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) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. 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
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.
Specifications
Spec
DeepSeek R1
North Mini Code
Provider
DeepSeek (China)
Cohere (Global)
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 — A core design strength of DeepSeek R1.
Transparent chain-of-thought: DeepSeek R1 — A core design strength of DeepSeek R1.
Low cost: DeepSeek R1 — A core design strength of DeepSeek R1.
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: North Mini Code — Its 256K window is about 2× larger, 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.
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: 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
DeepSeek R1 and North Mini Code overlap enough that the right pick depends on your specific job. North Mini Code costs less per token; North Mini Code holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, 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.
Frequently 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.
DeepSeek R1 vs North Mini Code
DeepSeek · China | Cohere · Global · 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) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. 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.
Side-by-side specs
Spec
DeepSeek R1
North Mini Code
Provider
DeepSeek (China)
Cohere (Global)
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
A core design strength of DeepSeek R1.
Transparent chain-of-thought
DeepSeek R1
A core design strength of DeepSeek R1.
Low cost
DeepSeek R1
A core design strength of DeepSeek R1.
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
North Mini Code
Its 256K window is about 2× larger, 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.
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: 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
DeepSeek R1 and North Mini Code overlap enough that the right pick depends on your specific job. North Mini Code costs less per token; North Mini Code holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, 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 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.
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.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.