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Mistral releases its largest model open-weight: 1 trillion total parameters, ~49B active per token (52B including embeddings and output), natively multimodal, up to 1M-token context, under the Mistral Ecosystem Open-Weight License (a modified MIT — a Model-as-a-Service business needs a separate commercial license).
A new reference-free benchmark: 273 tasks and 2,331 expert-authored acceptance criteria, curated from 37,463 deduplicated queries across 26,314 real user coding sessions; two tracks (100 multimodal editing tasks, 173 repository editing tasks) and a judge-then-refute protocol that evaluates the running application with browser evidence.
Per Gartner's report 'Open-Weight Models Will Run 90% of Enterprise Agentic Workflow Steps', 9 of 10 steps in enterprise agentic workflows are routine: open-weight models handle them at parity for a quarter of the cheapest closed model's price — as little as a twentieth versus a frontier model on every step. Planning assumption: by 2030, 90% of operational steps run on open-weight.
LiquidAI puts the post-trained LFM3 checkpoint in trending: 1.73B parameters, tri-hybrid architecture, explicit agentic post-training (harnesses like Hermes Agent), 256K-token context, 16 languages including Italian, ~1GB of RAM at 4-bit, LFM Open License v1.0.
OpenMobile-2 opens up mobile agent training environments and recipes, and LFM3-1.7B shows a 1.7GB agent already runs on your phone: numbers, minimum requirements and honest limits.
The engine that runs models inside Chrome and YouTube is now open source: architecture, benchmark numbers, and an honest comparison with Ollama and llama.cpp.
74B parameters, 9B active: NVIDIA's MoE for local agentic work fits 24GB of VRAM via IQ2 quantization. Real VRAM, declared benchmarks, and honest limits.
DeepSeek's MoE model with 552B total parameters and 8-16B active per token. 1M-token context, MIT license: free commercial use. Built for online serving, too large for local use on a single PC.
Frontier-class open model with 131K-token context. Needs 48-80GB VRAM, a Mac with unified memory, or a multi-GPU setup.
Bespoke Labs' 9B decision model: it runs entirely locally via Ollama 0.35 and answers typed questions (true/false, numeric scale, multiple choice) through the new systemone API. Built for fast classification in automated pipelines, no cloud.
Terminal-free desktop app for downloading and running GGUF models: GUI, built-in model search and a local server. The simplest way to get started.
Meta's largest open model: closed-frontier quality but datacenter requirements. In practice it needs high-VRAM multi-GPU or the cloud.
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