Model comparison
GPT-6 Sol vs Llama 3-70B
GPT-6 Sol is the stronger model overall, scoring 61.8 to 28.8 on the Noometry Index.
Last verified . 21 shared benchmarks.
MetaSummary
- They share 21 benchmarks with published results for both. GPT-6 Sol scores higher in 9 categories and Llama 3-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 12.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6 Sol and 4.3% for Llama 3-70B.
- Llama 3-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Llama 3-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 61.8 | 28.8 |
| Released | 2026-09-22 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | — |
| Max output | 128K | — |
| Input $ / M tokens | $2 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 45 | 31 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Llama 3-70B: 35.8 (#218)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Coding | 1447 | 1206 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| BigCodeBench Instruct | — | 43.6% |
| BigCodeBench Complete | — | 54.5% |
| ALE-Bench | 2,462 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 69% |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 37.2 (#36), Llama 3-70B: 21.1 (#139)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| Cybench | — | 5% |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Llama 3-70B: 18.0 (#288)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1195 |
| DTBench | 97.3% | 54.2% |
| Epoch Capabilities Index | 162.72 | 122.93 |
| ARC-AGI-2 | 89.6% | — |
| Kagi LLM Benchmark | — | 35.1% |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| LMCA | 59.1% | — |
| ForecastBench | — | 57.1 |
| WinoGrande | — | 83.5% |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Llama 3-70B: 12.8 (#305)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 4.3% |
| LMArena Math | 1402 | 1218 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| ProofBench | 83% | — |
| MATH Level 5 | — | 22.6% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Llama 3-70B: 20.8 (#277)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| GPQA Diamond | 94.3% | 40.6% |
| LMArena Expert | 1439 | 1149 |
| SimpleQA Verified | 60.7% | — |
| Vectara Hallucination Rate | 6.5% | — |
| MMLU | — | 79.3% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Llama 3-70B: —
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 50.5 (#118), Llama 3-70B: 33.6 (#251)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Non-English | 1385 | 1142 |
| LMArena Chinese | 1405 | 1114 |
| LMArena French | 1410 | 1232 |
| LMArena German | 1390 | 1169 |
| LMArena Japanese | 1385 | 1017 |
| LMArena Korean | 1341 | 1017 |
| LMArena Russian | 1401 | 1159 |
| LMArena Spanish | 1384 | 1241 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Llama 3-70B: 62.5 (#238)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1194 |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Llama 3-70B: 35.6 (#240)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Longer Query | 1411 | 1174 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Llama 3-70B: 42.8 (#231)
| Benchmark | GPT-6 Sol | Llama 3-70B |
|---|---|---|
| LMArena Text | 1395 | 1221 |
| LMArena Creative Writing | 1378 | 1210 |
| LMArena Multi-Turn | 1412 | 1223 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Llama 3-70B?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 28.8 on the Noometry Index.
Is GPT-6 Sol or Llama 3-70B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 35.8 in the Noometry coding category.
How many benchmarks do GPT-6 Sol and Llama 3-70B share?
21 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Llama 3-70B has 31.