Grok 4.6, SpaceXAI's new model that caught up with the flagships for 2 dollars

What Grok 4.6 is and how it differs from Grok 4.5

On August 12, 2026, SpaceXAI released Grok 4.6. The model builds on the previous Grok 4.5 with a focus on two things, long-running autonomous tasks and more ambitious visual work. In plain terms, it can stay with a complex task across many steps, whether that's researching a topic, analyzing information, working across a large codebase, or turning an idea into a finished application.

The headline number from the announcement looks like this. On the Artificial Analysis Intelligence Index, a composite score that combines results from nine different benchmarks, Grok 4.6 scores 61 and matches GPT-5.6 Sol. Only Claude Fable 5 sits ahead at 62, while the previous Grok 4.5 scored 56.

^{Competitor figures are drawn from the respective developers' published system cards or benchmark leaderboards}

The model belongs to SpaceXAI's 1.5-trillion-parameter family and was developed in collaboration with the team behind Cursor, the well-known code editor. Its training data cutoff is January 2026.

How Grok 4.6 handles coding

Coding is the foundation of the whole model, because an agent that can read, write, and run code can operate almost any digital tool or file. That's why the model's other abilities, office work, engineering, and design, all rest on this same base.

Here's how Grok 4.6 performs on the main coding benchmarks,

• APEX-SWE, which measures integration work and diagnosing failures from monitoring data, 56.4 percent, third place behind Opus 5 at 63.7 and Fable 5 at 58.8

• FrontierCode v1.1, which checks whether real open-source maintainers would accept the model's changes, 61.3 percent, third place

• DeepSWE v1.1, where the model must resolve a real repository issue end to end, 65.9 percent against the leader Opus 5 at 74

• Terminal-Bench 3.0, command line work, 26 percent against 43.5 for Opus 5

As you can see, on raw code quality Grok 4.6 isn't first, it's usually third or fourth. But the percentage alone misses half the picture, because the other half is cost and token usage.

^{CursorBench 3.2 evaluates coding agents on realistic IDE-style tasks drawn from production-like Cursor workflows, multi-file edits, tool use, and iterative fixing}

That chart is where things get interesting. Grok 4.6 lands around 70 percent, right alongside Opus 5, while burning noticeably fewer tokens and costing several times less per task. The model reaches its result in fewer steps, and that efficiency is exactly what turns into real money saved in day-to-day work.

Where Grok 4.6 beats every other model

There are several areas where Grok 4.6 takes first place, and they're fairly unexpected.

The first is real-world engineering. The model was tested on tasks requiring reasoning about geometry, materials, and physical devices rather than code alone. On 3DCodeBench, where the AI builds 3D models through code, Grok 4.6 takes first place at 54 percent, ahead of Opus 5 at 49.9. On CadGenBench, where it must produce a technical part from a description, it also leads at 40.9 percent.

The second area is office and legal work. On OfficeQA Pro, where the model answers workplace questions over real company documents, Grok 4.6 leads at 63.2 percent. And on Harvey's legal benchmark, the result really stands out, 22 percent against 14.2 for Fable 5 and 11.7 for Opus 5.

The third area is helping build AI models themselves. On SpaceXAI's internal benchmark of model-development tasks, Grok 4.6 scores 61.1 percent, ahead of both Opus 5 and GPT-5.6 Sol. On InferenceEval, which requires shipping working changes to the code that serves live users, it also takes first place at 46.9 percent. That paints a curious picture, the model may trail in classic programming while winning where the task leans toward engineering and everyday professional work.

How an early Grok 4.6 version sped itself up

The most telling experiment in the company's report went like this. An early checkpoint of Grok 4.6 was tasked with speeding up its own chat inference. The model was free to experiment but required to verify real end-to-end gains before proposing any change.

Over five hours, it worked through 297 candidate optimizations across different areas, discarded the ones that showed no measurable real-world gain, including several that looked good on intermediate measurements, and ultimately opened seven change proposals. Three of those now serve live Grok Chat traffic, delivering a combined throughput gain of roughly 1.5 percent on response generation and 3.1 percent on request processing.

This is a good example of the point made above. Benchmark percentages show potential, but stories like this show the model can carry long autonomous work through to a concrete result while filtering out false improvements on its own.

Accuracy, search, and safety

On factual accuracy, the picture is mixed. The model makes unsupported claims in 1.7 percent of cases, worse than the previous Grok 4.5 at 0.98 percent, but noticeably better than Opus 4.8 at 3.4 percent. On DeepSearchQA, though, where the model must find an answer through multi-step search and synthesize the results, Grok 4.6 scores 81.6 percent against 38.4 for the previous version, a very large jump.

In cybersecurity, the model improved moderately. The company specifically notes that these capabilities are more useful to defenders, meaning finding and patching vulnerabilities rather than carrying out attacks. The deployed version refuses the large majority of clearly harmful requests.

On resistance to jailbreaks, the progress is clear, the share of successful attempts to trick the model with standard techniques dropped from 0.73 to 0.04 percent. Refusal accuracy on weapons-related topics rose to 100 percent, and the child safety figure stayed at zero, meaning the model doesn't engage with those requests at all. In fairness, one number moved the other way, on an internal test measuring the tendency to abandon the truth under user pressure, the score worsened from 0.67 to 3.8 percent.

How much Grok 4.6 costs and where to try it

This is where the model's main argument lives. Pricing starts at 2 dollars per million input tokens and 6 dollars per million output tokens. There's also a fast variant that runs at twice the price.

For comparison, competitors' flagship models cost significantly more for a difference of just a few points in quality. That's exactly why Grok 4.6 lands in such a favorable spot on price-versus-performance charts.

You can try the model in the Cursor code editor and in the company's own Grok Build tool, where SpaceXAI offered double the included usage for the first week. It's also available through the company's API and third-party platforms including OpenRouter, Vercel, and Cloudflare. The consumer Grok apps for web, mobile, and the X platform are set to get the model later.

Cheaper, Faster, More Practical

Grok 4.6 shows clearly where the entire AI market is heading. The race for the top score is gradually giving way to a different question, who delivers comparable results for noticeably less money and in fewer steps. The model rarely takes first place in pure coding, yet it leads on engineering, office, and legal tasks, and on price it sits in an entirely different weight class.

The takeaway is simple. Picking a model by its overall ranking is nearly pointless, because the leader of a composite table can lose to a cheaper model on your specific task. A one or two point gap between flagships often makes no practical difference, while a threefold price gap is felt immediately.

The problem is that honestly comparing Grok, Claude, GPT, and the rest means setting up access with each company separately, sorting out billing, and constantly switching between services. For a single comparison, that's too much work.

That's exactly what unitool.ai is for. Get one subscription and unlock access to every current AI model at once, so you can run your real task through several of them and pick whichever handles it best. No foreign cards, no separate sign-up on each platform, just hands-on comparison instead of someone else's percentage tables.