Kimi

Kimi K3 triggers the alarm in Silicon Valley

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Kimi K3 has just dismantled an idea that had been installed among analysts for many months: the Chinese laboratories of artificial intelligence (IA) were, at least, between six and eight months behind its American rivals. This model, published last week by Beijing startup Moonshot AI, has 2.8 trillion parameters and has been designed for programming, knowledge work and complex reasoning tasks, as well as incorporating native vision capabilities. In fact, it’s bigger than the latest flagship models launched by Chinese rivals, such as Zhipu AI or DeepSeek. Its performance, and this is what is really surprising, has surpassed that of the main closed models from the USAsuch as Claude Opus 4.8 from Anthropic or GPT-5.5 from OpenAI, leaving only from behind of the most recent Claude Fable 5 and GPT-5.6 Sol, according to Artificial Analysis data. China is only a few weeks behind The most shocking conclusion Ryan Fedasiuk picked it upresearcher at the American Business Institute, in a report published this Saturday under the title “China has caught up with the US in frontier AI.” Fedasiuk maintains that the gap between the two countries, which until now was measured in months, has narrowed to something close to a few weeks. Large American laboratories continue to lead the absolute frontier with Claude Fable 5 and GPT-5.6 Sol And Kimi K3 not only competes in performance. In tests such as the Arena AI ranking for development front-endthis Chinese model has surpassed both GPT-5.6 Sol and Claude Fable 5 despite being noticeably cheaper than both American alternatives. Of course, its price is somewhat higher than other Chinese models, such as DeepSeek or those from Zhipu AI. This combination (a larger, more capable and cheaper model than much of the American competition) is what has triggered concern in Silicon Valley, a location accustomed until now to taking for granted a cushion of several months of advantage. In any case, the large American laboratories continue to lead the absolute frontier with Claude Fable 5 and GPT-5.6 Sol. What has changed, according to the Fedasiuk report, is the speed at which that distance is shortened. Silicon Valley can no longer continue discounting that advantage for months. Now it is measured in weeks. Image | Generated by Xataka with ChatGPT More information | SCMP In Xataka | China has a plan to win the AI ​​war against the US. And DeepSeek is its champion

Kimi K3 forces Trump to resume his plan to stop Chinese AI

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The Trump Administration likes to veto things. Now they seem to want to do it with the AI ​​models of Chinese companies, which are becoming increasingly competitive. The launch of Kimi K3 seems to have been the trigger for this new plan to be activated, but there is a problem: vetoing those models is a terrible idea. This comes from afar. The US Department of Commerce I had already studied last year included several Chinese AI startups, including DeepSeek, in its famous Entity List. With this they wanted to limit the access of these companies to sensitive hardware and technology developed in the US. Companies at risk for using Chinese models. Recently it has even been proposed drafting an executive order to hold US companies responsible for security breaches that appear due to using Chinese models in their systems. The objective was always the same: to discourage the use of these Chinese models as much as possible. Why US companies use Chinese models. The reason is simple: Chinese open weights like DeepSeek V4 or Kimi K3 allow companies to download and run them on their own servers, dramatically reducing inference costs and keeping all data private. Coinbase CEO Brian Armstrong himself has indicated that use models such as GLM-5.2 and Kimi K2.7 in local production, which has allowed them to cut their total spending on AI in half despite the fact that token consumption has skyrocketed. Duopolies without competition. David Sacks, White House AI advisor, posted a message on X on Sunday in which he warned of the risk of using these models: “We are at a critical turning point in AI policies. The leading laboratories with proprietary models, which are already a duopoly in terms of revenue from their AI models, want the government to eliminate Open Source competition.” A Axios report reveals that indeed both OpenAI and Anthropic could have part of the responsibility in promoting this ban. This could be a shot in the foot for the US.. An analysis published in The Washington Post raises an argument worth considering. Treating open models as a security threat is confusing competition with a danger that must be contained. This text recalls how the Sears chain was not allowed to ban Walmart, nor IBM to ban Compaq or Dell, nor traditional airlines to veto the operators that lowered prices. In each case the same thing happened: an established company ran into a rival that was lowering costs, so it had only two options: compete or lose. Linux and Open Source have already shown the way. As the author of the article says, open source eliminated the barriers of commercial software, which locked users into an alternative from which they had no way out. That did not make these companies disappear, but rather boosted competition. Red Hat, MongoDB, Android or Kubernetes showed that “giving away” the product was not incompatible with building profitable businesses around that product. Danger, duopoly. That analysis shows that almost all companies prefer a scenario in which open models remain available. The only ones who have a direct interest in maintaining closed models are precisely Anthropic and OpenAI, because their businesses depend precisely on there being no free (or very cheap) competitive alternatives. If they are so good, why are they afraid? What’s ironic is that if Anthropic and OpenAI really claim to be so far ahead of Chinese AI companies, they shouldn’t have to worry about the competition. Nor would they have to ask the government for help to stop their competition. The US antitrust laws themselves exist precisely to prevent a market from falling into the hands of one or two companies. This veto would precisely allow them to create that monopoly (or duopoly) to lock users and companies into it. In Xataka | A few days after the Kimi K3 “shock”, Alibaba has launched Qwen 3.8: it is the sign that the US has a problem

