OmniCoder, Crow 9B top 16GB VRAM coding picks

16GB VRAM has become the strategic "Goldilocks" zone where high-precision 9B models offer a superior agentic experience compared to crippled 3-bit quants of 30B+ models. OmniCoder-9B's training on 425k+ trajectories (distilled from Claude 4.6 and GPT-5.4) specifically targets "read-before-write" behaviors and LSP diagnostic awareness, while 9B models at Q8_0 or FP16 allow for 128k+ token context windows essential for repo-wide reasoning and long-running agent loops. Crow-9B (HERETIC) provides a logic-heavy alternative distilled from Claude Opus for architectural decisions. While Qwen3.5-27B remains a "brute force" logic choice, its 16k token context limit at 16GB VRAM makes it less viable for complex agents than these "agent-first" fine-tuned small models.