Meituan launches LongCat 2.0: 1.6T parameter MoE model with 1M+ context window at $0.30 per 1M input tokens
Meituan has released LongCat 2.0, a sparse mixture-of-experts language model with 48 billion active parameters out of 1.6 trillion total. The model features a 1,049,000 token context window and costs $0.30 per 1M input tokens and $1.20 per 1M output tokens.
LongCat 2.0 — Quick Specs
Meituan launches LongCat 2.0: 1.6T parameter MoE model with 1M+ context window
Meituan has released LongCat 2.0, a sparse mixture-of-experts (MoE) language model with 48 billion active parameters out of 1.6 trillion total parameters. The model is now available through OpenRouter's API.
Model specifications
LongCat 2.0 features a 1,049,000 token context window, placing it among the largest context windows available in production models. The sparse MoE architecture activates only 48 billion parameters per inference call while maintaining access to the full 1.6 trillion parameter base.
Pricing is set at $0.30 per 1 million input tokens and $1.20 per 1 million output tokens, making it competitively priced for long-context applications.
Target use cases
According to Meituan, LongCat 2.0 is designed for:
- Coding tasks and repository-level code changes
- Long-horizon problem solving
- Agentic workflows requiring extended context
The model operates as a text-to-text model and is accessed via the model ID meituan/longcat-2.0 on OpenRouter.
Technical architecture
The sparse MoE design allows LongCat 2.0 to maintain efficiency despite its massive total parameter count. By activating only 3% of its parameters (48B out of 1.6T) during inference, the model aims to balance computational cost with capability.
No benchmark scores have been publicly disclosed at this time. Training data cutoff date and additional technical details remain unannounced.
What this means
LongCat 2.0's million-token context window positions it for applications requiring extensive code repository analysis or long-document processing. The sparse MoE architecture suggests Meituan is following the industry trend of scaling total parameters while keeping active parameters manageable. At $0.30 per 1M input tokens, it's priced below several competitors offering similar context lengths, though real-world performance data will determine whether the pricing advantage translates to practical value. The model's availability through OpenRouter provides immediate API access without requiring direct integration with Meituan's infrastructure.
Related Articles
InclusionAI Releases Ling 3.0 Flash Fin, a Finance-Focused MoE Model with 5.1B Active Parameters
InclusionAI has released Ling 3.0 Flash Fin, a finance-specialized mixture-of-experts model built on Ling 3.0 Flash. The model activates 5.1B of its 124B total parameters and targets long-horizon investment planning tasks while retaining general reasoning, coding, and math capabilities.
Meta Releases Muse Spark 1.3 Contributor, a Low-Cost Multimodal Reasoning Model With 1M Context Window
Meta has released Muse Spark 1.3 Contributor, described as the cost-efficient contributor tier of its multimodal reasoning model line. The model offers a 1 million token context window at $0.10 per 1M input tokens and $0.20 per 1M output tokens, targeting experimentation and early-stage agentic workflows.
Meta Releases Muse Spark 1.3, a Free Multimodal Reasoning Model with 1M-Token Context
Meta has released Muse Spark 1.3, a multimodal reasoning model with a 1M-token context window, listed as free on OpenRouter. The model targets long-running agentic, multi-agent, and coding workflows, though audio input support remains incomplete.
Google Lists Gemini 3.8 Flash on OpenRouter With 1M-Token Context, September 2026 Release Date
Google's Gemini 3.8 Flash has surfaced on OpenRouter with a 1-million-token context window and discounted pricing of $0.75 per 1M input tokens and $3.75 per 1M output tokens. Google has not issued a separate public announcement, and the listed release date of September 2, 2026 is unusually far out, leaving key details unconfirmed.
Comments
Loading...