Moonshot
Moonshot's coding specialist — a trillion parameters pointed at agents that work across files, tools and the terminal.
262.144K tokens
Konteks
131,072 tokens
Output maks
Sedang
Kecepatan
Long runs across files, tools and terminal commands.
Multi-file changes that stay consistent to the end.
Reads real error output and forms a hypothesis first.
Paste a broken render and describe the fix.
Tuned to this model — click any line to copy.
Kimi K2.7 Code is the coding specialist in Moonshot AI's Kimi line, released on 12 June 2026. Where Kimi K3 is the generalist you ask about anything, K2.7 Code is the one pointed at software: a one-trillion-parameter mixture-of-experts model with about 32 billion parameters active per token, built for agents that operate across files, tools and terminal commands over an extended session rather than answering a single question.
Two things define it. The first is scope: a 262,144-token context window, which is enough to hold a substantial slice of a real codebase — several files, the configuration, the docs and a long agent transcript — without chunking tricks or aggressive summarisation. The second is restraint. Moonshot reports that K2.7 Code cuts reasoning token usage by roughly 30% against K2.6 while scoring about 21.8% higher on the lab's own Kimi Code Bench v2, which is the unusual combination of thinking less and getting more right.
Thinking is always on, so the model reasons before each response and each tool call rather than waiting to be asked. In practice that shows up as fewer wrong turns in the middle of a long refactor: it reads the error, forms a hypothesis, and checks it, instead of guessing and rerunning. It takes images as input too, so a screenshot of a broken render or a failing dashboard can go straight into the conversation.
The weights are open under a Modified MIT licence, which matters if you care about being able to self-host later or audit what you are building on. On NinjaChat it is included in every plan: pick it from the model list and keep a whole engineering task in one thread.
Dari nol ke hasil pertama Anda dalam waktu kurang dari satu menit.
01
Create a NinjaChat account and choose a plan
02
Open chat and pick Kimi K2.7 Code from the model list
03
Give it the files, the error output and the goal
04
Keep the whole task in one thread so its reasoning carries across turns
Perbandingan jujur — di mana {model} unggul, dan di mana tidak.
Tuned for code and long agent runs, and cheaper
K3 is the stronger generalist for non-coding work
Open weights and a lower price per token
Codex has OpenAI's tooling and reasoning-effort controls
More token-efficient over long coding sessions
GLM 5.2 is broader across systems design
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