Model comparison
GPT-6 Sol vs Llama-3.3-70B-Instruct
GPT-6 Sol is the stronger model overall, scoring 49.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 26× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
MetaSummary
- They share 14 benchmarks with published results for both. GPT-6 Sol scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 13.7.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 49.2 | 30.3 |
| Released | 2026-09-22 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $10 | $0.32 |
| Results tracked | 22 | 43 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 59.6 (#11), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1453 | 1268 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1692 | — |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 33.5 (#49), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 28% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 49.3 (#34), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1420 | 1257 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 90.1% | — |
| CritPt | — | 0% |
| EBR-Bench | 53.3% | — |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GPT-6 Sol leads
GPT-6 Sol: 35.8 (#135), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1404 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 43.0 (#87), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| Vectara Hallucination Rate | 6.5% | 4.1% |
| LMArena Expert | 1449 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#9), Llama-3.3-70B-Instruct: —
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 51.0 (#113), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1389 | 1236 |
| LMArena Chinese | 1416 | 1217 |
| LMArena Russian | 1407 | 1252 |
| LMArena Spanish | 1377 | 1269 |
| LMArena French | — | 1280 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1149 |
| LMArena Korean | — | 1142 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.4 (#97), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1406 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.2 (#100), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1415 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 72.3 (#15), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | GPT-6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1396 | 1274 |
| LMArena Creative Writing | 1392 | 1250 |
| LMArena Multi-Turn | 1405 | 1279 |
| EQ-Bench Creative Writing | 2125 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GPT-6 Sol better than Llama-3.3-70B-Instruct?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 26× 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-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Llama-3.3-70B-Instruct better for coding?
GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 30.6 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-3.3-70B-Instruct share?
14 benchmarks have published results for both models. GPT-6 Sol has 22 scored results on Noometry and Llama-3.3-70B-Instruct has 43.