Even Elon Musk surrenders to the open Chinese AI model Kimi K3. It is not for less

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It’s good, it’s pretty and it’s (quite) cheap. We met him a few days ago, but Kimi K3the new open AI model from the Chinese startup Moonshot AI, is causing a sensation. So much, so much, that they have had to pause new subscriptions because they cannot handle so much demand. Another turning point for Chinese AI. Kimi K3 is the largest open weights AI model ever published, with numbers that probably rival those of the frontier models from Anthropic and OpenAI, which do not provide information on the size of their models. Those 2.8 billion parameters make a difference and are a good part of the reason why this model represents a real leap in quality according to all the benchmarks that are being published. “Awesome”. Elon Musk himself published a single “Impresionante” on his X/Twitter account as answer to the very complete analysis Artificial Analysis performance. Its agentic behavior surpasses that of Opus 4.8 and only Fable 5 surpasses it, but in a specific benchmark it goes even further and is the best of all the models evaluated by this firm, including those from OpenAI and Anthropic. Source: Artificial Analysis. More tests. In programming it is better than Opus 4.8 and GPT-5.5, but inferior to Fable 5 or GPT-5.6, and all the independent tests validate these results: we are facing a model that at least on paper competes directly with the best that both Anthropic and OpenAI had until now. No Chinese model had come so close until now: GLM-5.2, although notable, competed more with GPT-5.5 and Sonnet 5 than with the US frontier models. Source: Artificial Analysis Gigantic… and not so cheap. DeepSeek showed that it was possible to access really capable models at a very affordable price, and recently GLM-5.2 proposed exactly the same: it is possible to achieve 90% capacity of frontier models such as Opus 4.8, but at 20% of the cost. The curious thing is that with Kimi K3 the trend changes: it is a more affordable model than Fable 5 or GPT-5.6, but not as much as one might expect: the cost per million input/output tokens is 3/15 dollars, while in Fable 5 it costs 10/50, Opus 4.8 costs 5/25 and GPT-5.6 Sol costs 5/30. Tokens everywhere. One of the factors that probably influences that quality/price ratio is the large number of tokens that Kimi K3 seems to use when answering. It is a model that “thinks a lot”, and that, although it undoubtedly improves the precision and capacity of the model, also causes it to generate higher bills for the user. Artificial Analysis’ own report goes further: the cost per task in its test battery is $0.95, at the level of GPT-5.6 Sol’s $1.04 and certainly cheaper than Fable 5 ($2.75), but also much more expensive than Grok 4.5 ($0.31) or GLM-5.2 ($0.47). The pelican test. Analyst Simon Willinson was able to test the model to perform a test to evaluate the behavior of all these developments: having the model generate an SVG image of a pelican on a bicycle. In their tests the image was of very good quality, but it generated almost 17,000 tokens for the response with a task cost of 25 cents. It is not that this test is too conclusive, but it does reveal that for a simple task, the result, although outstanding, is not especially efficient in token consumption. Cybersecurity, the unknown. Unlike the latest models from Anthropic or OpenAI, Moonshot AI does not seem interested at the moment in its use in the field of cybersecurity. There is no mention of those potential capabilities in the notes of launch, but that doesn’t mean it doesn’t deliver. Vercel’s CTO, Malte Ubl, explained Although it is not the most advanced of AI models in this area, after running several tests it seemed like a model that can be very useful when finding and correcting vulnerabilities. Demand, through the roof. The expectation generated by this model has been such that the company has announced that pause new subscriptions. This will allow them to be able to deal with all requests to use it without harming the experience for both old and new users. A striking decision that seems to make a reality clear: they cannot cope. In Xataka | The gigantic Qwen 3.8 is another worrying sign for the US: its AI advantage is evaporating

