Model comparison
GPT-5.6 Luna vs Llama 3.1-8B
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 7.8× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
MetaSummary
- They share 28 benchmarks with published results for both. GPT-5.6 Luna scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for GPT-5.6 Luna and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 54.6 | 23.0 |
| Released | 2026-07-09 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.20 | $0.05 |
| Output $ / M tokens | $1.20 | $0.08 |
| Results tracked | 52 | 43 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 54.5 (#28), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| SciCode | 53.6% | 13.2% |
| WeirdML | 60.9% | 1.7% |
| LMArena Coding | 1466 | 1195 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1519 | — |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,667 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GPT-5.6 Luna leads
GPT-5.6 Luna: 34.4 (#45), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| BALROG | 45.6% | 15.1% |
| APEX-Agents | 43% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.6 (#43), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| CritPt | 20.6% | 0% |
| Chess Puzzles | 40% | 0% |
| LMArena Hard Prompts | 1451 | 1175 |
| DTBench | 89.1% | 50.9% |
| LMCA | 48.5% | 5.4% |
| Epoch Capabilities Index | 156.39 | 116.57 |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| Mystery Game Puzzles | 21% | — |
| Surface Evolver Bench | 61.9% | — |
| PIQA | — | 81.2% |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.7 (#14), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 1.7% |
| LMArena Math | 1458 | 1179 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| ProofBench | 60% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 91.6% | 27% |
| LMArena Expert | 1478 | 1144 |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GPT-5.6 Luna: 42.7 (#28), Llama 3.1-8B: —
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1258 | — |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual GPT-5.6 Luna leads
GPT-5.6 Luna: 52.8 (#78), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1417 | 1148 |
| LMArena Chinese | 1470 | 1151 |
| LMArena French | 1456 | 1177 |
| LMArena German | 1454 | 1144 |
| LMArena Japanese | 1411 | 1061 |
| LMArena Korean | 1415 | 1053 |
| LMArena Russian | 1428 | 1158 |
| LMArena Spanish | 1448 | 1169 |
Instruction Following GPT-5.6 Luna leads
GPT-5.6 Luna: 75.6 (#57), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1437 | 1159 |
| IFEval | — | 74.3% |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#82), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1436 | 1182 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-5.6 Luna | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1431 | 1187 |
| LMArena Creative Writing | 1396 | 1154 |
| EQ-Bench Creative Writing | 1829 | 713 |
| LMArena Multi-Turn | 1434 | 1172 |
| WildBench | — | 68.7% |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Llama 3.1-8B?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 7.8× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Luna or Llama 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Llama 3.1-8B better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.6 Luna and Llama 3.1-8B share?
28 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and Llama 3.1-8B has 43.