DeepSeek R1 vs North Mini Code
DeepSeek · China | Cohere · Canada · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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.
DeepSeek R1 (DeepSeek, China) 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. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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 and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: North Mini Code holds 2× more — 256K (~384 pages) vs 128K (~192 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 17 months (released June 9, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-Canada matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | DeepSeek R1 | North Mini Code |
|---|---|---|
| Provider | DeepSeek (China) | Cohere (Canada) |
| Released | January 2025 | June 9, 2026 |
| Context window | 128K (~192 pages) | 256K (~384 pages) |
| Price (in/out) | $0.55/$2.19 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | Not published | 67.6% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open-weight reasoning model
DeepSeek R1
North Mini Code is comparatively weak here — 256K context and modest general-intelligence index trail frontier models
Transparent chain-of-thought
DeepSeek R1
DeepSeek R1 lists transparent chain-of-thought among its strengths; North Mini Code does not.
Low cost
DeepSeek R1
DeepSeek R1 lists low cost 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 carries the larger 256K context.
Efficient sparse MoE — 3B active of 30B, runs on a single H100
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.
High throughput (up to 2.8x Devstral Small 2) at low latency
North Mini Code
DeepSeek R1 is comparatively weak here — smaller 128K context
Lowest cost at scale
North Mini Code
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek R1's $0.55/$2.19 per 1M tokens.
Largest single-prompt input
North Mini Code
Its 256K window is about 2× larger than DeepSeek R1's 128K, fitting roughly 384 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 DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ North Mini Code
Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
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
→ North Mini Code or DeepSeek R1
Origin (China vs Canada) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: discontinued - no longer offered as a standalone model via DeepSeek's API as of mid-2026; the legacy alias that used to point to it stopped working July 24, 2026, older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 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
This is less "which is smarter" and more "which ecosystem fits." DeepSeek R1 (China) and North Mini Code (Canada) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. North Mini Code is the cheaper option, which matters at volume. 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 DeepSeek R1 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 DeepSeek R1 or North Mini Code better for coding?
Public SWE-Bench figures are not available for DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model 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, DeepSeek R1 or North Mini Code?
North Mini Code is cheaper — $0.55/$2.19 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
North Mini Code — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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, DeepSeek R1 or North Mini Code?
North Mini Code — released June 9, 2026, about 17 months after DeepSeek R1.
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.