The new Chinese model Kimi K3 is already number one in Frontend Code Arena. And it’s unleashing madness on the Internet

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It seems like yesterday when DeepSeek R1 called into question an idea that many took for granted: that the race for advanced artificial intelligence It still had a clear owner in Silicon Valley. The emergence of the Chinese model helped trigger a massive sale of technology and led NVIDIA to suffer a loss daily capitalization unprecedented until then. As the months passed, that image lost intensity, but the message remained: the Chinese technological ecosystem was not willing to limit itself to keeping pace with the United States. The following notice now has a different name: Kimi K3. Moonshot AI has just presented a model with 2.8 trillion total parameters that, as soon as it arrived, was placed at the top of Frontend Code Arenaahead of some of the most powerful proposals from Anthropic and OpenAI. But the story is not limited to a classification: developers and fans are already using it to create interfaces, games and recreations that anyone can see and, in some cases, try. That’s where this article really begins. It is worth dwelling on the details of that classification. At the time of writing, Kimi K3 reaches 1,679 points in Frontend Code Arena, ahead of Claude Fable 5with 1,631, and GPT-5.6 Sol xHigh, with 1,618. The improvement compared to the previous generation is also striking: Kimi K2.6 was in 18th placewhile his successor leads six of the seven domains evaluated. For now, Arena maintains the label of preliminary result, so it is convenient to read this position as a very significant photograph, but still susceptible to change. We are not facing a universal programming exam, but rather a very specific test. Frontend Code Arena compares web applications created by different models and lets users evaluate which one solves the task better, which one works more reliably, and which one presents a better experience. That approach is especially useful for measuring visible and practical capabilities, but it also has obvious limits. That Kimi K3 leads here tells us a lot about its frontend performance, although it doesn’t automatically allow us to extend that advantage to complex repositories, backend, mathematics, or general reasoning. Outside of this specific terrain, photography remains favorable, although more balanced. Vals AI places Kimi K3 second among 38 models, with 74.70%just behind Claude Fable 5, which reaches 75.14%, and above GPT-5.6 Sol, with 73.12%. Artificial Analysis also places it among the most advanced systems in its classification, with 57 points and third place overall. Where Kimi K3 seems to feel most comfortable is in tasks that combine programming, visual context and several chained steps. Arena supports its ability to build web interfaces, while Vals AI also records high performance in agent programming tests. Moonshot adds that the model can traverse large repositories, use terminal tools, and review screenshots of its own work to correct the output on the fly. That last capability, which the company calls “vision in the loop,” helps explain why it excels at transforming visual references into interactive products. There are also several cautions before interpreting Kimi K3 as a definitive victory. Moonshot presents it as an open weight model, but those files have not been published yet and the company promises to release them no later than July 27. Nor should we confuse this openness with complete open source, because details about the license and the rest of the system are still missing. Its 2.8 billion total parameters belong to a sparse architecture that activates 16 of its 896 experts. The company itself recommends configurations with 64 accelerators or more, very far from what a conventional computer can offer. The community reaction helps understand why Kimi K3 is attracting so much attention. One of the most striking examples is a recreation of macOS 27 which works within the browser and which its creator attributes to a swarm of model agents working for about three hours. They add to it Ballista, an interactive panel with a 3D balloon and several comparisons against Claude and GPT. They are not independent benchmarks, but demos shared by their own creators, but they allow you to see what kind of results the model is producing outside the tables. To create something like the macOS simulation or the ballista game, we don’t need to model every element by hand from scratch. We can describe the resultattach a reference and commission Kimi to build a functional application, for example with HTML, JavaScript and various graphics libraries. The project is then tested, modified, and finally published or recorded for sharing. Kimi K3 can be used from Kimi.comKimi Work, Kimi Code or tools connected to its API, although it is not confirmed which specific environment was used in several of the examples we have seen. It is still early to turn this launch into a definitive change of leadership. Fable 5 and GPT-5.6 Sun They are still ahead in several evaluations, the Kimi K3’s weights are not yet available and many of its capabilities will have to be verified with more time. Even so, what we have seen is already difficult to ignore: a Chinese company can compete for leading positions, offer competitive results and get the community to transform that capacity into real applications almost immediately. The race continues, but the margin between its main protagonists seems increasingly narrower. Images | Kimi | Screenshot In Xataka | China has a plan to win the AI ​​war against the US. And DeepSeek is its champion

Kimi Code does 75% of what Claude Code does at 20% of its price. The question is whether that 25% that is missing is the one that matters.

