Terms of Service

These terms govern use of the independent website at longcat20.online. This site provides summaries, links, and workflow-oriented material about LongCat 2.0. It does not replace official LongCat 2.0 documentation, model repositories, licenses, or benchmark reports.

Users should use the site lawfully and responsibly. This content is provided for informational and product-planning purposes. This site does not guarantee that LongCat 2.0 will perform a specific way in every repository, coding task, browser task, or terminal workflow.

This site may reference reported benchmark results such as SWE-bench Pro, Terminal-Bench 2.1, RWSearch, FORTE, and BrowseComp. This site presents those values as reported figures, not as a warranty. Users should run their own evaluations before making production decisions.

Users should not submit unlawful content, credentials, private keys, or data they do not have permission to use. This site may remove access or restrict use if the service is abused. This site may change features as the product develops.

This site may include links to third-party pages. This site is not responsible for the content, uptime, privacy practices, or policies of those external sources. This site links are provided so readers can verify public LongCat 2.0 information.

Support can be reached at support@longcat20.online. Terms may be updated when product features, billing, account systems, or legal requirements change. Users should review the current terms before relying on the service.

LongCat 2.0 terms on this page identify LongCat 2.0 as the subject of the site. LongCat 2.0 references are informational. LongCat 2.0 Online does not grant rights to official LongCat 2.0 assets beyond what public sources state.

LongCat 2.0 evaluation is the user’s responsibility. LongCat 2.0 benchmarks can guide tests, but LongCat 2.0 Online does not promise a particular result in production.

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.