NVIDIA Nemotron 3 Super vs North Mini Code
NVIDIA · US | Cohere · Canada · Updated June 2026
Quick verdict
Pick NVIDIA Nemotron 3 Super for high-throughput agentic reasoning (up to 2.2x gpt-oss-120b) or 1m-token context with strong long-context retrieval (91.6% ruler @ 1m). 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.
NVIDIA Nemotron 3 Super (NVIDIA, 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. NVIDIA Nemotron 3 Super is nVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context. 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 context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: NVIDIA Nemotron 3 Super holds 3.9× more — 1M (~1,500 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.
- ▸Coding: North Mini Code leads SWE-Bench Verified by 7.1 points (60.47% vs 67.6%) — a real edge on hard, real-world software tasks.
- ▸Recency: North Mini Code is the newer model by about 3 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 | NVIDIA Nemotron 3 Super | North Mini Code |
|---|---|---|
| Provider | NVIDIA (US) | Cohere (Canada) |
| Released | March 11, 2026 | June 9, 2026 |
| Context window | 1M (~1,500 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, code | text, code |
| SWE-Bench Verified | 60.47% | 67.6% |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
High-throughput agentic reasoning (up to 2.2x GPT-OSS-120B)
NVIDIA Nemotron 3 Super
NVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context — and it carries the larger 1M context.
1M-token context with strong long-context retrieval (91.6% RULER @ 1M)
NVIDIA Nemotron 3 Super
Its 1M window holds about 3.9× more than North Mini Code's 256K in a single prompt.
Strong math reasoning (90.21% AIME 2025)
NVIDIA Nemotron 3 Super
NVIDIA Nemotron 3 Super lists strong math reasoning (90.21% AIME 2025) among its strengths; North Mini Code does not.
Agentic software engineering, code generation, and terminal tasks
North Mini Code
It scores 67.6% on SWE-Bench Verified against NVIDIA Nemotron 3 Super's 60.47% — a 7.1-point edge on real repository work.
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 leads SWE-Bench Verified 67.6% to 60.47%.
High throughput (up to 2.8x Devstral Small 2) at low latency
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.
Largest single-prompt input
NVIDIA Nemotron 3 Super
Its 1M window is about 3.9× larger than North Mini Code's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ NVIDIA Nemotron 3 Super
Larger 1M window fits more in one prompt.
Anyone whose priority is high-throughput agentic reasoning (up to 2.2x gpt-oss-120b)
→ NVIDIA Nemotron 3 Super
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
→ NVIDIA Nemotron 3 Super 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.
NVIDIA Nemotron 3 Super: where it fits
NVIDIA's open 120B-total/12B-active hybrid Mamba-Transformer MoE built for high-throughput agentic reasoning at 1M-token context. Released March 11, 2026 by NVIDIA, it is built for high-throughput agentic reasoning (up to 2.2x GPT-OSS-120B), 1M-token context with strong long-context retrieval (91.6% RULER @ 1M), strong math reasoning (90.21% AIME 2025), and fully open weights, datasets, and recipes for self-hosting.
Its trade-offs are real: text-only; no image, audio, or video input, and requires roughly 8x H100-80GB GPUs to self-host at BF16. 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." NVIDIA Nemotron 3 Super (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 NVIDIA Nemotron 3 Super 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 NVIDIA Nemotron 3 Super or North Mini Code better for coding?
On SWE-Bench Verified, NVIDIA Nemotron 3 Super scores 60.47% and North Mini Code scores 67.6% — North Mini Code has the measurable edge.
Which is cheaper, NVIDIA Nemotron 3 Super or North Mini Code?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
NVIDIA Nemotron 3 Super — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both NVIDIA Nemotron 3 Super and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you NVIDIA Nemotron 3 Super, 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, NVIDIA Nemotron 3 Super or North Mini Code?
North Mini Code — released June 9, 2026, about 3 months after NVIDIA Nemotron 3 Super.
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.