
Grok 4.6 and Grok 4.7 roadmap detailed for August release schedule
Recent updates outline an aggressive release roadmap for xAI's Grok model family, with Grok 4.6 slated for early August as a 1.5-trillion-parameter model featuring improved supervised fine-tuning and reinforcement learning. Grok 4.7 is expected to follow shortly after as a 2.1-trillion-parameter version.
Published by Jin · 2 min read · 8 AUG 2026
The development pace for artificial intelligence models continues to accelerate rapidly, with xAI mapping out its upcoming release schedule for the Grok model family. Following the rollout of Grok 4.5, developers and industry watchers are turning their attention toward the next iterations of the foundational software, specifically Grok 4.6 and Grok 4.7.
The Upcoming Grok 4.6 Release
According to recent roadmaps shared by leadership, Grok 4.6 is targeting a release window around the first week of August. This upcoming version is built on a 1.5-trillion-parameter architecture and incorporates significantly improved supervised fine-tuning (SFT — a training process where models learn from human-labeled demonstration data) alongside advanced reinforcement learning (RL — a training method where models learn by trial and error through reward signals).
These training enhancements are designed to make the model more reliable and capable during complex reasoning and coding tasks. While initial versions established xAI's baseline frontier capabilities, the 4.6 checkpoint focuses heavily on refining instruction-following and reducing errors during multi-step tasks.
Grok 4.7 and Future Architecture
Looking further ahead into August, xAI has already outlined plans for Grok 4.7. Projected to arrive just a few weeks after version 4.6, Grok 4.7 scales the architecture up to a 2.1-trillion-parameter model. Early projections indicate that this larger version will deliver broad improvements in token efficiency and capability while maintaining competitive inference speeds.
This rapid cadence highlights an industry-wide push toward larger parameter counts and deeper integration of domain-specific data. As these models move from private beta environments into broader developer ecosystems, the focus remains on balancing massive scale with practical execution speed for enterprise and developer workflows.
Source — www.facebook.com ↗
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