Side-by-side analysis of Meta Llama 4 Maverick, Nvidia Llama 3 3 Nemotron Super 49b V1 5 across performance, benchmarks, capabilities, and infrastructure requirements.
Source: inferbase.ai
Side-by-side analysis of Meta Llama 4 Maverick, Nvidia Llama 3 3 Nemotron Super 49b V1 5 across performance, benchmarks, capabilities, and infrastructure requirements.
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages.
Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. It’s post-trained for agentic workflows (RAG, tool calling) via SFT across math, code, science, and multi-turn chat, followed by multiple RL stages; Reward-aware Preference Optimization (RPO) for alignment, RL with Verifiable Rewards (RLVR) for step-wise reasoning, and iterative DPO to refine tool-use behavior.
| Specification | Llama 4 Maverick | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| Provider | Meta AI | NVIDIA |
| Parameters | 400B | 49B |
| Context window | 1049K | 131K |
| Max output | 16K | — |
| Input modalities | text, image | text |
| Output modalities | text | text |
| License | llama-3.1 | other |
| Model type | vision | chat |
| Capability | Llama 4 Maverick | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| code_generation | Yes | — |
| function_calling | Yes | Yes |
| json_mode | Yes | Yes |
| reasoning | Yes | Yes |
| streaming | Yes | Yes |
| text_generation | Yes | Yes |
| vision | Yes | — |
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