Side-by-side analysis of Minimax Minimax M2 5 Free, Nvidia Llama 3 3 Nemotron Super 49b V1 5 across performance, benchmarks, capabilities, and infrastructure requirements.
Source: inferbase.ai
Side-by-side analysis of Minimax Minimax M2 5 Free, Nvidia Llama 3 3 Nemotron Super 49b V1 5 across performance, benchmarks, capabilities, and infrastructure requirements.
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams.
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 | MiniMax M2.5 (free) | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| Provider | Minimax | NVIDIA |
| Parameters | — | 49B |
| Context window | 197K | 131K |
| Max output | 197K | — |
| Input modalities | text | text |
| Output modalities | text | text |
| License | — | other |
| Model type | chat | chat |
| Capability | MiniMax M2.5 (free) | Llama 3.3 Nemotron Super 49B V1.5 |
|---|---|---|
| function_calling | — | Yes |
| json_mode | — | Yes |
| reasoning | — | Yes |
| streaming | — | Yes |
| text_generation | — | Yes |
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