Models · Compare
Llama-3.3-Nemotron Super 49B v1 vs Gemini 2.5 Pro
Rows highlighted in warm gray are where the models differ. Numbers carry their as-of date and primary source.
Specs
| Field | A: Llama-3.3-Nemotron Super 49B v1 | B: Gemini 2.5 Pro |
|---|---|---|
| Released | 2025-03-18 | 2025-03-25 |
| Developer | NVIDIA | Google DeepMind |
| Openness | Open weights | Proprietary |
| License | NVIDIA Open Model License | Proprietary |
| OSI-approved | no | no |
| Data released | yes | no |
| Training code | no | no |
| Architecture | dense | unknown |
| Total params | 49B | — |
| Active params | — | — |
| Experts | — | — |
| Context window | 131K | 1.0M |
| Attention | skip-attention | unknown |
| Position enc. | rope | unknown |
| Pretraining tokens | 40B | — |
| Post-training | sft, grpo | rlhf |
| Training hardware | H100 | — |
| $/M input | $0.00 | $1.25 |
| $/M output | $0.00 | $10.00 |
| Output tok/sec | 0 | 127.3 |
Benchmarks
Missing scores render as not reported; never inferred. Bold highlights the leader per benchmark.
General reasoning
| MMLU-Pro | 69.8 2026-05-21 | 86.2 2026-05-21 |
| GPQA-Diamond | 66.7 2025-03-18 | — |
Code
| LiveCodeBench | 28.0 2026-05-21 | 80.1 2026-05-21 |
Math
| MATH | 96.6 2025-03-18 | 96.7 2026-05-21 |
| AIME 2024 | 19.3 2026-05-21 | — |
| AIME 2025 | 58.4 2025-03-18 | 87.7 2026-05-21 |
Held-out / arena
| IFEval | 89.2 2025-03-18 | — |
Context · A
NVIDIA's NAS-distilled Llama 3.3 70B aimed at single-data-center-GPU throughput. Uses skip-attention and variable-FFN blocks selected per layer for the quality-versus-FLOPs tradeoff. Released March 18 2025 with the Llama-Nemotron-Post-Training-Dataset-v1 (30M samples) public.
Context · B
The first Gemini release to clearly lead on LMArena Elo and on hard reasoning benchmarks. Native 1M-token context with reported 2M expansion in pipeline.
Llama-3.3-Nemotron Super 49B v1 detail → · Gemini 2.5 Pro detail →