Model comparison
GPT-6 Luna vs Llama 3.2 1B
GPT-6 Luna is the stronger model overall, scoring 53.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 2.8× less per token, which makes it the better buy when GPT-6 Luna's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. GPT-6 Luna scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.9% for GPT-6 Luna and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.10 / $0.50 for GPT-6 Luna.
- GPT-6 Luna accepts more context: 1.05M tokens versus 60K.
- Llama 3.2 1B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Luna | Llama 3.2 1B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 53.3 | 20.1 |
| Released | 2026-09-22 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 60K |
| Max output | 128K | 54K |
| Input $ / M tokens | $0.10 | $0.027 |
| Output $ / M tokens | $0.50 | $0.20 |
| Results tracked | 42 | 22 |
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Category by category
Coding GPT-6 Luna leads
GPT-6 Luna: 55.5 (#25), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1439 | 1070 |
| DeepSWE | 66.6% | — |
| FrontierCode | 42.4% | — |
| LMArena WebDev | 1581 | — |
| SciCode | 54.6% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,577 | — |
Agentic & Tool Use GPT-6 Luna leads
GPT-6 Luna: 33.3 (#54), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 44.3% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| BALROG | — | 6.6% |
| GDP.pdf | 23% | — |
Reasoning GPT-6 Luna leads
GPT-6 Luna: 48.2 (#41), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 31% | 0% |
| LMArena Hard Prompts | 1411 | 1044 |
| Epoch Capabilities Index | 156.28 | 101.99 |
| ARC-AGI-2 | 59.3% | — |
| NYT Connections (extended) | 68.7% | — |
| ARC-AGI-1 | 86.7% | — |
| CritPt | 19.4% | — |
| Mystery Game Puzzles | 7% | — |
| DTBench | 90.1% | — |
| LMCA | 44.5% | — |
Math GPT-6 Luna leads
GPT-6 Luna: 76.1 (#15), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.9% | 0.6% |
| LMArena Math | 1416 | 1086 |
| FrontierMath (Tiers 1-3) | 78.9% | — |
| FrontierMath Tier 4 | 56.1% | — |
| ProofBench | 64% | — |
Knowledge GPT-6 Luna leads
GPT-6 Luna: 57.0 (#41), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 90.5% | 23.9% |
| LMArena Expert | 1444 | 1007 |
| SimpleQA Verified | 41.4% | — |
Multimodal Not comparable
GPT-6 Luna: 42.4 (#30), Llama 3.2 1B: —
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| LMArena Vision | 1217 | — |
| Blueprint-Bench 2 | 31.2% | — |
| Furniture Assembly | 44.2% | — |
Multilingual GPT-6 Luna leads
GPT-6 Luna: 50.5 (#117), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1386 | 973 |
| LMArena Chinese | 1433 | 959 |
| LMArena German | 1369 | 1014 |
| LMArena Russian | 1394 | 941 |
| LMArena French | 1420 | — |
| LMArena Japanese | 1369 | — |
| LMArena Korean | 1360 | — |
| LMArena Spanish | 1393 | — |
Instruction Following GPT-6 Luna leads
GPT-6 Luna: 74.3 (#99), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1409 | 1031 |
Long Context GPT-6 Luna leads
GPT-6 Luna: 43.0 (#111), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1409 | 1050 |
Writing & Preference GPT-6 Luna leads
GPT-6 Luna: 58.3 (#119), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GPT-6 Luna | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1391 | 1055 |
| LMArena Creative Writing | 1363 | 1033 |
| LMArena Multi-Turn | 1396 | 1030 |
| EQ-Bench Creative Writing | — | 200 |
Frequently asked questions
Is GPT-6 Luna better than Llama 3.2 1B?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 20.1 on the Noometry Index. Llama 3.2 1B costs 2.8× less per token, which makes it the better buy when GPT-6 Luna's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Luna or Llama 3.2 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; GPT-6 Luna lists at $0.10 and $0.50.
Is GPT-6 Luna or Llama 3.2 1B better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 60K.
How many benchmarks do GPT-6 Luna and Llama 3.2 1B share?
17 benchmarks have published results for both models. GPT-6 Luna has 42 scored results on Noometry and Llama 3.2 1B has 22.