DeepSeek V3.2 vs North Mini Code
DeepSeek · China | Cohere · Canada · Updated June 2026
Quick verdict
Pick DeepSeek V3.2 for long-context efficiency via deepseek sparse attention (dsa) or agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes). 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 V3.2 (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 V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. 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
- ▸Context window: North Mini Code holds 2× more — 256K (~384 pages) vs 131K (~197 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Coding: DeepSeek V3.2 leads SWE-Bench Verified by 5.5 points (73.1% vs 67.6%) — a real edge on hard, real-world software tasks.
- ▸Recency: North Mini Code is the newer model by about 6 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 V3.2 | North Mini Code |
|---|---|---|
| Provider | DeepSeek (China) | Cohere (Canada) |
| Released | December 1, 2025 | June 9, 2026 |
| Context window | 131K (~197 pages) | 256K (~384 pages) |
| Price (in/out) | $0.28/$0.42 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text, code |
| SWE-Bench Verified | 73.1% | 67.6% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
North Mini Code is comparatively weak here — 256K context and modest general-intelligence index trail frontier models
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
It scores 73.1% on SWE-Bench Verified against North Mini Code's 67.6% — a 5.5-point edge on real repository work.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference — and it leads SWE-Bench Verified 73.1% to 67.6%.
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; DeepSeek V3.2 does not.
Lowest cost at scale
North Mini Code
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V3.2's $0.28/$0.42 per 1M tokens.
Largest single-prompt input
North Mini Code
Its 256K window is about 2× larger than DeepSeek V3.2's 131K, 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 V3.2, 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 long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
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 V3.2
Origin (China vs Canada) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs are real: superseded - no longer listed on DeepSeek's current pricing page as of August 2026; DeepSeek V4-Flash/V4-Pro are the current models, text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 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 V3.2 (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 V3.2 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 V3.2 or North Mini Code better for coding?
On SWE-Bench Verified, DeepSeek V3.2 scores 73.1% and North Mini Code scores 67.6% — DeepSeek V3.2 has the measurable edge.
Which is cheaper, DeepSeek V3.2 or North Mini Code?
North Mini Code is cheaper — $0.28/$0.42 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
North Mini Code — 256K vs 131K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V3.2 and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V3.2, 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 V3.2 or North Mini Code?
North Mini Code — released June 9, 2026, about 6 months after DeepSeek V3.2.
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.