Artificial Intelligence that is more powerful, safer, and at the same price.
That is exactly how xAI is introducing Grok 4.7, the most advanced model the company has ever released for coding tasks and knowledge work.
If you have been following the AI space for a while, you know that getting all three of those things together is pretty rare.
Usually, when a model gets significantly better, the price goes up with it.
But that is not what is happening here.
Grok 4.7 arrives at the same price and the same speed as Grok 4.6, and it brings three major improvements to the table:
- Superior performance on long and complex tasks, such as coding and professional work that takes hours to complete
- Much more refined self-correction, allowing the model to review its own work with greater precision
- Reinforced cybersecurity, featuring an entirely new stack of safeguards
And the most interesting part?
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On CursorBench 4.0, a test that pushes long-duration coding tasks to the limit, Grok 4.7 is at the frontier when it comes to the price-to-performance ratio.
In the sections below, you will understand what changed on a technical level, what the tests show in practice, and why this release could be a major turning point in the large language model market. 🚀
What changed technically in Grok 4.7
When xAI talks about superior performance on long and complex tasks, it is not just repeating a marketing slogan. The technical improvement here is real and very measurable. Grok 4.7 uses a new base model, larger than the one behind Grok 4.6, and it was trained with a longer reinforcement learning session using a combination of harder tasks. A large portion of that training was specifically aimed at problems that take several hours to complete. This means the model can maintain the context of a conversation or task for much longer without losing coherence.
This gain is especially valuable when you are building complex software, writing extensive documentation, or solving multi-step problems that require the model to remember decisions made early in the session. Anyone who has used language models for professional work knows exactly what we are talking about: that frustrating moment when the model just forgets the context and starts contradicting what it said before. Grok 4.7 was built to significantly reduce that problem, with much more efficient long-context management.
Another technical point worth paying attention to is the refined self-correction. This goes well beyond simply reviewing a response. The model can now identify inconsistencies in its own reasoning, walk back an incorrect assumption, and recalculate its path before delivering the final result. This behavior is called self-verification in the technical artificial intelligence literature, and it is one of the biggest challenges for any large language model. Implementing this efficiently, without dramatically increasing computational cost or response time, is a considerable engineering achievement.
It is also worth noting that xAI trained Grok 4.7 to natively understand the Grok Bot harness, which makes it better at conversational tasks and general knowledge work. In other words, we are not just talking about a more capable model for code, but a more versatile tool for everyday use. For developers and professionals who rely on AI constantly, this is a real game-changer in practice.
What the benchmarks reveal in practice
Numbers speak loudly in the world of machine learning models, and Grok 4.7 has plenty to show. On CursorBench 4.0, focused on software engineering, it hit 46.3%, comfortably beating Grok 4.6 at 40.4% and GPT-5.6 Sol Max at 41.7%. On DeepSWE v1.1, it reached 71.0% in high-effort mode, landing very close to top-tier competitors.
But perhaps the most impressive data point comes from EEBench, the electrical engineering test, where Grok 4.7 scored 64.0%, leaving GPT-5.6 Sol Max far behind at 39.4%. In legal work, on the Harvey Legal Agent Benchmark, the model also shined with 19.6%, while some competitors barely cleared 2.5%. On AA Briefcase v1.1, which simulates hours of office work, Grok 4.7 scored 1,657 points, a clear step up from the previous version at 1,546.
These tests put AI through tasks normally performed by professionals like lawyers, nurses, and financial analysts. And Grok 4.7 improved over Grok 4.6 on benchmarks like GDPval and AA Briefcase, while maintaining performance comparable to other frontier models. From a technical standpoint, this shows the model evolved consistently across multiple fronts, not just in one specific niche.
Cybersecurity as a core priority
The new cybersecurity stack in Grok 4.7 is one of the most important aspects of this release, and possibly the most surprising for anyone who has followed xAI’s track record. The company completely overhauled the model’s safeguards, and the result is that it has become the strongest model xAI has ever tested for refusals and jailbreak resistance. In practice, this means the model now has layers of protection that act at different points of the text generation process, from interpreting the initial prompt all the way to delivering the final response.
In dual-use domains like cybersecurity and biological work, Grok 4.7 leads in both utility for legitimate tasks and safe refusal of dangerous requests. It reached the top of LatchBio’s biosafety benchmark with 62.4%. On HackerBench v0.3, xAI’s test for risky and malicious cyber tasks, the model showed the highest safety, allowing only 3.3% of risky dual-use prompts to pass through, while rarely blocking legitimate security work.
This overhaul also touches on something that has been a central discussion in the artificial intelligence world: how to balance freedom of use with responsibility. Models that are too restrictive end up being useless for legitimate and professional use cases. Models that are too permissive create real risks. xAI went with a safeguard system designed to be contextually intelligent, meaning it can understand the intent behind a request before simply blocking or approving it. This is much harder to implement than it sounds, because it requires the model itself to have a sophisticated understanding of linguistic and situational nuances.
The company has also started giving invite-only access to selected cybersecurity partners, opening up Grok 4.7’s red team capabilities for defense research. For anyone using the tool in a corporate environment or on projects that handle sensitive data, this advancement in cybersecurity is especially relevant. Companies that previously hesitated to integrate AI models into their workflows now have a concrete technical argument to revisit that decision.
Performance and price: the combination the market did not expect
Talking about performance and price in the context of language models is always a tricky conversation, because benchmarks do not always reflect the real-world user experience. But in the case of Grok 4.7, the numbers grab attention in a way that is hard to ignore. The model is priced starting at 2 dollars per million input tokens and 6 dollars per million output tokens, the same rates as Grok 4.6.
For comparison, GPT-5.6 Sol Max costs 4 dollars for input and 20 dollars for output, while Fable 5.1 Max goes as high as 10 dollars for input and 50 dollars for output. In some cases, that represents a cost difference of up to eight times for token generation. For startups, independent developers, and companies working with high volumes of requests, that difference can be the line between a project being financially viable and completely unfeasible.
The fact that xAI kept the Grok 4.6 pricing for the 4.7 release is a pretty clear strategic decision. The machine learning model market is getting more competitive by the day, and maintaining the price while delivering significant improvements is a way to consolidate the existing user base and attract those who were still on the fence. More than generosity, it is a smart market positioning move at a time when the race for adoption is at the center of every major AI company’s strategy.
For those who want extra speed, xAI also offers a fast variant, with double the output speed at double the price. That way, you can choose what makes the most sense for your project: maximum savings or maximum performance.
Where to find Grok 4.7
Grok 4.7 is already available on Cursor and Grok Build. You can also access it through the Grok API, third-party coding harnesses, as well as model routers and cloud platforms. So, whatever your preferred work environment might be, there is probably an easy path to start using the model today.
And here is the detail that might be the most relevant for anyone evaluating which model to use: Grok 4.7 does not need to be the best on every benchmark to be the smartest choice for most use cases. When you combine performance well above average with an accessible price point and a robust security layer, you create a product that serves a huge segment of the market very efficiently. This balance between technical capability and financial accessibility is exactly what xAI needed to position itself as a real player in the race for the future of artificial intelligence. 💡
