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.
Verification & Sources
- Evidence state
- Recheck due
- Source links
- 4
- Freshness
- Needs recheck: checked July 27, 2026
- Last updated
- July 27, 2026
What this evidence state means
- Definition
- The claim was previously checked, but its review window expired or a material change may have invalidated it.
- Required provenance
- The prior evidence and check date are retained, together with the expiry or change signal that triggered recheck.
- Owner
- Kingy freshness queue owner and assigned editorial reviewer
- Freshness rule
- This is already outside its freshness rule. It must not be presented as current until reviewed against current evidence.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
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
- Cohere launch announcement
- Cohere model documentation
- Official Cohere Labs model card and weights
- Cohere Labs technical introduction and benchmark methodology
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Main competitors
Claude Code, OpenAI Codex, Gemini coding models, Qwen coder models, DeepSeek coder models
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…