On November 4, 2026, Ricursive released Ripple-65B Preview, a dense 65-billion-parameter open-weight model trained from scratch for agentic coding: 80.5 on Terminal-Bench 2.1, ahead of MoE rivals of 501 to 975 billion parameters, on $32 million of research spend. The scores remain self-reported and uneven outside coding; the interest is first and foremost an efficiency story.
On 15 October 2026, Meta released the weights of Muse Spark Open, a multimodal model with 816 billion total parameters (42 billion active) and a one-million-token context, optimised to run on two Mac Studios with M5 Ultra chips. Before adopting it in production, read the license — open-weight stops at $20 million in service revenue.
Open-weight models accounted for 60% of OpenRouter’s US token consumption in August, most of them Chinese. The marketplace has now made US in-region routing generally available, guaranteeing that requests are decrypted, processed, and served entirely on American soil — or rejected.
On July 31, 2026, DeepSeek upgraded its V4 Flash model through re-post-training alone, with zero architecture changes. The result: the smaller 13B active parameter model now outperforms the larger V4 Pro on nine coding benchmarks.
Moonshot AI released Kimi K3 weights on July 26, 2026 — the largest open-weight model ever distributed, competitive with GPT-5.6 Sol and Claude Fable 5. Here’s what changes for your inference stack.