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A few days ago, the Chinese company Moonshot AI launched Kimi K2.6its new LLM that competes with the Gemini, GPT and Claude model families and is also especially competitive in price. Weeks earlier, it had launched Kimi Code, a programming AI agent that in turn competes with Gemini Cli, Codex and Claude Code. The question is obvious: can the Kimi Code/Kimi K2.6 pairing really compete with the fashionable pairing, Claude Code/Opus 4.7? The answer is complicated. A great model (but not perfect). Kimi K2.6 is an open weights model with one trillion parameters in total (an American trillion), of which 32 billion parameters are active and which uses the well-known Mixture-of-Experts architecture. In it launch article Its performance is shown compared to that of GPT-5.4 and Opus 4.6 and the truth is that its numbers in these synthetic tests seem really excellent: Here Kimi K2.6 is compared to GPT-5.4, Claude Opus 4.6 and Gemini 3.1 Pro. Source: Moonshot AI. Up to 8 times cheaper than Opus 4.6. Has subscription plans Claude Pro or ChatGPT Plus style, but it can also be used via API. The price in that case is $0.60 per million input tokens (0.16 if cached) and $4 per million output tokens. Claude Opus 4.6 costs $5 per million input tokens and $25 per million output tokens, or up to eight times more. Claude Opus 4.7 It has the same price and is theoretically better in performance, but when Kimi K2.6 was announced this version had not yet appeared (nor GPT-5.5). The magic of the swarm of AI agents. Claude Code works sequentially. Analyze the problem, execute a step, check the result and decide how to proceed. In Kimi Code a different approach is used: a “master agent” divides or decomposes the task we ask of it into independent subtasks and from that division launches up to 300 “subagents” that run in parallel and are capable of coordinating up to 4,000 steps simultaneously. Are many working at the same time better than one? It is the so-called “swarm of agents” of Kimi K2.6 that is used to the fullest in Kimi Code and that we can also activate in its free version on its official website. In Kimi K2.5 up to 100 subagents and 1,500 steps could be launched, so the jump is significant. In internal tests, Moonshot showed how these swarms managed, for example, to “refactor” an open source financial engine, working 13 hours straight and making more than 1,000 tool calls with a 185% improvement in average performance. Of course, these were internal tests. Beyond benchmarks. Kilo.ai is a company that develops tools like Kilo Code or Kilo CLI—programming agents similar to Kimi Code—and its engineers wanted evaluate the performance of both combinations. They gave Claude Opus 4.7 and Kimi K2.6 the same 1,042-line prompt to create FlowGraph, a workflow orchestration API with directed graph validation or real-time event streaming. Both models ran on Kilo CLI because what they wanted to compare were the models without further ado. Kimi was cheaper, but he also failed more. Claude Opus 4.7 finished in 20 minutes and the final cost was $3.56. Kimi K2.6 took longer, partly because server availability was limited (the model had just been launched), but it cost $0.67. Five times less. Kimi K2.6 did it well at a ridiculous price. Claude did much better, but it also cost five times as much. Kimi did 75% of what Claude did at 19% of the cost. The problem is that both believed they had done everything right and did not detect if they had made mistakes. Further analysis revealed that Claude had committed one and that Kimi had committed six of varying importance. According to Kilo.ai analysts, the final score for both was 91 points out of 100 for Opus 4.7 and 68 points out of 100 for Kimi. Two ways to see the glass. That score seems to make it clear that Kimi is simply cheaper because he did a worse job. But Kilo engineers had another way of looking at it. They have been comparing open weight models of Chinese companies for some time and have noticed how the gap with the “frontier” models of Anthropic or OpenAI is becoming less and less pronounced. “With a price of $0.67 and a thorough review, Kimi K2.6 is now a viable option. With a price of $3.56 and fewer fixes needed, Claude Opus 4.7 is the safer option. The choice between the two options depends on the analysis. A year ago, this choice was practically non-existent at this level of complexity.” Review is mandatory. Or what is the same: if after the work of Kimi K2.6 one carried out a more in-depth review and correction, it is likely that all these errors would be detected and corrected, but if we had to trust both models and we could only execute “one pass” of AI execution, Opus 4.7 would win the game. The key is that: one should not trust the code of any model right away, and it is advisable to always review that code. The geopolitical factor. Kimi and Kimi Code come from China, and the startup Moonshot AI has financial backing from Alibaba. The code that is processed in these models passes through their servers, something that for an individual developer may be irrelevant. However, for a company with sensitive proprietary code, contracts that must comply with certain European or American regulations and projects in regulated sectors, this can be a significant obstacle. Kimi Code mitigates this problem by offering the possibility of running the model locally thanks to its open weights, but that requires very powerful machines and eliminates part of the cost advantage. What Kimi Code has that Claude Code doesn’t. The clearest difference between both programming AI agents is parallelism. As we said, the ability to launch up to 300 subagents to work simultaneously attacking the same problem at the same time is remarkable. For analysis of large repositories or generation … Read more

