The best open-source large language models (LLMs)

This post provides a technical deep dive into nine leading open-source LLMs, evaluating their architectures, performance benchmarks, and suitability for production deployment on Baseten. It details specific technical innovations within each model, such as DeepSeek's CSA/HCA for KV cache reduction and mHC for training stability, Gemma 4's interleaved attention, GLM-5.3's SAO for asynchronous optimization, GPT OSS 120B's integration with TensorRT-LLM and EAGLE-3 speculative decoding, Kimi K3's AttnRes and KDA/MLA for long context, and MiniMax M3's Sparse Attention. The analysis highlights tradeoffs in context window size, inference speed, memory usage, and cost-effectiveness for different use cases like agentic coding and long-context reasoning.