We benchmark three leading open-source coding models on local hardware to determine the best choice for developer productivity.

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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.

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

MiniMax 2.5 vs Llama 3.1 vs DeepSeek-R1 Comparison

Models Under Test: Versions, Sizes, and Quantizations

Benchmark Methodology: Hardware, Prompts, and Scoring

Analysis: Strengths, Weaknesses, and Surprises

Which Model Should You Choose? Decision Framework