Model comparison
GPT-6 Sol vs Llama 3.1-8B
GPT-6 Sol is the stronger model overall, scoring 61.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 70× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GPT-6 Sol 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-6 Sol leads 87.2 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6 Sol 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 $2 / $10 for GPT-6 Sol.
- GPT-6 Sol 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-6 Sol | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 61.8 | 23.0 |
| Released | 2026-09-22 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $0.05 |
| Output $ / M tokens | $10 | $0.08 |
| Results tracked | 45 | 43 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| SciCode | 57.6% | 13.2% |
| LMArena Coding | 1447 | 1195 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 2,462 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 37.2 (#36), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| CritPt | 30.9% | 0% |
| LMArena Hard Prompts | 1418 | 1175 |
| DTBench | 97.3% | 50.9% |
| LMCA | 59.1% | 5.4% |
| Epoch Capabilities Index | 162.72 | 116.57 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| Chess Puzzles | — | 0% |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| PIQA | — | 81.2% |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 1.7% |
| LMArena Math | 1402 | 1179 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 94.3% | 27% |
| LMArena Expert | 1439 | 1144 |
| SimpleQA Verified | 60.7% | — |
| MMLU-Pro | — | 40.6% |
| Vectara Hallucination Rate | 6.5% | — |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Llama 3.1-8B: —
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 50.5 (#118), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1385 | 1148 |
| LMArena Chinese | 1405 | 1151 |
| LMArena French | 1410 | 1177 |
| LMArena German | 1390 | 1144 |
| LMArena Japanese | 1385 | 1061 |
| LMArena Korean | 1341 | 1053 |
| LMArena Russian | 1401 | 1158 |
| LMArena Spanish | 1384 | 1169 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1159 |
| IFEval | — | 74.3% |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1411 | 1182 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-6 Sol | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1395 | 1187 |
| LMArena Creative Writing | 1378 | 1154 |
| EQ-Bench Creative Writing | 2125 | 713 |
| LMArena Multi-Turn | 1412 | 1172 |
| WildBench | — | 68.7% |
Frequently asked questions
Is GPT-6 Sol better than Llama 3.1-8B?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 23.0 on the Noometry Index. Llama 3.1-8B costs 70× 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 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Llama 3.1-8B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 20.2 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 3.1-8B share?
25 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Llama 3.1-8B has 43.