A hands-on benchmark comparing three open-weight LLMs — MiniMax 2.5, Llama 3.1 70B, and DeepSeek-V3 — for local JavaScript/React/Node.js coding tasks on an RTX 4090 workstation. Five task categories were evaluated: code generation, bug detection, refactoring, unit test generation, and multi-file context understanding. MiniMax 2.5 scored highest overall (8.6/10 avg) with strong multi-file coherence; DeepSeek-V3 excelled at bug detection and test generation (9/10) but was slowest at 12.1 tok/s; Llama 3.1 70B was fastest (24.7 tok/s) and most memory-efficient but weakest on code quality (6.0 avg). Setup instructions using Ollama with Q4_K_M quantization and partial CPU offloading are included for all three models.
Table of contents
MiniMax 2.5 vs Llama 3.1 vs DeepSeek-V3 ComparisonTable of ContentsWhy Local Coding Models Matter in 2026Benchmark MethodologySetting Up Each Model LocallyBenchmark Results: Code GenerationBenchmark Results: Bug Detection and FixingBenchmark Results: Code RefactoringBenchmark Results: Unit Test GenerationBenchmark Results: Multi-File Context UnderstandingAggregate Benchmark ComparisonImplementation Checklist for Local Coding Model SetupKey Takeaways and Next StepsSort: