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Use in Tabbit LongCat 2.0
LongCat 2.0 · Model overview
A compact view of reviewed task guides, public evaluations, and evidence boundaries. Client access still depends on your current account.
Model access and permissions must be checked in the current Tabbit account.
Extract structured data, build a visual prototype, or start a coding task.
The LongCat Claude Code guide configures a compatible endpoint and keeps the first task in a disposable worktree.
The official model card’s chat template and tool-call examples are converted into a local inference configuration check.
The official LongCat integration guide configures a named client and keeps the first run observable and reversible.
The SGLang cookbook is a deployment recipe for LongCat-2.0 FP8; detail emphasizes GPU topology and health checks.
Public results use different versions, tiers, and harnesses; unknown values stay unknown.
The OpenRouter page provides a third-party view beyond the official figures: LongCat-2.0 is listed at $0.30/$1.20 per 1M tokens (with a 60% discount at collection time), while the actual weighted transaction price for input was only $0.03872/M (88.9% cache-hit rate); throughput was P50 29 tok/s, three-day availability 99.93%, and tool-call error rate 0.90%, with real traffic mainly coming from Hermes Agent (7.77B tokens) and Claude Code (3.31B tokens).
The official model card is the primary authoritative source for judging LongCat-2.0's suitable tasks: it scores 59.5 on SWE-bench Pro, ahead of GPT-5.5 (58.6) and Gemini 3.1 Pro (54.2), and reaches 70.8 on Terminal-Bench 2.1. However, it trails GPT-5.5 and Claude Opus 4.8 on several benchmarks including BrowseComp, GPQA, and IFEval—in short, it is strong at coding and agent tasks, but not a leader in retrieval and general reasoning.
The official technical blog provides the complete technical foundation for LongCat-2.0 (LSA sparse attention, N-gram Embedding, 6D parallel training on domestic compute, and prefill-decode disaggregated deployment), making it useful for assessing the model's intended long-context and Agent capabilities, as well as reproducing the official benchmarks and deployment path.
LongCat
Explore sourced prompt guides, evaluations, and community reports for LongCat 2.0—then use the model directly in Tabbit.