Kimi K2.6 vs North Mini Code

Moonshot AI · China  |  Cohere · Global · Updated June 2026

Quick verdict

Pick Kimi K2.6 for open-weight agentic coding and long-horizon tasks or multi-agent swarms (scales to ~300 sub-agents). Pick North Mini Code for agentic software engineering, code generation, and terminal tasks or efficient sparse moe — 3b active of 30b, runs on a single h100. On a tight budget at scale, North Mini Code is the value pick.

Kimi K2.6 (Moonshot AI) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. Kimi K2.6 is moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. North Mini Code is cohere's first agentic coding model: an open-weight 30B/3B-active MoE built for real software-engineering and terminal tasks that runs on a single H100. They diverge most on price, context window and coding benchmarks — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecKimi K2.6North Mini Code
ProviderMoonshot AI (China) Cohere (Global)
ReleasedApril 20, 2026 June 9, 2026
Context window256K (~393 pages) 256K (~384 pages)
Price (in/out)$0.6/$2.5 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, code text, code
SWE-Bench Verified80.2% 67.6%
MRCR v2 @ 1MNot published Not published

Who wins what

Open-weight agentic coding and long-horizon tasks

Kimi K2.6

A core design strength of Kimi K2.6.

Multi-agent swarms (scales to ~300 sub-agents)

Kimi K2.6

A core design strength of Kimi K2.6.

Self-hosting and data-residency control

Kimi K2.6

A core design strength of Kimi K2.6.

Agentic software engineering, code generation, and terminal tasks

North Mini Code

A core design strength of North Mini Code.

Efficient sparse MoE — 3B active of 30B, runs on a single H100

North Mini Code

A core design strength of North Mini Code.

High throughput (up to 2.8x Devstral Small 2) at low latency

North Mini Code

A core design strength of North Mini Code.

Lowest cost at scale

North Mini Code

At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Kimi K2.6

Its 256K window is about 1× larger, fitting roughly 393 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

North Mini Code

At Open weight (self-host / free) it undercuts Kimi K2.6, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Kimi K2.6

Larger 256K window fits more in one prompt.

Anyone whose priority is open-weight agentic coding and long-horizon tasks

Kimi K2.6

It is specifically built for that.

Anyone whose priority is agentic software engineering, code generation, and terminal tasks

North Mini Code

That is its strongest area.

Kimi K2.6: where it fits

Moonshot's open-weight 1T-parameter (32B active) MoE model — frontier-class agentic coding you can download and self-host. Released April 20, 2026 by Moonshot AI, it is built for open-weight agentic coding and long-horizon tasks, multi-agent swarms (scales to ~300 sub-agents), self-hosting and data-residency control, and strong price-to-performance across many API providers.

Its trade-offs are real: 256K context trails the 1M Claude and Gemini flagships, weaker on single-turn vision and grounded multimodal tasks, and chinese-jurisdiction data and newer vendor track record. At $0.6 in / $2.5 out per million tokens, it sits in the budget price band.

North Mini Code: where it fits

Cohere's first agentic coding model: an open-weight 30B/3B-active MoE built for real software-engineering and terminal tasks that runs on a single H100. Released June 9, 2026 by Cohere, it is built for agentic software engineering, code generation, and terminal tasks, efficient sparse MoE — 3B active of 30B, runs on a single H100, high throughput (up to 2.8x Devstral Small 2) at low latency, and fully open weights under Apache 2.0 with fp8 and 4-bit builds.

Its trade-offs: text-only and coding-specialized — not multimodal or general-purpose, and 256K context and modest general-intelligence index trail frontier models. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

The bottom line for this matchup

Kimi K2.6 and North Mini Code overlap enough that the right pick depends on your specific job. North Mini Code costs less per token; Kimi K2.6 holds the larger context; and each leads in its own area — Kimi K2.6 for open-weight agentic coding and long-horizon tasks, North Mini Code for agentic software engineering, code generation, and terminal tasks. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Kimi K2.6 and North Mini Code without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Kimi K2.6 or North Mini Code better for coding?

On SWE-Bench Verified, Kimi K2.6 scores 80.2% and North Mini Code scores 67.6% — Kimi K2.6 has the measurable edge.

Which is cheaper, Kimi K2.6 or North Mini Code?

North Mini Code is cheaper — $0.6/$2.5 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

Kimi K2.6 — 256K vs 256K, about 1× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Kimi K2.6 and North Mini Code together?

Yes — a multi-model platform like LumiChats gives you Kimi K2.6, North Mini Code and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Kimi K2.6 or North Mini Code?

North Mini Code — released June 9, 2026, about 50 days after Kimi K2.6.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.