Model comparison
GPT-6 Sol vs Llama 3.1-70B
GPT-6 Sol is the stronger model overall, scoring 61.8 to 29.6 on the Noometry Index. Llama 3.1-70B costs 10× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. GPT-6 Sol scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6 Sol and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 61.8 | 29.6 |
| Released | 2026-09-22 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $0.40 |
| Output $ / M tokens | $10 | $0.40 |
| Results tracked | 45 | 35 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Coding | 1447 | 1260 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 2,462 | — |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 37.2 (#36), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1241 |
| DTBench | 97.3% | 60% |
| LMCA | 59.1% | 14.8% |
| Epoch Capabilities Index | 162.72 | 125.92 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 3.6% |
| LMArena Math | 1402 | 1252 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 94.3% | 44.2% |
| LMArena Expert | 1439 | 1209 |
| SimpleQA Verified | 60.7% | — |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 6.5% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Llama 3.1-70B: —
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 50.5 (#118), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1385 | 1219 |
| LMArena Chinese | 1405 | 1215 |
| LMArena French | 1410 | 1261 |
| LMArena German | 1390 | 1222 |
| LMArena Japanese | 1385 | 1132 |
| LMArena Korean | 1341 | 1140 |
| LMArena Russian | 1401 | 1234 |
| LMArena Spanish | 1384 | 1253 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1231 |
| IFEval | — | 82.1% |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1411 | 1241 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1395 | 1261 |
| LMArena Creative Writing | 1378 | 1232 |
| EQ-Bench Creative Writing | 2125 | 784 |
| LMArena Multi-Turn | 1412 | 1256 |
| WildBench | — | 75.8% |
Frequently asked questions
Is GPT-6 Sol better than Llama 3.1-70B?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 29.6 on the Noometry Index. Llama 3.1-70B costs 10× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Sol or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Llama 3.1-70B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 128K.
How many benchmarks do GPT-6 Sol and Llama 3.1-70B share?
23 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Llama 3.1-70B has 35.