Grok 4.6 scores 61 points in SpaceXAI’s launch comparison for the Artificial Analysis Intelligence Index, tying GPT-5.6 Sol Max and finishing one point behind Claude Fable 5 Max. Released on August 12, 2026, the model’s pitch is not “I win every benchmark”; it is a more practical combination of frontier-level results, lower standard API rates, and reported efficiency on long-running agent tasks.
That distinction matters. Grok 4.6 looks like a serious model to evaluate for document-heavy work and multi-step workflows, but the coding results do not support calling it the overall leader.
The Grok 4.6 benchmark score—and the caveat behind it
The headline result is 61 points in SpaceXAI’s launch table. GPT-5.6 Sol Max also scores 61, while Claude Fable 5 Max reaches 62. Those figures belong to that specific launch comparison and should not be treated as a permanent global ranking.
A separate model-page snapshot using the v4.3 dataset lists Grok 4.6 at 44.405. That is a different evaluation snapshot, not a replacement value to blend with the 61-point launch result. The same warning applies to Terminal-Bench: the launch table uses version 3.0, while another snapshot reports version 2.1. Benchmark names alone are not enough; the version and evaluation setup travel with the score.
The model itself is a proprietary reasoning system. Its reported context window is 500,000 tokens, with text and image input and text output. Its parameter count has not been disclosed, and its weights are not publicly available.
Grok 4.6, GPT-5.6 Sol Max and Claude Fable 5 Max
| Benchmark | Grok 4.6 | GPT-5.6 Sol Max | Claude Fable 5 Max |
| Artificial Analysis Intelligence Index | 61 | 61 | 62 |
| GDPVal-AA v2 | 1,753 | 1,728 | 1,741 |
| CursorBench v3.2 | 69.9% | 67.2% | 70.5% |
| DeepSWE v1.1 | 65.9% | 73% | 70% |
| Terminal-Bench v3.0 | 26% | 34.6% | 34.1% |
| AA-Briefcase | 1,577 | 1,502 | 1,574 |
Where Grok 4.6 wins, and where it falls behind
The launch results describe a mixed profile rather than a clean sweep. Grok 4.6 leads the three-model comparison on GDPVal-AA v2, a professional knowledge-work evaluation, with 1,753 points. It also edges GPT-5.6 Sol Max on CursorBench v3.2, although Claude Fable 5 Max is ahead there at 70.5%.
The picture changes on software-engineering and terminal work. Grok 4.6 posts 65.9% on DeepSWE v1.1, behind GPT-5.6 Sol Max at 73% and Claude Fable 5 Max at 70%. On Terminal-Bench v3.0, it reaches 26%, compared with 34.6% for GPT-5.6 Sol Max and 34.1% for Claude Fable 5 Max.
So, is Grok 4.6 good at coding? It is competitive, but uneven. The results support using it for some in-editor coding and agentic tasks. They do not support assuming that it will handle every autonomous software-engineering or terminal task better than its rivals.
This is why a single leaderboard position can mislead. A model can be excellent at analyzing documents or coordinating a long workflow while still making more mistakes in a terminal, repository, or complex software project.
The practical advantage: fewer turns on long agent tasks
The most interesting efficiency result concerns AA-Briefcase, a long-running agent evaluation. The AA-Briefcase comparison shows Grok 4.6 completing tasks in about 53 turns and using roughly 500 million input tokens, compared with about 103 turns and 2 billion input tokens for Claude Opus 5 Max.
That is a meaningful workflow signal. Fewer turns can mean less orchestration overhead, fewer opportunities for an agent to drift, and a simpler path through a large task. Lower token use can also matter when a project runs repeatedly.
But this result should stay in its lane. It comes from one benchmark comparison, and it does not prove that Grok 4.6 will cost less for every production workload. Actual usage depends on prompts, tools, retries, model settings, caching, and the provider surface. The useful conclusion is narrower: Grok 4.6 appears particularly interesting for long, multi-step knowledge work and document-heavy agent runs.
The price is compelling—until context gets long
For the US API market, SpaceXAI lists standard pricing of $2 per million input tokens and $6 per million output tokens. Cached input is reported at $0.50 per million tokens.
Those are token rates, not a guaranteed price for completing a task. A workflow that repeatedly calls tools, sends large files, or generates long responses can consume far more tokens than a quick chat. The model’s large context window is useful, but it also makes it easier to send expensive prompts.
Higher rates have been reported for long-context and fast or priority use, but those charges should not be treated as part of the standard $2/$6 rate. For a real deployment, check the billing terms attached to the exact API or partner surface you plan to use.
The clean takeaway is simple: Grok 4.6’s listed entry price is attractive for developers comparing token economics, but the final bill depends on how the workflow uses the model—not just on the headline input rate.
What practical creative-coding tests add
Leaderboard scores cannot show whether an agent produces a usable interactive scene, follows a complex instruction, or gets stuck on one crucial piece of logic. A synchronized creative-coding comparison adds that practical layer, although it uses one-shot prompts rather than a standardized repeated-run audit.
The comparison shows Grok 4.6 producing interactive and 3D creative work alongside Claude Opus 5, Kimi K3, and GPT-5.6 Sol. It also captures a clear failure: Grok 4.6 could not complete the takeoff routine in a space-flight simulation. The result is useful precisely because it cuts both ways. The model can produce visually convincing output while still failing a core functional requirement.
The footage also shows why time, iteration, and output quality belong in the same conversation. A one-shot test rewards the result of a single prompt; real development often involves correction, testing, and several rounds of refinement. Neither type of evidence should be mistaken for the whole story.
Who should consider Grok 4.6 now?
Grok 4.6 makes the most sense for developers and technical teams evaluating:
- long-running research, analysis, and document workflows;
- multi-step agents where turn count and token use matter;
- coding assistance inside supported development environments;
- interactive prototypes and visual experiments, with human review of the result;
- API workloads where the standard $2/$6 input-output rates fit the budget.
SpaceXAI announced access through its API, Cursor, Grok Build, OpenRouter, Vercel, and Cloudflare. Account, partner, and market access can differ, so the relevant surface matters when planning a deployment.
The model is not open source or self-hostable: its weights are not public and its parameter count is undisclosed. That makes the access channel and its terms part of the practical decision, especially for teams that need control over model hosting.
As for Grok 4.7, there is no confirmed release date to plan around. A forecast attributed to Elon Musk pointed to a possible arrival three to four weeks after the cited post, but that remains a forecast rather than a release announcement.
Bottom line: Grok 4.6 is a credible cost-and-workflow competitor, not a universal benchmark champion. Its 61-point launch result, low standard token rates, and reported long-task efficiency make it worth testing for knowledge work and agentic workflows. Its weaker DeepSWE and Terminal-Bench results—and the failed takeoff in a practical coding comparison—are a useful reminder to test the exact job you need done.
