Moonshot AI has updated its flagship Kimi model. The new version, indexed as K2.5, has become notably smarter in tasks that require thinking rather than just providing a quick answer.
Что нового в Kimi K2.5
What Changed in K2.5
The main improvement concerns its reasoning ability. Moonshot utilized an approach currently known as reinforcement learning – the model learns not simply to generate text, but to seek a solution through a chain of steps. This is similar to how models like the OpenAI o1 series or DeepSeek R1 operate.
As a result, Kimi K2.5 demonstrated significant growth in benchmarks related to mathematics, programming, and logical tasks. For example, on the AIME 2024 test (which consists of math olympiad problems for high school students), the model scored 79.2%. For comparison, the previous version, K1.5, only managed 26.7%.
On GPQA Diamond – a test compiling PhD-level questions in physics, chemistry, and biology – the result grew from 49.5% to 65.2%. In programming tasks (LiveCodeBench), accuracy jumped from 35.3% to 56.8%.
Обработка длинного контекста в Kimi K2.5
Long Context Remains, But Is Now More Convenient
Kimi was originally known for its ability to work with very large texts – up to one million tokens in a single query. This makes up roughly 750,000 words in English or several hundred pages of text. In the new version, this capacity remains, but the company has improved the quality of processing such documents.
Now, the model is better at finding the necessary information in long texts and more precisely answering questions that require analyzing several fragments simultaneously. On the Ruler benchmark, which specifically checks this, K2.5 showed a result of 97.35% – higher than many Western competitors have achieved.
Как работает Kimi K2.5 на практике
How It Works in Practice
Moonshot demonstrates several use cases. One of them is the analysis of scientific articles. You can upload several papers, and the model will find key ideas, compare approaches, and highlight contradictions on its own.
Another example is working with code. K2.5 can not only write programs but also understand existing code, explain its structure, find errors, and suggest improvements. The company claims the model handles this better than before, thanks to its enhanced capability for step-by-step analysis.
One more scenario involves legal and financial documents. Here, accuracy and the ability to account for context from different parts of the text are crucial. According to Moonshot, K2.5 handles extracting facts and drawing conclusions based on them very well.
Доступность и ограничения Moonshot Kimi K2.5
Availability and Limitations
The Kimi K2.5 model is available via a web interface on the company's website and through an API. Moonshot also offers mobile apps for iOS and Android. There is a free access tier, though with limits on the number of queries. Paid plans are provided for active users.
An important point: the model works only with Chinese and English languages. Support for other languages has not been announced yet. This is a standard situation for models from China – they are primarily oriented towards the domestic market and the English-speaking audience.
Куда движется индустрия ИИ
Context: Where the Industry Is Heading
The release of K2.5 fits into the general trend. After OpenAI introduced o1 and DeepSeek released its R1, many teams began adding reasoning mechanisms to their models. The idea is that a language model shouldn't give the first answer that comes to mind – it needs to “think,” go through options, and verify hypotheses.
This is especially important in tasks where a single mistake breaks the entire solution: mathematics, programming, logical puzzles. Ordinary models often stumble in such tasks because they generate text sequentially, token by token, and if they make a mistake at the beginning – they can no longer correct it.
Models with enhanced reasoning attempt to solve this problem through internal “deliberations” – they generate several answer variants, check them, and choose the best one. This slows down operations but increases accuracy.
Дальнейшие перспективы Kimi K2.5
What's Next
Moonshot doesn't reveal the technical details of training K2.5, but judging by the results, the company used approaches similar to those applied by OpenAI and DeepSeek. This means that Chinese teams are not just catching up with Western leaders but are actively experimenting with new architectures.
The question remains open as to how applicable these improvements are to real tasks outside of benchmarks. Tests are good, but they don't always reflect how a model will behave in a live dialogue or when working with non-standard requests. For now, Kimi K2.5 looks like a serious step forward, but final conclusions can only be drawn after thousands of users try the model in action.
In any case, the appearance of such models expands the selection. If you need a system capable of working with huge texts while reasoning logically, Kimi K2.5 is one of the options worth paying attention to.