{"id":1980,"date":"2026-08-13T04:38:39","date_gmt":"2026-08-13T11:38:39","guid":{"rendered":"https:\/\/www.five.reviews\/?p=1980"},"modified":"2026-08-13T04:38:40","modified_gmt":"2026-08-13T11:38:40","slug":"grok-4-6-features-benchmark-and-pricing","status":"publish","type":"post","link":"https:\/\/www.five.reviews\/ai-tools\/grok-4-6-features-benchmark-and-pricing\/","title":{"rendered":"Grok 4.6 AI Model: Features, Benchmarks & Pricing"},"content":{"rendered":"\n

xAI released Grok 4.6<\/a> on August 12, 2026, positioning it as a frontier model designed for long-running AI agents, coding work, knowledge tasks, and interactive and visual projects. The model represents a targeted upgrade focused on multi-step agentic workflows rather than a fundamental architectural shift.<\/p>\n\n\n\n

Grok 4.6’s published benchmark results place it among leading frontier models, though the data is mixed. It ties GPT-5.6<\/a> Sol on the Artificial Analysis Intelligence Index but trails Fable 5<\/a> on several evaluations. Against Grok 4.5, the improvement is substantial and consistent across reported benchmarks.<\/p>\n\n\n\n

This article covers the model’s features, training approach, detailed benchmark analysis, pricing, where to access it, practical use cases, and a balanced assessment of when Grok 4.6 makes sense to use versus alternatives.<\/p>\n\n\n\n

Grok 4.6 at a Glance<\/strong><\/h2>\n\n\n\n
Specification<\/strong><\/td>Grok 4.6<\/strong><\/td><\/tr>
Developer<\/td>xAI<\/td><\/tr>
Release date<\/td>August 12, 2026<\/td><\/tr>
Primary focus<\/td>Long-running agents, coding, knowledge work, interactive and visual work<\/td><\/tr>
Context window<\/td>500K tokens<\/td><\/tr>
API input price<\/td>$2 \/ 1M tokens<\/td><\/tr>
API output price<\/td>$6 \/ 1M tokens<\/td><\/tr>
Fast variant<\/td>2x standard price<\/td><\/tr>
Available through<\/td>Cursor, Grok Build, xAI API, OpenRouter, Vercel, Cloudflare<\/td><\/tr>
Launch promotion<\/td>2x included usage in Cursor and Grok Build (first week)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Key takeaway: Grok 4.6 is xAI’s post-training-focused upgrade to Grok 4.5, built on the same 1.5 trillion parameter foundation with improved supervised fine-tuning and reinforcement learning. It competes directly with GPT-5.6 Sol and Fable 5 across agentic and coding tasks, with mixed but competitive benchmark results.<\/p>\n\n\n\n

What Is Grok 4.6?<\/strong><\/h2>\n\n\n\n
\"What<\/figure>\n\n\n\n

Grok 4.6 is xAI’s latest frontier AI model, built on the same 1.5 trillion parameter V9 foundation that powers Grok 4.5. Rather than scaling the base model larger, xAI invested in a longer supplemental training run and improved post-training techniques, particularly supervised fine-tuning and reinforcement learning.<\/p>\n\n\n\n

The model is designed to handle tasks that require multiple steps and sustained focus. According to xAI, it excels at researching topics, analyzing information, working across unfamiliar codebases, and turning product ideas into polished applications. The model features 500K context window length and supports both text and image inputs.<\/p>\n\n\n\n

Grok 4.6 Features<\/strong><\/h2>\n\n\n\n

Long-Running AI Agents<\/strong><\/h3>\n\n\n\n

xAI positions Grok 4.6 for workflows that span many steps and iterations. Unlike single-turn interactions, these agentic tasks might involve researching a topic, planning an implementation, coding a solution, testing the result, and refining based on feedback. Cursor and other tools can use Grok 4.6 to maintain state across these extended interactions.<\/p>\n\n\n\n

The focus is on practical multi-step work: research and information synthesis, codebase navigation and modification, application development from concept to working prototype, and iterative refinement in response to user input.<\/p>\n\n\n\n

Agentic Coding and Software Engineering<\/strong><\/h3>\n\n\n\n

Grok 4.6 has been specifically trained on agentic reinforcement learning tasks covering software engineering. According to xAI, training included general coding work, repository-scale modifications, web development, kernel optimization, and computer-aided design environments.<\/p>\n\n\n\n

The benchmark improvements over Grok 4.5 on coding-focused evaluations (particularly DeepSWE and FrontierCode) reflect this training direction.<\/p>\n\n\n\n

Interactive and Visual Application Work<\/strong><\/h3>\n\n\n\n

According to xAI, Grok 4.6 produces stronger first passes on visual and interactive projects compared with Grok 4.5. This means the model can establish visual structure and interaction patterns for an application in a single response, rather than requiring multiple rounds of revision.<\/p>\n\n\n\n

Cursor’s official announcement notes the model is “especially useful for projects where the fastest route to a good result was to begin with something substantial and then iterate in the loop.”<\/p>\n\n\n\n

