Llama 4 Scout vs North Mini Code
Meta · US | Cohere · Canada · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. 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.
Llama 4 Scout (Meta, US) and North Mini Code (Cohere, Canada) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. 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. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Context window: Llama 4 Scout holds 39× more — 10M (~15,000 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: North Mini Code is the newer model by about 14 months (released June 9, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-Canada matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | Llama 4 Scout | North Mini Code |
|---|---|---|
| Provider | Meta (US) | Cohere (Canada) |
| Released | April 2025 | June 9, 2026 |
| Context window | 10M (~15,000 pages) | 256K (~384 pages) |
| Price (in/out) | Open weight (self-host / free) | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, code | text, code |
| SWE-Bench Verified | Not published | 67.6% |
| MRCR v2 @ 1M | 15% | Not published |
Who wins what
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 39× more than North Mini Code's 256K in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.
Self-hosted, data-private deployment
Llama 4 Scout
Llama 4 Scout lists self-hosted, data-private deployment among its strengths; North Mini Code does not.
Agentic software engineering, code generation, and terminal tasks
North Mini Code
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 — and it is the newer of the two.
Efficient sparse MoE — 3B active of 30B, runs on a single H100
North Mini Code
North Mini Code lists efficient sparse MoE — 3B active of 30B, runs on a single H100 among its strengths; Llama 4 Scout does not.
High throughput (up to 2.8x Devstral Small 2) at low latency
North Mini Code
North Mini Code lists high throughput (up to 2.8x Devstral Small 2) at low latency among its strengths; Llama 4 Scout does not.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 39× larger than North Mini Code's 256K, fitting roughly 15,000 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Llama 4 Scout
Larger 10M window fits more in one prompt.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
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.
An enterprise with regional data-residency rules
→ Llama 4 Scout or North Mini Code
Origin (US vs Canada) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Llama 4 Scout: where it fits
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.
Its trade-offs are real: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
This is less "which is smarter" and more "which ecosystem fits." Llama 4 Scout (US) and North Mini Code (Canada) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both Llama 4 Scout 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 pricingFrequently asked questions
Is Llama 4 Scout or North Mini Code better for coding?
Public SWE-Bench figures are not available for Llama 4 Scout, so the honest test is your own repository — run an identical real bug through both. By design, Llama 4 Scout leans toward largest advertised context (10m) while North Mini Code leans toward agentic software engineering, code generation, and terminal tasks, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or North Mini Code?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Llama 4 Scout — 10M vs 256K, about 39× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, 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, Llama 4 Scout or North Mini Code?
North Mini Code — released June 9, 2026, about 14 months after Llama 4 Scout.
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.