DeepSeek promised them happiness as the great Chinese AI. I didn’t count on a small detail: Kimi

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Just a year ago, DeepSeek was one of the biggest scares that Silicon Valley had received dwarves. A Chinese model trained with a fraction of OpenAI’s budget equal to GPT-4 in benchmarks. Upon its arrival the message seemed clear: Western dominance of AI had its days numbered. Today, the story stands, but not thanks to DeepSeek. The DeepSeek case. DeepSeek carries months late for its V4 and, to date, has already lost three of the authors of R1, the model that catapulted them to success. The monthly downloads fell 72% in the second quarter of the year, seeing how Doubao (ByteDanec) snatched the lead. With missed dates, usage errors due to cyber attacksand the difficulty of split from NVIDIA To bet almost entirely on Huawei’s Ascend chips, Chinese alternatives like Kimi have been gaining ground. Meanwhile, on the other side of China. Moonshot AI was not born surrounded by noise like DeepSeek. It was founded in March 2023 by three former colleagues from Tsinghua University: Yang Zhilin—PhD from Carnegie Mellon, former Google Brain and Meta AI—, along with Zhou Xinyu and Wu Yuxin. There were no visible or media faces behind it, only product. That product is Kimi, and in early January 2026 the company launched it in its K2.5 version. In code and video benchmarks managed to surpass GPT-5 and Gemini Pro 3with the key to Chinese AI: its API costs between 4 and 17 times less than OpenAI’s. Those responsible for Moonshot explained how Kimi was almost at Claude’s level in software development testing, encouraging the race for open models. The money arrived. The commercial results are what really attract attention. In less than 20 days Following the launch of K2.5, Kimi’s cumulative revenue exceeded everything billed during 2025. API’s international revenue increased fourfold since November of the previous year. The consequence in valuation has been dizzying: 4.3 billion dollars in December 2025, 10 billion in February 2026, 18 billion in March. Three months, valuation multiplied by four. Kimi has thus become the fastest decacorn in Chinese business history. The Chinese maelstrom. DeepSeek was born a year ago as the great revolution that questioned the closed model of Silicon Valley. It only took a few months for Moonshot to steal the limelight and manage to be on par with – or even above – giants like Google and OpenAI in the most used models in the world. In favor of DeepSeek, it should be noted that its objective is different: it does not follow the typical startup pattern with pressure for immediate monetization and it is a gigantic AI laboratory that can afford not to win in the short term. In Xataka | DeepSeek API: what it is, what it is for, prices and how you can get one to use in your projects

