Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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.
GLM 5 (Z.ai) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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
Context window: North Mini Code holds 1.3× more — 256K (~384 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Coding: GLM 5 leads SWE-Bench Verified by 10.2 points (77.8% vs 67.6%) — a real edge on hard, real-world software tasks.
Recency: North Mini Code is the newer model by about 4 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GLM 5
North Mini Code
Provider
Z.ai (China)
Cohere (Global)
Released
February 11, 2026
June 9, 2026
Context window
200K (~300 pages)
256K (~384 pages)
Price (in/out)
$1/$3.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
77.8%
67.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows: GLM 5 — A core design strength of GLM 5.
Complex systems design and backend reasoning: GLM 5 — A core design strength of GLM 5.
Iterative self-correction on autonomous tasks: GLM 5 — A core design strength of GLM 5.
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 1.3× 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 GLM 5, 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 agentic planning and long-horizon coding workflows: GLM 5 — 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.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 11, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 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
GLM 5 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 — GLM 5 for agentic planning and long-horizon coding workflows, 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 GLM 5 or North Mini Code better for coding?
On SWE-Bench Verified, GLM 5 scores 77.8% and North Mini Code scores 67.6% — GLM 5 has the measurable edge.
Which is cheaper, GLM 5 or North Mini Code?
North Mini Code is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
North Mini Code — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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, GLM 5 or North Mini Code?
North Mini Code — released June 9, 2026, about 4 months after GLM 5.
GLM 5 vs North Mini Code
Z.ai · China | Cohere · Global · Updated June 2026
Quick verdict
Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. 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.
GLM 5 (Z.ai) and North Mini Code (Cohere) are two of the models people most often weigh against each other in 2026. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. 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 1.3× more — 256K (~384 pages) vs 200K (~300 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Coding: GLM 5 leads SWE-Bench Verified by 10.2 points (77.8% vs 67.6%) — a real edge on hard, real-world software tasks.
▸Recency: North Mini Code is the newer model by about 4 months (released June 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GLM 5
North Mini Code
Provider
Z.ai (China)
Cohere (Global)
Released
February 11, 2026
June 9, 2026
Context window
200K (~300 pages)
256K (~384 pages)
Price (in/out)
$1/$3.2 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
77.8%
67.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Agentic planning and long-horizon coding workflows
GLM 5
A core design strength of GLM 5.
Complex systems design and backend reasoning
GLM 5
A core design strength of GLM 5.
Iterative self-correction on autonomous tasks
GLM 5
A core design strength of GLM 5.
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 1.3× 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 GLM 5, 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 agentic planning and long-horizon coding workflows
→ GLM 5
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.
GLM 5: where it fits
Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 11, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.
Its trade-offs are real: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 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
GLM 5 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 — GLM 5 for agentic planning and long-horizon coding workflows, 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 GLM 5 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.
On SWE-Bench Verified, GLM 5 scores 77.8% and North Mini Code scores 67.6% — GLM 5 has the measurable edge.
Which is cheaper, GLM 5 or North Mini Code?
North Mini Code is cheaper — $1/$3.2 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
North Mini Code — 256K vs 200K, about 1.3× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 5 and North Mini Code together?
Yes — a multi-model platform like LumiChats gives you GLM 5, 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, GLM 5 or North Mini Code?
North Mini Code — released June 9, 2026, about 4 months after GLM 5.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.