# SpaceXAI Releases Grok 4.6: A 500K-Context Frontier Model Tuned for Long-Running Agents, Coding, and Knowledge Work > Source: > Published: 2026-08-13 06:18:14+00:00 [SpaceXAI](https://x.ai/) just released [Grok 4.6](https://docs.x.ai/developers/grok-4-6). The release is a post-training upgrade over [Grok 4.5](https://docs.x.ai/developers/grok-4-5) rather than a larger base model. SpaceXAI held the foundation constant and spent the improvement on a longer supplemental training run, regenerated supervised fine-tuning trajectories, and reinforcement learning in agentic environments. Agents that stay on a task across many steps without drifting. Grok 4.6 scores 61 on the Artificial Analysis Intelligence Index, up five points from Grok 4.5 and tied with GPT-5.6 Sol Max. The model takes 500,000 context tokens, is live today in [Cursor](https://cursor.com/) and [Grok Build](https://docs.x.ai/build/overview), and adds a new `xhigh` reasoning-effort level above the ladder Grok 4.5 shipped with. **Is it deployable?** **Yes, in production, with a bounded set of workloads.** The model is generally available through the [xAI API](https://console.x.ai/) as `grok-4.6` , is the default model in [Grok Build](https://docs.x.ai/build/overview), ships in [Cursor](https://cursor.com/) on all plans, and is routable via OpenRouter, Vercel, and Cloudflare. There is no open-weights release and no self-hosting path, so air-gapped deployments are out. **Company stage**: Seed-stage teams and indie developers can adopt it immediately, since Cursor and Grok Build need no harness work. Mid-market engineering orgs are the strongest fit: API-only integration, mTLS authentication, batch and priority processing are documented. Regulated enterprises should stage a pilot first — the vendor’s brand history is a live procurement question in several buying committees.**Industries**: Software and developer tooling, semiconductor and kernel engineering, hardware and CAD-adjacent design, financial research, and legal analysis. The training mix explicitly targeted several of these.**Applications**: Repository-wide refactors, migration agents, research-and-synthesis pipelines over 500K-token corpora, first-pass application scaffolding from a product brief, GPU kernel optimization, and document-heavy knowledge work. ## What actually changed Grok 4.6 is not a larger base model. SpaceXAI describes a longer supplemental training run than Grok 4.5 received, using curated model-generated data for reasoning and advanced technical concepts, high-quality engineering data, and an improved optimizer and training recipe. Grok 4.5 was then used to regenerate supervised fine-tuning trajectories across reasoning-effort levels, agent harnesses, and domains spanning STEM, software engineering, and knowledge work, with problematic traces filtered by model-based checks. Reinforcement learning followed in agentic environments covering knowledge work, general coding, web development, computer-aided design, and kernel optimization. The behavioral insight is an important one: on longer trajectories, SpaceXAI reports more self-testing and verification, with the model checking its own work before moving on. That is a vendor observation from internal testing, not an independently measured result. The model takes 500,000 context tokens, accepts text and image input with text-only output, has no stated text output limit, and carries a February 1, 2026 knowledge cutoff. `reasoning_effort` now supports `low` , `medium` , `high` (default), and a new `xhigh` level. SpaceXAI did not publish a parameter count for Grok 4.6. **Benchmarks: read the losses first** On xAI’s launch table, Grok 4.6 (High) scores **61** on the Artificial Analysis Intelligence Index, up from 56 for Grok 4.5 and tied with GPT-5.6 Sol Max. It leads the table on GDPval-AA v2 (1753 Elo, versus 1526 for Grok 4.5), AA-Briefcase (1577, versus 1313), and Harvey LAB. It trails on the coding rows that matter most to engineering teams. DeepSWE v1.1 lands at 65.9%, up 11.9 points generationally but behind GPT-5.6 Sol Max at 73%. Terminal-Bench v3.0 reaches 26%, nearly double Grok 4.5’s 15.7% and still last of the four listed models. CursorBench v3.2 is 69.9%, FrontierCode v1.1 Extended is 61.3%, and APEX-Agents is 57.5%. Two things to note while evaluating. First, the table’s bolded wins on GDPval-AA v2 and AA-Briefcase sit inside [Artificial Analysis](https://artificialanalysis.ai/)‘ published confidence intervals — they are statistical ties, not leads. Second, the comparison set excludes Anthropic’s Claude Opus 5, which currently tops that index. The disclosed losses are the more reliable signal. **Pricing and access** Per the [release notes](https://docs.x.ai/developers/release-notes), Grok 4.6 bills $2 / $0.50 / $6 per 1M tokens (input / cached input / output) below 200K prompt tokens, and $4 / $1 / $12 above that threshold. The launch page also references a faster variant at double the price, with no separate model ID published. Grok Build and Cursor are offering 2× included usage for the first week. Teams should set a `prompt_cache_key` (or the `x-grok-conv-id` header on Chat Completions). Without it, requests scatter across servers and cache hits become unreliable, so full input price applies. **Interactive explainer** ## Key Takeaways - Grok 4.6 is a post-training upgrade on Grok 4.5, not a bigger base model. - 500K context, text and image input, and a new `xhigh` reasoning-effort level. - Ties GPT-5.6 Sol Max at 61 on the AA Intelligence Index; trails on DeepSWE and Terminal-Bench. - Same $2 / $6 headline price, with rates doubling above 200K prompt tokens. - Available today via API, Cursor, and Grok Build — no open weights, no self-hosting. Check out the ** FULL TECHNICAL DETAILS here**. **Also, feel free to follow us on** **and don’t forget to join our**[Twitter](https://x.com/intent/follow?screen_name=marktechpost) **and Subscribe to** [150k+ML SubReddit](https://www.reddit.com/r/machinelearningnews/)**. Wait! are you on telegram?** [our Newsletter](https://magic.beehiiv.com/v1/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email={{email}}) [now you can join us on telegram as well.](https://t.me/machinelearningresearchnews)Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? [Connect with us](https://forms.gle/wbash1wF6efRj8G58) Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.