Omnicoder-9b SLAPS in Opencode

A new open-source coding model is challenging the dominance of expensive, quota-limited cloud services. Developer Tesslate has released OmniCoder-9B, a specialized fine-tune of the Qwen3.5-9B model trained on coding traces from high-performance models like Claude Opus. Early benchmarks show it running at over 40 tokens per second on consumer hardware with just 8GB of VRAM, while supporting context windows up to 100,000 tokens when quantized to 4-bit precision (Q4_K_M). This performance makes it a viable local alternative for developers frustrated with the recent restrictions and pricing changes from services like GitHub Copilot and Google's Gemini Code Assist.

Users report the model completes complex coding tasks "flawlessly" when integrated with tools like OpenCode and the llama.cpp server. The key breakthrough is achieving agentic coding capabilities—where AI can take multi-step actions—on hardware that previously struggled with larger models. While multi-expert models (MoEs) might offer better quality, their inference speed is significantly slower. OmniCoder-9B's efficiency comes from its targeted training on high-quality coding data and optimized quantization, though users note some bugs like full prompt reprocessing that need ironing out. This release signals a shift toward capable, specialized small models that democratize advanced AI coding tools.