Self-Testing and Verification<\/strong><\/h3>\n\n\n\n

xAI observed that on longer trajectories, Grok 4.6 demonstrates more self-testing and verification behavior. The model checks its own work before moving forward, a capability that could reduce the need for manual review in certain agentic workflows. This observation comes from xAI’s testing and should be understood as an architectural strength rather than a guarantee that self-testing will occur in every context.<\/p>\n\n\n\n

How Was Grok 4.6 Trained?<\/strong><\/h2>\n\n\n\n

Grok 4.6 underwent a longer supplemental training run than Grok 4.5, with three primary components driving improvement.<\/p>\n\n\n\n

First, curated model-generated data targeted reasoning and advanced technical concepts. This data was created by Grok models and filtered for quality and correctness.<\/p>\n\n\n\n

Second, xAI incorporated high-quality engineering datasets reflecting real software development workflows. This continues Grok’s coding-focused training diet established in earlier versions.<\/p>\n\n\n\n

Third, an improved optimizer and training recipe strengthened the foundation for subsequent stages. xAI regenerated supervised fine-tuning trajectories across reasoning efforts, agent harnesses, and domains including STEM, software engineering, and knowledge work, filtering problematic traces with model-based checks.<\/p>\n\n\n\n

Grok 4.6 was then trained across a wide range of agentic reinforcement learning tasks. According to xAI, these include knowledge work, general coding, kernel optimization, web development, computer-aided design, and other environments designed to teach the model how to persist across multi-step problems.<\/p>\n\n\n\n

Supervised fine-tuning (SFT) teaches the model correct responses on curated examples. Reinforcement learning (RL) trains the model to optimize for outcomes the model actually cares about (like solving a coding problem or completing a research task) rather than simply mimicking training data. Agentic RL adds the dimension of tasks requiring multiple steps and tool use.<\/p>\n\n\n\n

Grok 4.6 Benchmarks<\/strong><\/h2>\n\n\n\n

Grok 4.6’s performance across published benchmarks reveals a competitive but mixed picture. Here are the official results reported by xAI:<\/p>\n\n\n\n

Benchmark<\/strong><\/td>Grok 4.6<\/strong><\/td>Grok 4.5<\/strong><\/td>GPT-5.6 Sol Max<\/strong><\/td>Fable 5 Max<\/strong><\/td><\/tr>
Artificial Analysis Intelligence Index<\/strong><\/td>61<\/td>56<\/td>61<\/td>62<\/td><\/tr>
GDPVal-AA v2<\/td>1,753<\/td>1,526<\/td>1,728<\/td>1,741<\/td><\/tr>
CursorBench v3.2<\/td>69.9%<\/td>66.7%<\/td>67.2%<\/td>70.5%<\/td><\/tr>
DeepSWE v1.1<\/td>65.9%<\/td>54%<\/td>73%<\/td>70%<\/td><\/tr>
FrontierCode v1.1 Extended<\/td>61.3%<\/td>56.6%<\/td>60.6%<\/td>63.6%<\/td><\/tr>
APEX-Agents<\/td>57.5%<\/td>47.1%<\/td>56.7%<\/td>59.2%<\/td><\/tr>
Terminal-Bench v3.0<\/td>26%<\/td>15.7%<\/td>34.6%<\/td>34.1%<\/td><\/tr>
APEX-SWE<\/td>56.4%<\/td>53.6%<\/td>\u2014<\/td>58.8%<\/td><\/tr>
AA-Briefcase<\/td>1,577<\/td>1,313<\/td>1,502<\/td>1,574<\/td><\/tr>
Harvey LAB<\/td>15.8%<\/td>12.9%<\/td>2.5%<\/td>11.3%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

What the Results Actually Mean<\/strong><\/h3>\n\n\n\n

Grok 4.6 improves over Grok 4.5 on every reported benchmark, with the largest gains on agent-focused evaluations. The 10.4-point jump on APEX-Agents and 11.9-point gain on DeepSWE represent meaningful progress.<\/p>\n\n\n\n

Against GPT-5.6 Sol, Grok 4.6 ties on the headline Artificial Analysis Intelligence Index (a composite of nine benchmarks) but shows mixed performance on specific tasks. It leads on knowledge work benchmarks (GDPVal-AA, AA-Briefcase, Harvey LAB) but trails significantly on DeepSWE and Terminal-Bench, where GPT-5.6 Sol scores 73% and 34.6% respectively versus Grok’s 65.9% and 26%.<\/p>\n\n\n\n

Against Fable 5, Grok 4.6 trails on the Intelligence Index and on several task-specific benchmarks. Fable 5 leads on CursorBench, DeepSWE, FrontierCode, and APEX-Agents. Grok 4.6 performs better on knowledge work tasks and Harvey LAB.<\/p>\n\n\n\n

The practical implication: Grok 4.6 belongs in serious agentic model evaluations, but benchmark results do not establish universal leadership. Performance varies by task category. These are vendor-reported launch results using each company’s preferred benchmark harnesses and reasoning settings, so independent verification over time remains valuable.<\/p>\n\n\n\n

