Model comparison
GPT-4o vs GPT-6 Luna
GPT-6 Luna is the stronger model overall, scoring 53.3 to 28.6 on the Noometry Index.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GPT-4o scores higher in 0 categories and GPT-6 Luna in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 10.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.4% for GPT-4o and 98.9% for GPT-6 Luna.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- GPT-6 Luna accepts more context: 1.05M tokens versus 128K.
Side by side
| GPT-4o | GPT-6 Luna | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 28.6 | 53.3 |
| Released | 2024-05-13 | 2026-09-22 |
| Weights | Proprietary | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 16K | 128K |
| Input $ / M tokens | $2.50 | $0.10 |
| Output $ / M tokens | $10 | $0.50 |
| Results tracked | 72 | 42 |
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Category by category
Coding GPT-6 Luna leads
GPT-4o: 24.8 (#328), GPT-6 Luna: 55.5 (#25)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| LMArena Coding | 1297 | 1439 |
| SWE-bench Verified | 31% | — |
| DeepSWE | — | 66.6% |
| FrontierCode | — | 42.4% |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| LMArena WebDev | — | 1581 |
| SciCode | — | 54.6% |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| ALE-Bench | — | 1,577 |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use GPT-6 Luna leads
GPT-4o: 21.0 (#141), GPT-6 Luna: 33.3 (#54)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| APEX-Agents | — | 44.3% |
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| GDP.pdf | — | 23% |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning GPT-6 Luna leads
GPT-4o: 9.4 (#343), GPT-6 Luna: 48.2 (#41)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| ARC-AGI-2 | 0% | 59.3% |
| ARC-AGI-1 | 4.5% | 86.7% |
| CritPt | 0% | 19.4% |
| Chess Puzzles | 13% | 31% |
| LMArena Hard Prompts | 1281 | 1411 |
| DTBench | 64.5% | 90.1% |
| LMCA | 16.6% | 44.5% |
| Epoch Capabilities Index | 128.97 | 156.28 |
| SimpleBench | 17.8% | — |
| NYT Connections (extended) | — | 68.7% |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | 60.9% | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
Math GPT-6 Luna leads
GPT-4o: 10.6 (#312), GPT-6 Luna: 76.1 (#15)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0.4% | 78.9% |
| OTIS Mock AIME 2024-2025 | 6.4% | 98.9% |
| LMArena Math | 1285 | 1416 |
| FrontierMath Tier 4 | — | 56.1% |
| ProofBench | — | 64% |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge GPT-6 Luna leads
GPT-4o: 28.8 (#242), GPT-6 Luna: 57.0 (#41)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| GPQA Diamond | 49.2% | 90.5% |
| SimpleQA Verified | 26% | 41.4% |
| LMArena Expert | 1250 | 1444 |
| Humanity's Last Exam | 2.7% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| MMLU | 88.1% | — |
Multimodal GPT-6 Luna leads
GPT-4o: 34.5 (#91), GPT-6 Luna: 42.4 (#30)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| LMArena Vision | 1137 | 1217 |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| Blueprint-Bench 2 | — | 31.2% |
| Furniture Assembly | — | 44.2% |
| ScienceQA | 88.5% | — |
Multilingual GPT-6 Luna leads
GPT-4o: 43.2 (#186), GPT-6 Luna: 50.5 (#117)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| LMArena Non-English | 1283 | 1386 |
| LMArena Chinese | 1277 | 1433 |
| LMArena French | 1304 | 1420 |
| LMArena German | 1282 | 1369 |
| LMArena Japanese | 1257 | 1369 |
| LMArena Korean | 1234 | 1360 |
| LMArena Russian | 1286 | 1394 |
| LMArena Spanish | 1292 | 1393 |
Instruction Following GPT-6 Luna leads
GPT-4o: 66.6 (#207), GPT-6 Luna: 74.3 (#99)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| LMArena Instruction Following | 1278 | 1409 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context GPT-6 Luna leads
GPT-4o: 39.4 (#179), GPT-6 Luna: 43.0 (#111)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| LMArena Longer Query | 1289 | 1409 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference GPT-6 Luna leads
GPT-4o: 52.6 (#166), GPT-6 Luna: 58.3 (#119)
| Benchmark | GPT-4o | GPT-6 Luna |
|---|---|---|
| LMArena Text | 1300 | 1391 |
| LMArena Creative Writing | 1292 | 1363 |
| LMArena Multi-Turn | 1302 | 1396 |
| Short-Story Creative Writing | 81.8% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than GPT-6 Luna?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 28.6 on the Noometry Index.
Which is cheaper, GPT-4o 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-4o lists at $2.50 and $10.
Is GPT-4o or GPT-6 Luna better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 24.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 128K.
How many benchmarks do GPT-4o and GPT-6 Luna share?
29 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and GPT-6 Luna has 42.