AI Tool Profile
North Mini Code: Open-Weight Coding Model, Specs, and Evaluation
North Mini Code is Cohere’s open-weight mixture-of-experts model for repository-level coding, terminal agents and multi-step software-engineering tasks.
Before you buy
Best for: Teams evaluating a compact-active-footprint coding model in local, sovereign or controlled agent harnesses.
Price: C$0 Apache-2.0 weights; hosted limits and infrastructure extra
Included: North Mini Code released weights and documented coding deployment routes.
Main limits: A free hosted allowance is rate-limited. Repository-scale correctness and self-hosted memory requirements need separate evaluation.
What Kingy showed: Related coverage: North Mini Code: Cohere’s 30B Open-Weight Coding Model. This review does not treat that article as a new test of this exact model, plan or current release.
Buying details checked 2026-10-23 with region and currency stated above. Historical demonstrations and the broader fact ledger keep their own dates.
Check official plans · See the demonstration and disclosure · Compare these workflows
Coverage describes this profile, not a product score or a current price guarantee.
Kingy AI Product Facts
North Mini Code
Current statusUnknown
- Company
- Not yet reviewed
- Primary job
- Not yet reviewed
- Audience
- Not yet reviewed
- Evidence
- Source-backed; coverage expanding
- Latest source check
- October 23, 2026
See all tracked product factsPricing, platforms, dependencies, data claims, regions, and timeline
Sources and checks
- Coverage
- 1 of 87 tracked fields currently verified
- Sources
- 1
Source-check dates apply to the tracked facts. Editorial updates elsewhere on this page may have a different date.
Plans and pricing units
Not yet structured in this Kingy record.
Where it runs
- Availability
- Not yet reviewed
Stack and integrations
- Model / provider dependencies
- Not yet reviewed
- Integrations
- Not yet reviewed
Data and regions
- Vendor data-use claim
- Not yet reviewed
- Vendor retention claim
- Not yet reviewed
- Regions
- Not yet reviewed
Launch and latest material update
- Launch
- Not yet reviewed
- Latest material update
- Not yet reviewed
Review history
- North Mini Code: Open-Weight Coding Model, Specs, and Evaluation — Product Facts updated
Kingy published an approved change to this Product Facts record.
View published revision
Who should consider it
Teams evaluating a compact-active-footprint coding model in local, sovereign or controlled agent harnesses. North Mini Code is Cohere’s open-weight mixture-of-experts model for repository-level coding, terminal agents and multi-step software-engineering tasks.
Plan, price and billing scope
C$0 Apache-2.0 weights; hosted limits and infrastructure extra. North Mini Code released weights and documented coding deployment routes. Prices use the currency and billing term stated here. Where a numeric rate, region or current entitlement could not be verified, it remains explicit; no checkout was completed.
Limits that affect the decision
A free hosted allowance is rate-limited. Repository-scale correctness and self-hosted memory requirements need separate evaluation.
What the existing evidence shows
Related coverage: North Mini Code: Cohere’s 30B Open-Weight Coding Model. This review does not treat that article as a new test of this exact model, plan or current release. Product coverage; original publication date and evidence scope retained. Read the related article. Open the previously linked demo.
Sources and buying-review date
Buying review completed October 23, 2026. This is a source-based buying assessment. No paid product test, new generation, checkout or performance benchmark was performed. Vendor descriptions and historical examples remain distinct from independently measured results. The date does not renew the broader canonical fact ledger.
- Official source: huggingface.co
- Official source: docs.cohere.com
- Official source: cohere.com
- Official source: huggingface.co
- Official source: huggingface.co
- Related Kingy coverage
Check the official offer · Compare related options
Earlier profile, sources and dates
This older record retains its original scope. Current buying qualifications above take precedence where terms have changed.
Kingy verdict: North Mini Code is notable because it combines open Apache 2.0 weights, a 30-billion-parameter mixture-of-experts architecture with 3 billion active parameters, and explicit training for agentic software engineering. The small active count can improve inference economics, but it does not reduce the stored model to 3 billion parameters or remove the need to test a complete serving stack.
What North Mini Code is
Cohere released North Mini Code on June 9, 2026 as the first model in its North family. The official model card lists 30 billion total parameters, 3 billion active parameters per token, Apache 2.0 licensing and a text-focused coding-agent design. Cohere’s documentation lists a 256K-token context window, up to 64K output tokens and the API identifier north-mini-code-1-0.
The model is designed for repository-level changes, terminal agents and software-engineering harnesses. Cohere says its training spans multiple agent scaffolds rather than one fixed interface. That is useful positioning, but it does not guarantee identical performance across an organization’s tools, prompts, containers or permission model.
Access and cost
The weights are available through the official Cohere Labs Hugging Face repository. Cohere’s current documentation also says trial and production API keys can use the model free until applicable rate limits are reached, while production deployment is available through Model Vault. “Free” therefore describes the documented API meter or the downloadable license, not hardware, engineering, support, observability or security cost.
Local buyers should size memory against the full 30-billion-parameter weight set, chosen precision, KV cache, context length and serving concurrency. Three billion active parameters describes routing during inference; it is not a complete capacity plan.
How to evaluate it
Run the same repository task in at least two harnesses. Require the model to inspect the codebase, propose a plan, edit multiple files, run tests, recover from one failed command and provide a reviewable diff. Measure task completion, valid tool calls, test pass rate, unnecessary churn, time to first accepted patch, total generated tokens and peak serving memory. Add an adversarial case that checks whether the agent respects file and command boundaries.
Cohere and Cohere Labs publish benchmark results and methodology notes, including cautions about harness differences. Kingy did not reproduce those evaluations. The defensible comparison is therefore a fixed local task pack under the buyer’s actual harness and hardware, with provider scores retained as context rather than treated as an independent verdict.
Primary sources
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Main competitors
Claude Code, OpenAI Codex, Gemini coding models, Qwen coder models, DeepSeek coder models
Alternatives
Tool Links
Related Kingy Links
Launch History
North Mini Code open-source coding model
Cohere announced North Mini Code, a small open-source agentic coding model for developers and coding agents.
- Launch readiness
- 7.7 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-06-12.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
Cohere released North Mini Code, a small open-weight agentic coding model with downloadable weights on Hugging Face and an available API (cohere.com). Developers, AI…