MiniMax 2.5 vs Llama 3.1 vs DeepSeek: Local Coding Model Benchmark 2026

A hands-on benchmark comparing MiniMax 2.5, Llama 3.1 (70B and 405B), DeepSeek-R1, and Qwen2.5-Coder 32B on four coding tasks—function generation, bug detection, refactoring, and multi-file context understanding—run locally on dual RTX 3090 GPUs using llama.cpp. Key findings: MiniMax 2.5 is the best all-rounder (1st or 2nd on 3 of 4 tasks, ~17.5 tok/s), Llama 3.1 405B produces the highest code quality but is too slow for interactive use (~7.8 tok/s), DeepSeek-R1 excels at debugging via chain-of-thought but is the slowest (~9.8 tok/s), and Qwen2.5-Coder 32B is the only single-GPU option with the fastest throughput (~36.8 tok/s) at lower quality. Llama 3.1 70B failed two of four tasks, making its quality drop from 405B larger than expected. Detailed VRAM requirements, scoring methodology, and actual model outputs are included.