Model comparison
GPT-6 Sol vs Nvidia Llama 3.3 Nemotron Super 49b v1.5
GPT-6 Sol is the stronger model overall, scoring 61.8 to 40.3 on the Noometry Index. Nvidia Llama 3.3 Nemotron Super 49b v1.5 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 . 12 shared benchmarks.
NVIDIASummary
- They share 12 benchmarks with published results for both. GPT-6 Sol scores higher in 8 categories and Nvidia Llama 3.3 Nemotron Super 49b v1.5 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 38.2.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 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 131K.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 | |
|---|---|---|
| Provider | OpenAI | NVIDIA |
| Noometry Index | 61.8 | 40.3 |
| Released | 2026-09-22 | 2025-07-25 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 131K |
| Input $ / M tokens | $2 | $0.40 |
| Output $ / M tokens | $10 | $0.40 |
| Results tracked | 45 | 12 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 39.8 (#154)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Coding | 1447 | 1355 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| ALE-Bench | 2,462 | — |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 26.8 (#128)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1336 |
| 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% | — |
| DTBench | 97.3% | — |
| LMCA | 59.1% | — |
| Epoch Capabilities Index | 162.72 | — |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 38.2 (#141)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Math | 1402 | 1392 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 36.7 (#165)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Expert | 1439 | 1330 |
| GPQA Diamond | 94.3% | — |
| SimpleQA Verified | 60.7% | — |
| Vectara Hallucination Rate | 6.5% | — |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 50.5 (#118), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 45.5 (#168)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Non-English | 1385 | 1316 |
| LMArena Japanese | 1385 | 1300 |
| LMArena Russian | 1401 | 1332 |
| LMArena Chinese | 1405 | — |
| LMArena French | 1410 | — |
| LMArena German | 1390 | — |
| LMArena Korean | 1341 | — |
| LMArena Spanish | 1384 | — |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 68.6 (#188)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Instruction Following | 1412 | 1299 |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 40.0 (#164)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Longer Query | 1411 | 1315 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 53.1 (#159)
| Benchmark | GPT-6 Sol | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Text | 1395 | 1338 |
| LMArena Creative Writing | 1378 | 1307 |
| LMArena Multi-Turn | 1412 | 1334 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Nvidia Llama 3.3 Nemotron Super 49b v1.5?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 40.3 on the Noometry Index. Nvidia Llama 3.3 Nemotron Super 49b v1.5 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 Nvidia Llama 3.3 Nemotron Super 49b v1.5?
Nvidia Llama 3.3 Nemotron Super 49b v1.5 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 Nvidia Llama 3.3 Nemotron Super 49b v1.5 better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 39.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6 Sol and Nvidia Llama 3.3 Nemotron Super 49b v1.5 share?
12 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Nvidia Llama 3.3 Nemotron Super 49b v1.5 has 12.