In the future, writing code will really become more and more like "making a request and waiting for the result".
Article author and source: AI Frontier Early Knowledge
The dark side of the moon suddenly dropped a bombshell – the Kimi K2.7 Code programming model, which was immediately released as a fully open-source tool!
Not only did my coding skills improve dramatically, but it also completely cured the problem of "overthinking and wasting money" that everyone had been complaining about for the past six months.
Even more impressive is the 6x speed-up version, which will officially launch next Monday.
Today's article will expose all the highlights and pitfalls, and after reading it, you'll know just how valuable it is for your work.
I. Three core upgrades, each addressing a key pain point
1. Long code capabilities have increased dramatically; the more challenging the task, the more stable the code becomes.
First, the hard data: In official internal and external benchmark tests, the K2.7 Code shows a significant improvement over the previous generation K2.6.
Kimi Code Bench v2 saw a direct increase of 21.8%, MLS Bench Lite surged by 31.5%, and even Agent-based autonomous execution tests generally saw an improvement of around 10%.
To put it simply: In the past, if you asked AI to write a complete project with tens of thousands of lines, such as a complete mini-program backend, it would likely "forget" as it wrote it—the interface parameters defined earlier would all be written incorrectly later; the logic defined earlier would go completely off track later, and you would have to correct it back and forth, which was a lot of effort.
This time, the instruction compliance capability of long context programming has been specifically enhanced. The longer the code and the more complex the task, the more obvious the advantage becomes. The logic remains online from beginning to end, without any mishaps or inconsistencies.
2. Eliminate ineffective thinking, saving 30% on tokens directly.
Anyone who has ever used AI to write code has surely complained about its thinking patterns: it's incredibly accurate, but the tokens drop faster than your heartbeat, and you feel the pinch when you check your bills at the end of the month.
This K2.7 Code specifically cured this internal friction caused by "overthinking".
The reasoning logic has been optimized, and unnecessary mental steps have been greatly reduced. When doing the exact same task, the average token consumption has been reduced by 30%.
To put it simply: the effect is better, and it costs less money, which is equivalent to a 30% discount. Over the long term, it can save you a considerable amount of money.
3. More for the same price – these users get it for free.
The question many people are most concerned about: Will the price increase if it becomes stronger?
The official statement directly reassured everyone: the input and output prices for a 1M context are exactly the same as those for K2.6—6.5 yuan per million tokens for input and 27 yuan per million tokens for output.
Even the input price for cache hits has dropped to 1.3 yuan per million tokens, and it's cheaper to call commonly used project documents and code frameworks a second time.
The default model for Kimi Code Plan has now been upgraded, and both regular and enterprise members can experience it directly without spending extra money.
Developers who want to experiment can simply download the complete open-source model from Hugging Face, and freely deploy it locally or modify it further.
⚠️ Here are two important points to note to avoid these pitfalls:
• This model must be in "thinking mode" to unleash its full potential. Manually closing the API will result in an error, while closing it on the web will automatically revert to K2.6.
• It's a pure programming-focused model. For non-programming tasks like writing copy, creating plans, or chatting, the versatile K2.6 is more suitable.
II. Bonus Scene: 6x Speed Version, See You Next Monday
If the upgrades mentioned above were already appealing enough, then the next piece of news has the entire developer community on edge.
The official announcement stated that the Kimi K2.7 Code high-speed version will officially launch its API on June 15th, which is next Monday.
The same model can be output at a speed 5-6 times faster than the standard version.
• In a typical programming scenario, it can output 180 tokens per second;
• In short-context scenarios, it directly reaches 260 Tokens/s.
What does that mean? In the past, if you entered a feature requirement, you would have to wait ten or twenty seconds for the complete code to appear; now, the code is almost finished as soon as you finish speaking, as fast as if a senior programmer is sitting across from you typing code in real time.
The price is also very reasonable: 6x the speed for only twice the price, which means the cost-effectiveness is tripled. When rushing projects or meeting deadlines, the high-speed version maximizes efficiency.
III. Understanding the Trend: Three Fundamental Changes are Occurring in AI Programming
Many people think this is just a regular version update, but in my opinion, it reveals three very clear trends in the AI programming industry:
1. Intensive competition within large-scale models: shifting from "flaunting parameters" to "competing on user experience".
In the past two years, when various companies held press conferences, they would talk about trillions of parameters and millions of contexts, which sounded very high-tech. However, for ordinary developers, they were either too expensive to use or performed poorly in actual work.
The tide has completely turned: everyone is now focusing on real pain points. Too expensive? Reduce token consumption; too slow? Develop a high-speed version; long codebases are inefficient? Optimize them specifically.
To put it simply, when it comes to the practical application of AI tools, the competition is never about who has the biggest parameters, but about who can truly help users save money and improve efficiency.
2. AI programming: From "code snippet tools" to "fully automated agent assistants"
In this upgrade, many people overlooked the fact that the Agent's capabilities also improved by about 10%.
This is the signal that deserves the most attention.
Previously, AI was just your "code completion tool"—you tell it to write a function, and it writes a function. In the future, it will become your "project assistant"—you give it a requirement, and it will break down the task, write the code, run the tests, fix the bugs, and complete the entire process on its own.
Before long, the core work of programmers may no longer be writing code, but rather making requirements, conducting acceptance tests, and controlling the direction. Pure coding work will be increasingly taken over by AI.
3. "Open source as the foundation + value-added services" will become the industry norm.
This time, Dark Side of the Moon has made the model completely open source, allowing individuals and small teams to use it for free and modify it at will; at the same time, it has also launched a high-speed version and enterprise services with differentiated pricing.
On one hand, they rely on open source to build an ecosystem and reputation, expanding their business; on the other hand, they rely on high-end services to generate revenue and cover R&D costs.
In the future, the large model industry will no longer be a choice between purely closed-source or purely open-source. Layered operation and differentiated services will be a must for all players.
IV. Finally, a couple of words
Overall, this update to Kimi K2.7 Code is a genuine upgrade to the user experience, not just incremental improvements.
From long code accuracy to token usage costs, and the upcoming 6x speed output, every point precisely addresses the daily pain points of developers.
With the high-speed version launching next Monday, the user experience of AI programming is expected to take another leap forward. This is definitely good news for ordinary developers and small teams—they can use top-tier AI programming capabilities without spending a fortune, effectively doubling their work efficiency.
In the future, writing code will really become more and more like "making a request and waiting for the result".
