 90.7% | 90.1% |
| LMCA | 40% | 44.5% |
| Epoch Capabilities Index | 150 | 156.28 |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| NYT Connections (extended) | — | 68.7% |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| ForecastBench | 61.4 | — |
Math GPT-6 Luna leads
GPT-5: 55.0 (#44), GPT-6 Luna: 76.1 (#15)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.4% | 78.9% |
| FrontierMath Tier 4 | 22% | 56.1% |
| OTIS Mock AIME 2024-2025 | 91.4% | 98.9% |
| ProofBench | 18% | 64% |
| LMArena Math | 1407 | 1416 |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge Too close to call
GPT-5: 56.6 (#43), GPT-6 Luna: 57.0 (#41)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| GPQA Diamond | 86.2% | 90.5% |
| SimpleQA Verified | 50.1% | 41.4% |
| LMArena Expert | 1419 | 1444 |
| Humanity's Last Exam | 25.3% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| Vectara Hallucination Rate | 14.7% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal GPT-5 leads
GPT-5: 46.8 (#13), GPT-6 Luna: 42.4 (#30)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| LMArena Vision | 1232 | 1217 |
| GeoBench | 81% | — |
| VPCT | 66% | — |
| Blueprint-Bench 2 | — | 31.2% |
| Furniture Assembly | — | 44.2% |
Multilingual Too close to call
GPT-5: 51.4 (#110), GPT-6 Luna: 50.5 (#117)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1397 | 1386 |
| LMArena Chinese | 1422 | 1433 |
| LMArena French | 1410 | 1420 |
| LMArena German | 1416 | 1369 |
| LMArena Japanese | 1409 | 1369 |
| LMArena Korean | 1360 | 1360 |
| LMArena Russian | 1406 | 1394 |
| LMArena Spanish | 1399 | 1393 |
Instruction Following Too close to call
GPT-5: 73.8 (#113), GPT-6 Luna: 74.3 (#99)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1388 | 1409 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), GPT-6 Luna: 43.0 (#111)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1399 | 1409 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference GPT-5 leads
GPT-5: 63.4 (#65), GPT-6 Luna: 58.3 (#119)
| Benchmark | GPT-5 | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1406 | 1391 |
| LMArena Creative Writing | 1365 | 1363 |
| LMArena Multi-Turn | 1426 | 1396 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than GPT-6 Luna?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 50.9 on the Noometry Index.
Which is cheaper, GPT-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; GPT-5 lists at $1.25 and $10.
Is GPT-5 or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 50.3 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 400K.
How many benchmarks do GPT-5 and GPT-6 Luna share?
35 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and GPT-6 Luna has 42.