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Introducing Cohere's first open-source coding model: North Mini Code Small & efficient, designed for agentic performance and built for community input.

600,819 次观看 • 4 个月前 •via X (Twitter)

34 条评论

NVIDIA AI 的头像
NVIDIA AI4 个月前

Nice! Great to see another open source model released. 🙌

Cohere 的头像
Cohere4 个月前

For the community 🤝

Cohere 的头像
Cohere4 个月前

Small: 30 billion parameters, 3B active. Efficient: Benchmarks to 33.4 on the Artificial Analysis Coding Index, competitive among similar sized models. Open Source: Apache 2.0 license so developers can experiment, test, and build their way. Learn more:

Cohere 的头像
Cohere4 个月前

We encourage developers to share their builds with us and give feedback to shape future iterations. Let’s shape the future of sovereign AI together. Download:

maxwell 的头像
maxwell4 个月前

Did you literally just post a six-minute launch video where it fails edit tool calls for half of the video?

Berke Demiralp 的头像
Berke Demiralp4 个月前

Open source model over closed source. Any day.

Florian Brand 的头像
Florian Brand4 个月前

banger after banger after banger thanks canada bros 🙏

Jeffrin James🆗️ 的头像
Jeffrin James🆗️4 个月前

How do these 2 heads work in the same office without any conflict

Sakura Yuki 的头像
Sakura Yuki4 个月前

30B total / 3B active is a wild MoE config. Speed of a 3B model, but you still need the VRAM of a 30B to hold all 128 experts. Local VRAM is the real bottleneck here, but the TPS should be ridiculous.

Caleb 的头像
Caleb4 个月前

congrats!

elvis 的头像
elvis4 个月前

Wow! This is another cool release!

brendan 的头像
brendan4 个月前

codehere

Ivan Burazin 的头像
Ivan Burazin4 个月前

So happy we at @daytonaio could be a small part of this 🙏

Nick Khami 的头像
Nick Khami4 个月前

tuesday launch day is always such a great day

Luis Catacora 的头像
Luis Catacora4 个月前

congrats team on the model launch!

mr-r0b0t 的头像
mr-r0b0t4 个月前

@DJLougen you’re up 😈

Vaclav Cerny 的头像
Vaclav Cerny4 个月前

I mean, I would very much like Cohere to succeed, but why should I use this instead of qwen3.6 35b again?

Guilherme O'Tina 的头像
Guilherme O'Tina4 个月前

nice to see another moe at 30b/3b form factor. apache 2.0 is the real win. would love to see how agentic coding shakes out vs qwen3 on the same hardware, that 33.4 benchmark number only tells part of the story

Runloop Developer 的头像
Runloop Developer4 个月前

Congrats on the launch! So happy to have been a part of this process 🚀

Zaid 的头像
Zaid4 个月前

I liked the honest bit about usability. Could be nice for some offline robots as backup I think

Amit Shekhar 的头像
Amit Shekhar4 个月前

Great work. It's a great time to be alive in the age of AI.

Jarett Forzano 的头像
Jarett Forzano4 个月前

It’s going to be less resource intensive to run models locally than GTA-6

Oli Wilkins 的头像
Oli Wilkins4 个月前

Really cool and a big win for everyone! Would love it for the data to be open too. Then it can truly be called open source, like @allen_ai’s LLMs.

Mariusz Kurman 的头像
Mariusz Kurman4 个月前

Awesome, congrats and thanks for sharing!

g023 的头像
g0234 个月前

Nice size to target, be also good to get the ~8b locked down too

Aaliya 的头像
Aaliya4 个月前

Simple and efficient models can be very helpful.

Eclipse 🌖 的头像
Eclipse 🌖4 个月前

Interesting they're calling it "Mini" — curious how the agentic performance holds up vs Qwen-Coder 2.5 at similar parameter counts.

AI Mastery Guide 的头像
AI Mastery Guide4 个月前

Small, efficient and open source for agentic coding is a solid combo. How does it actually benchmark against the other small coding models?

Ben Minor 的头像
Ben Minor4 个月前

Making Canada proud. LFG

Shortwav Labs 的头像
Shortwav Labs4 个月前

Gave it a try. Pretty fast on my m5 max MacBook Pro. It did ok but for some reason it was determined to use Deno instead of Node.js? Also couldn’t handle pnpm… performance is good, but reasoning and output need to be improved.

Colt Feltes 的头像
Colt Feltes4 个月前

Awesome work, team. Appreciate you guys contributing to the ecosystem!

Nabeel Ahmed 的头像
Nabeel Ahmed4 个月前

Make a bigger real one and deploy via Cursor Canadian coding model that everyone can use and is near-SOTA

Victoria Blake 的头像
Victoria Blake4 个月前

Exciting to see open-source coding models becoming more efficient and accessible. North Mini Code highlights that strong agentic performance doesn't always require massive models—optimization and community-driven innovation matter just as much. Great work by the Cohere team. 🚀

Christopher Tanner 的头像
Christopher Tanner4 个月前

Nice

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