AI Tool Profile
Jan
Jan is an open-source local AI assistant for running and chatting with models on a personal computer.
Before you buy
Buying details reviewed 2026-10-23. Price, included amounts and access are dated observations; check the official source for current terms. This date does not review the whole fact ledger or renew historical demonstrations.
Best for: Local-first users, developers, and open-source maintainers who want a desktop AI assistant.
Price: C$0 open-source app; model/compute costs separate
Included: A local-first desktop assistant with model and provider connections.
Main limits: Model licenses, hardware requirements and optional hosted-provider charges are separate from the application license. Test the chosen local model on your device.
What Kingy showed: Related coverage: How to Make Money Using AI on YouTube in 2026 (The Complete Guide). This review does not treat that article as a new test of this exact model, plan or current release.
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
Jan
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
- Jan — Product Facts updated
Kingy published an approved change to this Product Facts record.
View published revision
Who should consider it
Local-first users, developers, and open-source maintainers who want a desktop AI assistant. Jan is an open-source local AI assistant for running and chatting with models on a personal computer.
Plan, price and billing scope
C$0 open-source app; model/compute costs separate. A local-first desktop assistant with model and provider connections. 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
Model licenses, hardware requirements and optional hosted-provider charges are separate from the application license. Test the chosen local model on your device.
What the existing evidence shows
Related coverage: How to Make Money Using AI on YouTube in 2026 (The Complete Guide). This review does not treat that article as a new test of this exact model, plan or current release. Related workflow reading; not a demonstration of this exact product. Read the related article.
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.
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.
Jan is an open-source local AI assistant for running and chatting with models on a personal computer.
Alternatives
Tool Links
Related Kingy Links
Launch History
Jan v0.8.3 release
Jan released v0.8.3 with fixes across MCP browser-port handling, backend installs, media handling, and chat rendering.
- Launch readiness
- 7.4 / 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: 2026-06-24.
- 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.
Jan's v0.8.3 release on GitHub fixes MCP browser-port handling, backend installs, media handling, and chat rendering in its open-source, local-first desktop assistant (github.com/janhq/jan). It…