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. Pick OLMo 3 32B Think for the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights or a genuine 32b reasoning ('think') model that generates explicit chain-of-thought.
North Mini Code (Cohere, Canada) and OLMo 3 32B Think (Allen Institute for AI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. OLMo 3 32B Think is allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: North Mini Code holds 3.9× more — 256K (~384 pages) vs 65K (~98 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 7 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a Canada-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
North Mini Code
OLMo 3 32B Think
Provider
Cohere (Canada)
Allen Institute for AI (US)
Released
June 9, 2026
November 20, 2025
Context window
256K (~384 pages)
65K (~98 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
67.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
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 — North Mini Code lists high throughput (up to 2.8x Devstral Small 2) at low latency among its strengths; OLMo 3 32B Think does not.
The most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights: OLMo 3 32B Think — OLMo 3 32B Think lists the most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights among its strengths; North Mini Code does not.
A genuine 32B reasoning ('Think') model that generates explicit chain-of-thought: OLMo 3 32B Think — North Mini Code is comparatively weak here — 256K context and modest general-intelligence index trail frontier models
Fully open under Apache 2.0 - free to self-host: OLMo 3 32B Think — OLMo 3 32B Think lists fully open under Apache 2.0 - free to self-host among its strengths; North Mini Code does not.
Largest single-prompt input: North Mini Code — Its 256K window is about 3.9× larger than OLMo 3 32B Think's 65K, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: North Mini Code — Larger 256K window fits more in one prompt.
Anyone whose priority is agentic software engineering, code generation, and terminal tasks: North Mini Code — It is specifically built for that.
Anyone whose priority is the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights: OLMo 3 32B Think — That is its strongest area.
An enterprise with regional data-residency rules: OLMo 3 32B Think or North Mini Code — Origin (Canada vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
OLMo 3 32B Think: where it fits
Allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. Released November 20, 2025 by Allen Institute for AI, it is built for the most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights, a genuine 32B reasoning ('Think') model that generates explicit chain-of-thought, fully open under Apache 2.0 - free to self-host, and built specifically to let researchers reproduce and audit exactly how the model was trained.
Its trade-offs: a 65K context window is modest next to million-token frontier models, prioritizes full openness and reproducibility over topping raw capability leaderboards, and 32B scale trails much larger frontier and open MoE models on general benchmarks. 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." North Mini Code (Canada) and OLMo 3 32B Think (US) 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.
Frequently asked questions
Is North Mini Code or OLMo 3 32B Think better for coding?
Public SWE-Bench figures are not available for OLMo 3 32B Think, so the honest test is your own repository — run an identical real bug through both. By design, North Mini Code leans toward agentic software engineering, code generation, and terminal tasks while OLMo 3 32B Think leans toward the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, North Mini Code or OLMo 3 32B Think?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
North Mini Code — 256K vs 65K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both North Mini Code and OLMo 3 32B Think together?
Yes — a multi-model platform like LumiChats gives you North Mini Code, OLMo 3 32B Think 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, North Mini Code or OLMo 3 32B Think?
North Mini Code — released June 9, 2026, about 7 months after OLMo 3 32B Think.
North Mini Code vs OLMo 3 32B Think
Cohere · Canada | Allen Institute for AI · US · Updated June 2026
Quick verdict
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. Pick OLMo 3 32B Think for the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights or a genuine 32b reasoning ('think') model that generates explicit chain-of-thought.
North Mini Code (Cohere, Canada) and OLMo 3 32B Think (Allen Institute for AI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. OLMo 3 32B Think is allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. 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: North Mini Code holds 3.9× more — 256K (~384 pages) vs 65K (~98 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 7 months (released June 9, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a Canada-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
North Mini Code
OLMo 3 32B Think
Provider
Cohere (Canada)
Allen Institute for AI (US)
Released
June 9, 2026
November 20, 2025
Context window
256K (~384 pages)
65K (~98 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
67.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
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
North Mini Code lists high throughput (up to 2.8x Devstral Small 2) at low latency among its strengths; OLMo 3 32B Think does not.
The most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights
OLMo 3 32B Think
OLMo 3 32B Think lists the most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights among its strengths; North Mini Code does not.
A genuine 32B reasoning ('Think') model that generates explicit chain-of-thought
OLMo 3 32B Think
North Mini Code is comparatively weak here — 256K context and modest general-intelligence index trail frontier models
Fully open under Apache 2.0 - free to self-host
OLMo 3 32B Think
OLMo 3 32B Think lists fully open under Apache 2.0 - free to self-host among its strengths; North Mini Code does not.
Largest single-prompt input
North Mini Code
Its 256K window is about 3.9× larger than OLMo 3 32B Think's 65K, fitting roughly 384 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ North Mini Code
Larger 256K window fits more in one prompt.
Anyone whose priority is agentic software engineering, code generation, and terminal tasks
→ North Mini Code
It is specifically built for that.
Anyone whose priority is the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights
→ OLMo 3 32B Think
That is its strongest area.
An enterprise with regional data-residency rules
→ OLMo 3 32B Think or North Mini Code
Origin (Canada vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
OLMo 3 32B Think: where it fits
Allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. Released November 20, 2025 by Allen Institute for AI, it is built for the most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights, a genuine 32B reasoning ('Think') model that generates explicit chain-of-thought, fully open under Apache 2.0 - free to self-host, and built specifically to let researchers reproduce and audit exactly how the model was trained.
Its trade-offs: a 65K context window is modest next to million-token frontier models, prioritizes full openness and reproducibility over topping raw capability leaderboards, and 32B scale trails much larger frontier and open MoE models on general benchmarks. 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." North Mini Code (Canada) and OLMo 3 32B Think (US) 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 North Mini Code and OLMo 3 32B Think 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 North Mini Code or OLMo 3 32B Think better for coding?
Public SWE-Bench figures are not available for OLMo 3 32B Think, so the honest test is your own repository — run an identical real bug through both. By design, North Mini Code leans toward agentic software engineering, code generation, and terminal tasks while OLMo 3 32B Think leans toward the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, North Mini Code or OLMo 3 32B Think?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
North Mini Code — 256K vs 65K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both North Mini Code and OLMo 3 32B Think together?
Yes — a multi-model platform like LumiChats gives you North Mini Code, OLMo 3 32B Think 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, North Mini Code or OLMo 3 32B Think?
North Mini Code — released June 9, 2026, about 7 months after OLMo 3 32B Think.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.