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Moonshot Open-Sources Kimi-K2.7-Code Coding Model, Says It Uses 30% Fewer Reasoning Tokens

aiai-modelingai-model-releasesai-open-modelsai-productsai-agents-coding 11 posts · 8 accounts

Moonshot released and open-sourced Kimi-K2.7-Code, its latest coding model, saying it improved on K2.6 by 21.8% on Kimi Code Bench v2, 11.0% on Program Bench and 31.5% on MLS Bench Lite, while using 30% fewer reasoning tokens. The model is available through Kimi API and Kimi Code, and Moonshot separately published the weights and code.

Moonshot also launched a Kimi Code Beta Program for users who want to test upcoming models and features before public release. The company said a 6x High-Speed Mode is coming soon and described the update as improving instruction following and end-to-end success rates on longer coding tasks.

From the sources (11 posts)

@kimi_moonshot

🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less

@kimi_moonshot

🎸 We're also launching the Kimi Code Beta Program today. Apply now if you'd like to try upcoming models and features before public release 👉

@stevibe

Kimi K2.7-Code!

@kimi_moonshot

🔗 Weights & code:

@teortaxestex

I want to see this compared with Composer 2.5 Like, really hard Cursor has a ton of proprietary data, a large head start, and threw a Colossus at RLing Kimi K2.5 checkpoint. What is the gap now?

@vanstriendaniel

RT @Kimi_Moonshot: 🔗 Weights & code:

@crystalsssup

less overthinking 👀

@teortaxestex

HUGE @htihle and others will get to test this soon I hope

@zephyr_z9

Great work from KIMI

@eliebakouch

RT @Kimi_Moonshot: 🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over…

@code_star

It would be really cool if the top Chinese labs could properly benchmark and evaluate Fable/Mythos to show how it compares to their releases. I guess that isn’t possible though.

Preview built on a synthetic news corpus (16 weeks, Apr–Jul 2026). Impact calls are model reads, not price data.

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