Model comparison
Grok 4.6 vs Nvidia Llama 3.3 Nemotron Super 49b v1.5
Grok 4.6 is the stronger model overall, scoring 56.9 to 40.3 on the Noometry Index. Nvidia Llama 3.3 Nemotron Super 49b v1.5 costs 7.5× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
NVIDIASummary
- They share 12 benchmarks with published results for both. Grok 4.6 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 reasoning, where Grok 4.6 leads 61.4 to 26.8.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 is cheaper at $0.40 / $0.40 per million input/output tokens, against $2 / $6 for Grok 4.6.
- Grok 4.6 accepts more context: 500K tokens versus 131K.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 has downloadable open weights; the other is API-only.
Side by side
| Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 | |
|---|---|---|
| Provider | xAI | NVIDIA |
| Noometry Index | 56.9 | 40.3 |
| Released | 2026-08-12 | 2025-07-25 |
| Weights | Proprietary | Open |
| Context window | 500K | 131K |
| Max output | 500K | 131K |
| Input $ / M tokens | $2 | $0.40 |
| Output $ / M tokens | $6 | $0.40 |
| Results tracked | 49 | 12 |
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Category by category
Coding Grok 4.6 leads
Grok 4.6: 58.5 (#16), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 39.8 (#154)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Coding | 1465 | 1355 |
| DeepSWE | 67.5% | — |
| FrontierCode | 48% | — |
| CursorBench | 41.4% | — |
| LMArena WebDev | 1617 | — |
| FrontierSWE | 25.3% | — |
| SciCode | 56.5% | — |
| WeirdML | 67.3% | — |
| ALE-Bench | 1,508 | — |
Agentic & Tool Use Not comparable
Grok 4.6: 39.4 (#27), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| APEX-Agents | 65.3% | — |
| GDP.pdf | 17.2% | — |
| Vending-Bench 2 | 9,047 | — |
Reasoning Grok 4.6 leads
Grok 4.6: 61.4 (#20), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 26.8 (#128)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Hard Prompts | 1447 | 1336 |
| ARC-AGI-2 | 67.1% | — |
| SimpleBench | 75.9% | — |
| NYT Connections (extended) | 80% | — |
| ARC-AGI-1 | 87.5% | — |
| CritPt | 19.7% | — |
| Chess Puzzles | 40% | — |
| EBR-Bench | 30.5% | — |
| Mystery Game Puzzles | 34% | — |
| DTBench | 97.3% | — |
| LMCA | 48.5% | — |
| Epoch Capabilities Index | 156.44 | — |
Math Grok 4.6 leads
Grok 4.6: 67.0 (#24), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 38.2 (#141)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Math | 1423 | 1392 |
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 31.7% | — |
| OTIS Mock AIME 2024-2025 | 99.2% | — |
| ProofBench | 51% | — |
Knowledge Grok 4.6 leads
Grok 4.6: 63.3 (#20), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 36.7 (#165)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Expert | 1467 | 1330 |
| GPQA Diamond | 94% | — |
| SimpleQA Verified | 49.3% | — |
Multimodal Not comparable
Grok 4.6: 43.6 (#23), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Vision | 1263 | — |
| Blueprint-Bench 2 | 33.2% | — |
| Furniture Assembly | 40% | — |
| LMArena Document | 1452 | — |
Multilingual Grok 4.6 leads
Grok 4.6: 53.0 (#74), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 45.5 (#168)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Non-English | 1420 | 1316 |
| LMArena Japanese | 1376 | 1300 |
| LMArena Russian | 1422 | 1332 |
| LMArena Chinese | 1480 | — |
| LMArena French | 1461 | — |
| LMArena German | 1431 | — |
| LMArena Korean | 1397 | — |
| LMArena Spanish | 1404 | — |
Instruction Following Grok 4.6 leads
Grok 4.6: 75.4 (#63), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 68.6 (#188)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Instruction Following | 1431 | 1299 |
Long Context Grok 4.6 leads
Grok 4.6: 44.5 (#66), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 40.0 (#164)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Longer Query | 1454 | 1315 |
Writing & Preference Grok 4.6 leads
Grok 4.6: 62.3 (#80), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 53.1 (#159)
| Benchmark | Grok 4.6 | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Text | 1428 | 1338 |
| LMArena Creative Writing | 1428 | 1307 |
| LMArena Multi-Turn | 1425 | 1334 |
Frequently asked questions
Is Grok 4.6 better than Nvidia Llama 3.3 Nemotron Super 49b v1.5?
Grok 4.6 is the stronger model overall, scoring 56.9 to 40.3 on the Noometry Index. Nvidia Llama 3.3 Nemotron Super 49b v1.5 costs 7.5× less per token, which makes it the better buy when Grok 4.6's lead doesn't matter for your workload.
Which is cheaper, Grok 4.6 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; Grok 4.6 lists at $2 and $6.
Is Grok 4.6 or Nvidia Llama 3.3 Nemotron Super 49b v1.5 better for coding?
Grok 4.6 scores higher on coding benchmarks: 58.5 versus 39.8 in the Noometry coding category.
Which has the bigger context window?
Grok 4.6 does, with 500K tokens against 131K.
How many benchmarks do Grok 4.6 and Nvidia Llama 3.3 Nemotron Super 49b v1.5 share?
12 benchmarks have published results for both models. Grok 4.6 has 49 scored results on Noometry and Nvidia Llama 3.3 Nemotron Super 49b v1.5 has 12.