OpenReview can be used without paying: No charge shown on the homepage; the site has a donate link. Check the vendor’s pricing page for current limits.

Is OpenReview open source?

No. OpenReview is proprietary software; its source code is not public.

Which platforms does OpenReview support?

OpenReview is available on: Web.

What is OpenReview best for?

Reviews and submissions for ML conferences. In our ranking of machine learning research resources it is number 6 of 10 with a score of 85.5.

What are the best alternatives to OpenReview?

The highest-ranked alternatives on listme.name are arXiv, Hugging Face, Semantic Scholar, Epoch AI.

OpenReview

Open peer review platform hosting conference reviews and submissions

#6 of 10 in best machine learning research resources
Free
Free to use
Web

85.5

Score in best machine learning research resources

Visit openreview.net Alternatives Compare Share

Ranked 6 of 10 in best machine learning research resources

Open peer review platform that hosts submissions and reviews for a long list of machine learning and AI venues, with 171 venues on its index. Its review record is primary for those venues, but coverage depends on which conferences use it.

Best for Reviews and submissions for ML conferences

About OpenReview

OpenReview runs peer review for academic venues and hosts submissions, reviews and discussions. The homepage describes it as open peer review and open source. Its venue index lists 171 venues and includes machine learning and AI conferences such as ICLR, AAAI and ACL, along with a 2026 launch of multi-factor authentication for all users.

Because review happens on the platform for each venue that uses it, the record is primary for those venues only. Venues outside the index are not covered. Researchers who need preprints before review may use arXiv, which is also in this ranking. The homepage includes a donate link and a news section about platform changes.

Key features

Venue index
Peer review workflow
Open source platform
Multi-factor authentication

Strengths

Hosts review for many AI and machine learning venues
Index of 171 venues on one site
Open source according to the homepage

Limits

Coverage depends on which venues use the platform

Where OpenReview ranks

Best machine learning research resources#6of 10 · 85.5
Best for: Reviews and submissions for ML conferences

How OpenReview compares

Product	Score	Price	Platforms	Main content	Developer API	Peer review
#4Epoch AI	86.5	Free	Web	Datasets and reports	Not listed	Not stated
#5Know Your ML *	78.5	Free plan, paid upgrades	Web	Feed, briefs and reports	Not listed	Reports not peer reviewed
#6OpenReview	85.5	Free	Web	Submissions and peer reviews	Not listed	Peer review run on the platform
#7Arena AI	84.5	Free	Web	Model leaderboards	Not listed	Vote-based rankings
#8Google Scholar	83.5	Free	Web	Index of articles, theses and books	Not listed	Mixed; check each source

The full comparison table for best machine learning research resources has all 10 products.

Alternatives to OpenReview

All 9 alternatives

Compare OpenReview

Questions about OpenReview

Is OpenReview free?: OpenReview can be used without paying: No charge shown on the homepage; the site has a donate link. Check the vendor’s pricing page for current limits.
Is OpenReview open source?: No. OpenReview is proprietary software; its source code is not public.
Which platforms does OpenReview support?: OpenReview is available on: Web.
What is OpenReview best for?: Reviews and submissions for ML conferences. In our ranking of machine learning research resources it is number 6 of 10 with a score of 85.5.
What are the best alternatives to OpenReview?: The highest-ranked alternatives on listme.name are arXiv, Hugging Face, Semantic Scholar, Epoch AI.

Show this ranking on your site

If you make or maintain OpenReview, this badge links back to this page and shows its current rank. It updates when the ranking does.

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Made by, or connected to, the publisher of this site or people the publisher works with. Listed on the same criteria as everything else, with the lift described in How we rank and Disclosure.