Grok 4.6 vs Grok 4.5<\/strong><\/h2>\n\n\n\n

Grok 4.6 represents a meaningful upgrade over Grok 4.5 across the board. Here’s the generational comparison:<\/p>\n\n\n\n

Area<\/strong><\/td>Grok 4.5<\/strong><\/td>Grok 4.6<\/strong><\/td>Improvement<\/strong><\/td><\/tr>
AA Intelligence Index<\/td>56<\/td>61<\/td>+5 points<\/td><\/tr>
Agentic performance (APEX-Agents)<\/td>47.1%<\/td>57.5%<\/td>+10.4%<\/td><\/tr>
Repository-scale coding (DeepSWE)<\/td>54%<\/td>65.9%<\/td>+11.9%<\/td><\/tr>
Interactive projects<\/td>Baseline<\/td>Stronger<\/td>xAI reports stronger first passes<\/td><\/tr>
Visual work<\/td>Baseline<\/td>Stronger<\/td>Improved structure and language<\/td><\/tr>
Self-testing on long tasks<\/td>Occasional<\/td>More frequent<\/td>Observed improvement<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Is Grok 4.6 a major upgrade over Grok 4.5? Yes, based on the published benchmarks and xAI’s specific focus areas. The improvement is especially pronounced on agentic and repository-scale coding tasks, which aligns with xAI’s stated training emphasis. The model also reportedly handles interactive and visual work better, though this claim rests on xAI’s internal testing rather than published third-party benchmarks.<\/p>\n\n\n\n

However, the upgrade is focused. Grok 4.6 is not a fundamental architectural breakthrough, but a meaningful post-training improvement on a stable 1.5T parameter foundation.<\/p>\n\n\n\n

Grok 4.6 vs GPT-5.6 and Fable 5<\/strong><\/h2>\n\n\n\n

Grok 4.6 is highly competitive with leading frontier models but does not dominate all categories. Here’s how the three compare:<\/p>\n\n\n\n

Metric<\/strong><\/td>Grok 4.6<\/strong><\/td>GPT-5.6 Sol Max<\/strong><\/td>Fable 5 Max<\/strong><\/td>Leader<\/strong><\/td><\/tr>
AA Intelligence Index<\/td>61<\/td>61<\/td>62<\/td>Fable 5<\/td><\/tr>
Knowledge work (GDPVal-AA)<\/td>1,753<\/td>1,728<\/td>1,741<\/td>Grok 4.6<\/td><\/tr>
Coding breadth (CursorBench)<\/td>69.9%<\/td>67.2%<\/td>70.5%<\/td>Fable 5<\/td><\/tr>
Repository work (DeepSWE)<\/td>65.9%<\/td>73%<\/td>70%<\/td>GPT-5.6 Sol<\/td><\/tr>
Agentic performance (APEX-Agents)<\/td>57.5%<\/td>56.7%<\/td>59.2%<\/td>Fable 5<\/td><\/tr>
Knowledge work (Harvey LAB)<\/td>15.8%<\/td>2.5%<\/td>11.3%<\/td>Grok 4.6<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Against GPT-5.6 Sol<\/strong>: Grok 4.6 ties on the composite intelligence index but shows a pattern of strength in knowledge work and moderate weakness in repository-scale coding. GPT-5.6 Sol maintains clear leads on DeepSWE and Terminal-Bench.<\/p>\n\n\n\n

Against Fable 5<\/strong>: Fable 5 leads the headline intelligence index and performs better on most coding and agentic benchmarks. Grok 4.6 outperforms on specific knowledge work evaluations (GDPVal-AA and Harvey LAB).<\/p>\n\n\n\n

Practical interpretation<\/strong>: If your primary need is repository-scale coding modifications, GPT-5.6 Sol benchmarks higher. If your priority is coding breadth or general agentic work, Fable 5 shows an edge. If you emphasize knowledge work and professional reasoning tasks, Grok 4.6 is competitive or leading. The “best” model depends on your specific workflow.<\/p>\n\n\n\n

Is Grok 4.6 Good for Coding?<\/strong><\/h2>\n\n\n\n

Yes, with nuance. Grok 4.6 was specifically trained for agentic coding tasks and shows meaningful improvements over Grok 4.5 on multiple coding benchmarks.<\/p>\n\n\n\n

Where it performs well<\/strong>: CursorBench (69.9%, just behind Fable 5), FrontierCode (61.3%), APEX-SWE (56.4%), and general coding workflows. The APEX-Agents benchmark (57.5%) reflects coding agent performance across multiple steps.<\/p>\n\n\n\n

Where it trails<\/strong>: Repository-scale coding modifications (DeepSWE at 65.9% versus GPT-5.6 Sol at 73%) and terminal-based development (Terminal-Bench at 26% versus competitors at 34-35%).<\/p>\n\n\n\n

Best for<\/strong>:<\/p>\n\n\n\n