Kimi Archives - usatoday24

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.

April 28, 2026 by usatoday24

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

April 7, 2026 by usatoday24

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

February 24, 2026 by usatoday24

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

July 14, 2025 by usatoday24

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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