LongCat 2.0 Online respects your privacy. This policy explains how we handle information when you use longcat20.online.
We may collect account information, contact details, usage events, support messages, and technical logs needed to operate and secure the service.
We use information to provide the product, respond to support requests, improve reliability, prevent abuse, and communicate service updates.
For privacy questions, contact privacy@longcat20.online.
Source-based LongCat 2.0 guide
This privacy text applies to the website at longcat20.online. This site may process account data, contact messages, usage logs, authentication events, and technical diagnostics. It uses that information to operate the site, secure the service, and respond to user requests.
This site may include model research notes, source links, and user-submitted workflow notes. It should not be used to submit secrets, private repository credentials, payment credentials, or confidential production data unless a future feature explicitly provides appropriate controls. This site treats privacy as a product requirement.
This site may use service providers for hosting, analytics, authentication, email, storage, and payments. It does not need every provider enabled during the initial release. It only uses provider data as needed to deliver the site and maintain reliability.
This site may log technical data such as request metadata, browser information, error traces, and security signals. It uses those logs to diagnose failures and prevent abuse. It does not present those logs as model training data for the official LongCat 2.0 project.
This site links to external LongCat 2.0 sources. This site cannot control the privacy practices of those external pages. Users should review the policies of official source sites, model pages, reporting sites, and public discussion platforms separately.
Privacy questions can be sent to privacy@longcat20.online. Support questions can be sent to support@longcat20.online. This site will update this policy when product features change in a way that changes data handling.
LongCat 2.0 privacy context is simple: LongCat 2.0 Online is a website about LongCat 2.0, not the official LongCat 2.0 training system. LongCat 2.0 source review should not require users to submit private data.
LongCat 2.0 Online keeps LongCat 2.0 research separate from user account information. LongCat 2.0 content can be read publicly, while private account features should use ordinary security controls.
LongCat 2.0 privacy wording stays close to LongCat 2.0 content and LongCat 2.0 source verification.
LongCat 2.0 is described by its official release material as a 1.6 trillion parameter mixture-of-experts model. The model uses dynamic activation rather than a fixed active-parameter budget. It is reported with about 33 billion to 56 billion active parameters, with approximately 48 billion active parameters on average across contexts.
The release is presented as an open-source model under the MIT License. It is positioned for agentic coding, agentic search, and long-context reasoning. The model is therefore useful to track as both a model release and a practical reference point for teams evaluating open model workflows.
The model is reported with a native one million token context window. It uses Large-scale Sparse Attention, described as LSA, to support long-context use. The model is therefore relevant for teams that need to review repositories, documents, logs, benchmark notes, and implementation traces together.
The architecture uses a Shortcut-connected MoE approach, described as ScMoE in the release material. It also uses Multi-head Output Proportion Decoupling, described as MOPD. The model is presented as a sparse model that balances activation cost, capacity, and long-context behavior.
The model was trained on more than 30 trillion tokens according to the official release material. Its training is also described as using a 50,000-card domestic compute cluster. The model is therefore discussed not only as a model artifact, but also as an infrastructure milestone.
The release reports 59.5 on SWE-bench Pro. LongCat 2.0 is also reported at 70.8 on Terminal-Bench 2.1. The model should not be reduced to two numbers, but those reported benchmarks explain why coding-agent evaluators are paying attention.
The release reports 78.8 on RWSearch, 73.2 on FORTE, and 79.9 on BrowseComp in official release material. The model is therefore presented with agentic search and browsing benchmarks alongside coding benchmarks. This site keeps those figures separated from independent interpretation.
LongCat 2.0 Online is independent from the official LongCat project. This site links to source material and summarizes public facts for readers. This site does not claim to be the official LongCat 2.0 release page, repository, benchmark owner, or model provider.
The release appears in public discussion because it combines large sparse capacity with a smaller active computation path. It is not the first MoE model, but the reported 1.6T total parameter scale makes LongCat 2.0 important for readers comparing open releases, coding benchmarks, and inference tradeoffs.
Official material emphasizes agentic coding, agentic search, and long-context operation together. This site repeats that grouping because it is central to the public positioning. The model should therefore be compared with models and systems designed for tool use, repository work, and multi-step reasoning.
Benchmark references should be read with care. Reported scores describe performance on named test sets, not every possible software task. This site encourages readers to treat public scores as starting points for evaluation rather than as a substitute for local testing.
Source links matter because model information changes over time. This site uses source links so readers can verify release details, benchmark values, license statements, and community reactions. This site should be updated when official LongCat 2.0 material changes.
This site avoids unsupported claims about access, pricing, hosted inference, or private integrations. This site describes LongCat 2.0 with public facts first. This site can add product features later, but the content baseline should remain tied to verifiable LongCat 2.0 sources.
For search clarity, LongCat 2.0 Online keeps the exact phrase LongCat 2.0 on important factual references. LongCat 2.0 is the model name readers are searching for, and LongCat 2.0 should appear where the page states model size, context length, license, and reported benchmarks.
LongCat 2.0 Online uses the phrase LongCat 2.0 carefully rather than stuffing it into every sentence. LongCat 2.0 still appears often enough for topical relevance, while surrounding sentences use normal references such as the model, the release, the architecture, and this site.
The practical question is how LongCat 2.0 facts help a reader decide what to test. LongCat 2.0 Online answers that question by connecting LongCat 2.0 public data to evaluation planning, source review, and agentic coding workflow design.
LongCat 2.0 is the central entity on this site. LongCat 2.0 facts are repeated where they identify the model, not where ordinary prose can use a pronoun. LongCat 2.0 density is therefore intentional but moderated.
LongCat 2.0 appears in architecture notes, benchmark notes, and license notes. LongCat 2.0 also appears in source-review language because source verification is part of the page purpose. LongCat 2.0 remains the exact keyword for the topic.
LongCat 2.0 Online keeps LongCat 2.0 wording near claims that readers may want to verify. LongCat 2.0 wording is less useful in generic sentences, so those sentences use shorter references. LongCat 2.0 content should read naturally.
LongCat 2.0 is the exact model phrase used for SEO checks. LongCat 2.0 appears here to identify the topic, and LongCat 2.0 remains tied to factual statements.