Local / Private AI — Your Brain, Your Machine, Your Rules

The open-weight MoE colossus that makes 'run frontier AI on your own iron' feel realistic for the first time. 1.6 trillion parameters (49B active), 1 million tokens of context, and inference efficiency that slashes compute by ~73% versus its predecessor — all under MIT license. The Pro variant chases closed frontier; the Flash variant makes it accessible. DeepSeek didn't just release a model. They released a reminder that the best AI in 2026 might be the one you run yourself.

1.6T Pro (49B active) and 284B Flash (13B active) — both MIT open-weights with 1M context. ~73% FLOPs reduction and ~90% KV cache reduction vs V3.2 at 1M context. API pricing 3-7× cheaper than Claude Opus equivalents. Competitive with GPT-5.4 and Gemini 3.1 Pro on reasoning benchmarks. Huawei Ascend + NVIDIA optimization. Open-source SOTA claims on agentic coding.

Preview release — full independent benchmarks (SWE-Bench Pro, Terminal-Bench) not yet posted by third parties. V4-Pro needs serious hardware (multi-GPU clusters for comfortable speeds). Self-reported numbers — treat with healthy skepticism until independent verification. No native multimodal output. Quantized versions still arriving.

Alibaba's latest 27B dense model doesn't just succeed the previous local AI king — it surpasses their own 397B flagship on every major agentic coding benchmark while running on a single consumer GPU. SWE-bench Verified 77.2, Terminal-Bench 2.0 59.3, native vision and video, Apache 2.0. The local inference turning point.

Beats Qwen3.5-397B-A17B (a 397B MoE model) on SWE-bench Verified (77.2), SWE-bench Pro (53.5), Terminal-Bench 2.0 (59.3), and SkillsBench Avg5 (48.2). GPQA Diamond 87.8. Native multimodal with thinking preservation. r/LocalLLaMA calls it "the biggest release of the year" and "a turning point for local inference."

Similar VRAM profile to predecessor (~17–20 GB in 4-bit); very new so quantized options are still rolling out; thinking mode can be verbose on simpler tasks (toggleable). Not quite closed-model SOTA on the absolute hardest long-horizon agent runs.

Moonshot AI's trillion-parameter open-weight beast — a Mixture-of-Experts colossus that only fires 32 billion parameters per token, yet sweeps agentic coding benchmarks harder than most closed models. Open weights, multimodal input, 256K context, and agent swarms that coordinate hundreds of sub-agents. The frontier just went open.

SWE-Bench Pro 58.6 (beats GPT-5.4 and Claude Opus 4.6), Terminal-Bench 66.7, BrowseComp 83.2, HLE-Full with tools 54.0. Artificial Analysis ranks it #4 overall — the highest any open model has ever reached. Multimodal vision input where GLM-5.1 was text-only.

One trillion total parameters means ~600+ GB VRAM even at INT4 — this is not a laptop model. You'll use it via API ($0.95/M input tokens) or self-host on enterprise GPU clusters. Real-world vibe-coding tests show occasional polish gaps. Token usage runs high on long agentic sessions.

Google's answer to 'what if a frontier AI ran on your phone?' Gemma 4 isn't one model — it's a family of four, from a 2-billion-parameter edge model that fits in 1.5 GB of RAM to a 31-billion-parameter dense powerhouse. The E2B and E4B variants bring multimodal intelligence — text, images, and audio — to smartphones, without an internet connection.

E4B scores 42.5% on AIME 2026, doubling the previous generation's 27B model. Full Apache 2.0 license. Native audio input on edge models. 140+ language support. Four distinct sizes covering every deployment scenario from Raspberry Pi to workstation.

Smaller edge models (E2B, E4B) lack the raw reasoning depth of desktop-class models. No video input on the edge variants (only 26B and 31B). Google ecosystem tooling preferred — less out-of-the-box compatibility with non-Google deployment stacks.