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 Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
North Mini Code (Cohere, Canada) and Qwen3.6 35B A3B (Alibaba, China) 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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. They diverge most on context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Context window: 256K vs 256K — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Coding: Qwen3.6 35B A3B leads SWE-Bench Verified by 5.8 points (67.6% vs 73.4%) — a real edge on hard, real-world software tasks.
Recency: North Mini Code is the newer model by about 54 days (released June 9, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a Canada-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
North Mini Code
Qwen3.6 35B A3B
Provider
Cohere (Canada)
Alibaba (China)
Released
June 9, 2026
April 16, 2026
Context window
256K (~384 pages)
256K (~393 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, image, code
SWE-Bench Verified
67.6%
73.4%
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 is the newer of the two.
Efficient sparse MoE — 3B active of 30B, runs on a single H100: North Mini Code — North Mini Code lists efficient sparse MoE — 3B active of 30B, runs on a single H100 among its strengths; Qwen3.6 35B A3B does not.
High throughput (up to 2.8x Devstral Small 2) at low latency: North Mini Code — Qwen3.6 35B A3B is comparatively weak here — loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost: Qwen3.6 35B A3B — A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it leads SWE-Bench Verified 73.4% to 67.6%.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU: Qwen3.6 35B A3B — Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; North Mini Code does not.
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN: Qwen3.6 35B A3B — North Mini Code is comparatively weak here — 256K context and modest general-intelligence index trail frontier models
Which should you pick?
Someone analysing very long documents or codebases: Qwen3.6 35B A3B — 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 extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost: Qwen3.6 35B A3B — That is its strongest area.
An enterprise with regional data-residency rules: Qwen3.6 35B A3B or North Mini Code — Origin (Canada vs China) 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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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 Qwen3.6 35B A3B (China) 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 Qwen3.6 35B A3B better for coding?
On SWE-Bench Verified, North Mini Code scores 67.6% and Qwen3.6 35B A3B scores 73.4% — Qwen3.6 35B A3B has the measurable edge.
Which is cheaper, North Mini Code or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Effectively neither — 256K vs 256K is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both North Mini Code and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you North Mini Code, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B?
North Mini Code — released June 9, 2026, about 54 days after Qwen3.6 35B A3B.
North Mini Code vs Qwen3.6 35B A3B
Cohere · Canada | Alibaba · China · 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 Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.
North Mini Code (Cohere, Canada) and Qwen3.6 35B A3B (Alibaba, China) 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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. They diverge most on context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: 256K vs 256K — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Coding: Qwen3.6 35B A3B leads SWE-Bench Verified by 5.8 points (67.6% vs 73.4%) — a real edge on hard, real-world software tasks.
▸Recency: North Mini Code is the newer model by about 54 days (released June 9, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a Canada-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
North Mini Code
Qwen3.6 35B A3B
Provider
Cohere (Canada)
Alibaba (China)
Released
June 9, 2026
April 16, 2026
Context window
256K (~384 pages)
256K (~393 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, image, code
SWE-Bench Verified
67.6%
73.4%
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 is the newer of the two.
Efficient sparse MoE — 3B active of 30B, runs on a single H100
North Mini Code
North Mini Code lists efficient sparse MoE — 3B active of 30B, runs on a single H100 among its strengths; Qwen3.6 35B A3B does not.
High throughput (up to 2.8x Devstral Small 2) at low latency
North Mini Code
Qwen3.6 35B A3B is comparatively weak here — loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost
Qwen3.6 35B A3B
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware — and it leads SWE-Bench Verified 73.4% to 67.6%.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU
Qwen3.6 35B A3B
Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; North Mini Code does not.
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN
Qwen3.6 35B A3B
North Mini Code is comparatively weak here — 256K context and modest general-intelligence index trail frontier models
Which should you pick?
Someone analysing very long documents or codebases
→ Qwen3.6 35B A3B
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 extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost
→ Qwen3.6 35B A3B
That is its strongest area.
An enterprise with regional data-residency rules
→ Qwen3.6 35B A3B or North Mini Code
Origin (Canada vs China) 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.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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 Qwen3.6 35B A3B (China) 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 Qwen3.6 35B A3B 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 Qwen3.6 35B A3B better for coding?
On SWE-Bench Verified, North Mini Code scores 67.6% and Qwen3.6 35B A3B scores 73.4% — Qwen3.6 35B A3B has the measurable edge.
Which is cheaper, North Mini Code or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Effectively neither — 256K vs 256K is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both North Mini Code and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you North Mini Code, Qwen3.6 35B A3B 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 Qwen3.6 35B A3B?
North Mini Code — released June 9, 2026, about 54 days after Qwen3.6 35B A3B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.