Select the model to use between Claude, GPT, Gemini, Kimi, Grok or Sonar

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Let’s tell you how you can choose the artificial intelligence model What are you going to use with? Perplexity in a prompt. This is a chatbot known for allowing you to access many cutting-edge models from third-party companies, something it does automatically depending on the request you make. However, if you are going to use Perplexity, it is advisable to know one of its functions basic, being able to choose by hand which model you want to use. And yes, every time Google, Anthropic or OpenAI launch a new model of artificial intelligenceat Perplexity they are going to add it to their catalog. The results will not be exactly the same as if you use the paid versions of ChatGPT, Grok, Claude or Gemini, because Perplexity may modify them a little. However, you will be able to take advantage of the reasoning power of these models. Choose the AI ​​model to use in Perplexity To choose the AI ​​you want to use in Perplexity, you have to look at the box where you write the prompt. In it, you must click on the option AI modelwhich will appear with the icon of what appears to be a chip. It is to the far left of the series of icons that appear at the bottom right in the prompt writing field. When you click on that button, it will appear a list of all models of artificial intelligence that you can use. Both the best and the latest available from Gemini, GPT, Claude, Grok, Kimi or Perplexity’s own Sonar will appear. This is something that you can do in its web version or in its mobile or computer applications. Here, you should know that you can choose the model with each prompt within a conversation with Perplexity. Come on, you can ask a question with one model, and then ask the next question with another. Also, below the list you will see the number of queries you can make with the most modern models. In Xataka Basics | The best prompts to save hours of work and do your tasks with ChatGPT, Gemini, Copilot or other artificial intelligence

Deepseek marked a turning point in the AI race. Now another Chinese company wants to imitate its success: Kimi K2 is born

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The Chinese startup Monshot AI has presented Kimi K2, an open -source artificial intelligence model that arrives with outstanding programming capabilities and autonomous tasks that, according to The published benchmarksThey spray competition in several of their models. Its launch occurs at a key moment for the sector, when Chinese companies seek to replicate the disruptive success of Deepseek with potential height models and much cheaper than market alternatives. Kimi does not come from nothing. MoNshot ai was one of the most promising startups in the Chinese ecosystem of AI and that giants like Alibaba have invested greatly. His Kimi chatbot reached third place in monthly active users in August 2024, but fell to the seventh in June After the emergence of Deepseek R1 in January. Now try to recover ground with a strategy that combines open source and aggressive prices, following the formula that catapulted Deepseek. Image: MoNshot AI What Kimi K2 offers. The model has 1 billion total parameters and 32,000 million activated parameters, using The well-known Mixture-Of-Experts architecture to optimize computational costs. It is presented in two versions: a base for researchers and developers, and another optimized for conversation and autonomous tasks. Kimi K2 thus becomes Moonshot AI’s proposal with the ability to act as an intelligent agent to use tools, write code, complete workflows or talk, among other tasks. Kimi K2 explained in numbers. In performance testsKimi K2 has achieved 65.8% precision at Swe-Bench Verified, one of the most demanding benchmarks for software engineering. In LivecodeBench it reached 53.7%, exceeding 46.9% of Deepseek-V3 and 44.7% of GPT-4.1. In mathematics, its 97.4% score in Math-500 exceeds 92.4% of GPT-4.1, suggesting significant advances in mathematical reasoning. The price factor. MoNshot is charging $ 0.15 per million input tokens and $ 2.50 per million tokens out of the developers who use their API. Compared, Claude Opus 4 It charges 100 times more for the entrance (15 dollars) and 30 times more for the output ($ 75), while GPT-4.1 charges 2 dollars per entrance and 8 per exit. In addition, the model is available for free in Web applications and Kimi mobile, without monthly subscriptions that require chatgpt or Claude for their most advanced models. Technical innovation. MoNshot has developed the MuCanclip optimizer, which allows train models of one billion parameters “With zero training instability.” This technology could drastically reduce the training costs of large models, a problem that has limited the development of AI to companies with greater resources. Double channel strategy. The company offers so much Free access to the source code as payment API at a very competitive price. This strategy allows companies to start with the API for immediate implementation and then migrate to self -healing versions either by regulatory cost or compliance. And it is that each developer who downloads Kimi K2 becomes a potential business client. Moment of inflection. Kimi K2 represents a convergence point where open source models and proprietary alternatives shake hands. MoNshot AI intends to turn Kimi into a tool for everything, while offering its open source model and is reserved to charge for the use of its API for all types of implementations. And now what. The launch reaches a critical point in which both Openai, such as Google or Anthropic, must respond to this wave of cheap and high quality language models. The issue is no longer whether open source models can match the owners, but if large technological ones can adapt their business models fast enough to compete in this new scenario. The looks are put in GPT-5 And in the next movements of the industry at a rate, as always, accelerated. Cover image | Xataka with Mockuuuups Studio and Kimi AI In Xataka | Grok 4 destroys the tests and aims to be the most advanced AI model. The problem is that Elon Musk continues to sabotage his answers

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