LongCat-2.0
LongCat-2.0 is a next-generation, open-source MoE (Mixture of Experts) model with 1.6T total parameters, designed for agentic coding and featuring native 1M token context support. It achieves state-of-the-art performance on software engineering and terminal benchmarks, and is available on longcat.ai and OpenRouter.
Key features
- ✓1.6T total parameters with dynamic activation of 33B–56B per token
- ✓Native 1M token context with Linear-complexity Sparse Attention (LSA)
- ✓Zero-computation experts + ScMoE for token-level dynamic compute
- ✓MOPD (Multi-Teacher On-Policy Distill) multi-expert fusion
- ✓SWE-bench Pro 59.5 – leads Gemini 3.1 Pro, GPT-5.5, Claude Opus 4.6
- ✓Terminal-Bench 70.8 – stable execution and error recovery in real terminal environments
- ✓Open-source on longcat.ai and OpenRouter – top 3 globally by call volume
Use cases
- →Agentic coding and software engineering
- →Complex terminal-based automation and error recovery
- →Long-context document understanding and code analysis
- →Multi-expert reasoning for challenging technical tasks
Pros
- +Industry-leading benchmark scores on SWE-bench Pro and Terminal-Bench
- +Efficient dynamic compute – simple tasks cost near zero compute
- +Fully open-source with active community support
Cons
- −Requires 50,000-card domestic compute cluster for full training (not accessible to all)
- −Model size may limit deployment on consumer hardware
Positioning
- Core value: Industry-first trillion-parameter open-source model that delivers state-of-the-art agentic coding with dynamic compute and native 1M context.
- Ideal for: Developers, AI researchers, software engineering teams
- Product type: Web app, API (OpenRouter)
Frequently asked questions
What is LongCat-2.0?
LongCat-2.0 is a 1.6T-parameter open-source MoE model designed for agentic coding, with native 1M context support and dynamic compute per token.
How does LongCat-2.0 achieve dynamic compute?
It uses Zero-computation experts + ScMoE: simple tokens cost nothing, while complex tokens get more resources, optimizing efficiency.
What benchmarks does LongCat-2.0 excel at?
It scores 59.5 on SWE-bench Pro and 70.8 on Terminal-Bench, outperforming models like Gemini 3.1 Pro and GPT-5.5.
Is LongCat-2.0 open-source?
Yes, it is open-source and available on longcat.ai and OpenRouter, ranking top 3 globally by call volume.
What is MOPD in LongCat-2.0?
MOPD stands for Multi-Teacher On-Policy Distill, a training technique that fuses Agent, Reasoning, and Interaction experts into a unified model.
Can I try LongCat-2.0 online?
Yes, it can be used online at longcat.ai and via OpenRouter.
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