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Three weeks away and the leaderboard reshuffled. Kimi K2.6 brings 1T parameters under open weights, Qwen 3.6 stays the consumer GPU king, and DeepSeek V4-Flash proves too hungry for single-card setups.
DeepSeek returns with a 1.6T MoE monster under MIT license, Gemma 4's 31B dense model climbs to #3 on Arena AI, and ICLR 2026 papers point to what's next for local inference.
DeepSeek V4 Pro approaches frontier-level performance. Google, Mistral, and Alibaba ship under Apache 2.0. Ollama hits 52 million monthly downloads.
Qwen3.6-27B scores 77.2% on SWE-Bench Verified with a dense architecture that fits on a single RTX 4090. The MoE efficiency narrative just got complicated.
Alibaba drops Qwen3.6-35B-A3B with 73.4% on SWE-Bench Verified and Apache 2.0 licensing. The 3-billion active parameter class now has three serious contenders.
Google gives Gemma 4 a real open-source license. Mozilla launches Thunderbolt for self-hosted enterprise AI. Arcee AI trains a 400B reasoning model for $20 million. And Milla Jovovich broke GitHub.
Google, Alibaba, Meta, Mistral, OpenAI, and Zhipu all ship competitive open-weight models under permissive licenses. The battleground shifts from benchmarks to inference speed on your actual GPU.
Alibaba's Qwen 3.6 Plus ships the first truly agentic open model. Google finally picks a real license. And OpenAI's Sora shutdown proves closed-source video generation can't pay the bills.
Google's Gemma 4 lands with Apache 2.0 licensing and benchmark-topping scores. But a nasty inference speed problem means Qwen still wins on your actual hardware.
Forget the marketing - here's how the latest open-weight models actually perform on your GPU, from 8GB budget cards to 24GB workstations.
