'Open-source' in AI doesn't mean what it means in software. MIT and Apache 2.0 let you do almost anything; the Llama License requires a separate deal above 700M MAU; old Gemma terms let Google change rules unilaterally (Gemma 4 switched to Apache 2.0). This guide maps what you can and can't do by license type.
DeepReinforce's Ornith 1.5 family, trained with self-improvement RL: the 397B flagship scores 86.0 on SWE-bench Verified, matching Claude Opus 4.8; the 35B-A3B activates only 3B parameters per token yet leads every coding benchmark in its class; the 9B runs on phones. MIT-licensed, fully open-source.
For running Traditional Chinese LLM workloads on Cloudflare Workers AI, the Gemma family follows instructions more reliably than same-tier Llama models. gemma-3-12b-it was marked deprecated on 2026-05-30; the current equivalent is gemma-4-26b-a4b-it: 256K context, Vision, Function calling, at $0.10 / $0.30 per M tokens.
The main on-device LLMs in 2026 are Gemma 3n, Qwen 3.5 Small, Llama 3.2, Phi-4-mini, Ministral 3, and SmolLM3. Sub-3B quantized models can hit 30-50 tokens/sec on phones with 8GB RAM, but RAM, thermal throttling, and context window remain hard constraints.
2026 Q1 saw a full-blown open-source model explosion: on the LLM front, GLM-5, Kimi K2.5, and Qwen3.5 caught up with closed-source models; Embedding and Reranker are dominated by Qwen3 and BGE; speech has Voxtral TTS and Whisper V3; image has FLUX.2; and video has Wan 2.2 rivaling Sora. This is the complete navigation map.
A dynamically composable RAG pipeline built on Cloudflare Workers AI (gemma-3-12b-it + bge-m3): 14 base steps + 6 LangGraph-specific nodes, with three strategy graphs (Baseline / Agentic / Plan-Execute) selected at runtime.