Model comparison
GPT-6 Luna vs Llama-3.3-70B-Instruct
GPT-6 Luna is the stronger model overall, scoring 53.3 to 30.6 on the Noometry Index.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. GPT-6 Luna scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for GPT-6 Luna and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.10 / $0.50 for GPT-6 Luna.
- GPT-6 Luna accepts more context: 1.05M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Luna | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 53.3 | 30.6 |
| Released | 2026-09-22 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.10 | $0.10 |
| Output $ / M tokens | $0.50 | $0.32 |
| Results tracked | 42 | 43 |
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Category by category
Coding GPT-6 Luna leads
GPT-6 Luna: 55.5 (#25), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 54.6% | 26% |
| LMArena Coding | 1439 | 1268 |
| DeepSWE | 66.6% | — |
| FrontierCode | 42.4% | — |
| LMArena WebDev | 1581 | — |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,577 | — |
Agentic & Tool Use GPT-6 Luna leads
GPT-6 Luna: 33.3 (#54), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| APEX-Agents | 44.3% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 23% | — |
Reasoning GPT-6 Luna leads
GPT-6 Luna: 48.2 (#41), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 19.4% | 0% |
| LMArena Hard Prompts | 1411 | 1257 |
| DTBench | 90.1% | 59.5% |
| LMCA | 44.5% | 17.5% |
| Epoch Capabilities Index | 156.28 | 127.33 |
| ARC-AGI-2 | 59.3% | — |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 68.7% | — |
| ARC-AGI-1 | 86.7% | — |
| Chess Puzzles | 31% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 7% | — |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GPT-6 Luna leads
GPT-6 Luna: 76.1 (#15), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 5.1% |
| LMArena Math | 1416 | 1267 |
| FrontierMath (Tiers 1-3) | 78.9% | — |
| FrontierMath Tier 4 | 56.1% | — |
| ProofBench | 64% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GPT-6 Luna leads
GPT-6 Luna: 57.0 (#41), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 90.5% | 47.4% |
| LMArena Expert | 1444 | 1225 |
| SimpleQA Verified | 41.4% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GPT-6 Luna: 42.4 (#30), Llama-3.3-70B-Instruct: —
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1217 | — |
| Blueprint-Bench 2 | 31.2% | — |
| Furniture Assembly | 44.2% | — |
Multilingual GPT-6 Luna leads
GPT-6 Luna: 50.5 (#117), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1386 | 1236 |
| LMArena Chinese | 1433 | 1217 |
| LMArena French | 1420 | 1281 |
| LMArena German | 1369 | 1251 |
| LMArena Japanese | 1369 | 1150 |
| LMArena Korean | 1360 | 1143 |
| LMArena Russian | 1394 | 1252 |
| LMArena Spanish | 1393 | 1270 |
Instruction Following GPT-6 Luna leads
GPT-6 Luna: 74.3 (#99), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1409 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GPT-6 Luna leads
GPT-6 Luna: 43.0 (#111), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1409 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GPT-6 Luna leads
GPT-6 Luna: 58.3 (#119), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | GPT-6 Luna | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1391 | 1274 |
| LMArena Creative Writing | 1363 | 1250 |
| LMArena Multi-Turn | 1396 | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GPT-6 Luna better than Llama-3.3-70B-Instruct?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 30.6 on the Noometry Index.
Which is cheaper, GPT-6 Luna or Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; GPT-6 Luna lists at $0.10 and $0.50.
Is GPT-6 Luna or Llama-3.3-70B-Instruct better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 31.0 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-6 Luna and Llama-3.3-70B-Instruct share?
24 benchmarks have published results for both models. GPT-6 Luna has 42 scored results on Noometry and Llama-3.3-70B-Instruct has 43.