LongCat Flash Thinking 2601 is Meituan's N/A upgrade to the reasoning model series. It introduces parallel multi-path thinking, noise-resistant tool calling, and a τ²-Bench score of 88.2 with an AIME-25 score of 100.0.
import { streamText } from 'ai'
const result = streamText({ model: 'meituan/longcat-flash-thinking-2601', prompt: 'Why is the sky blue?'})LongCat Flash Thinking 2601 builds on the original Flash Thinking model with targeted improvements in three areas: thinking mode diversity, real-world robustness, and agent search capability. The version name reflects its January 2026 (2601) update cadence.
The main architectural change is the Re-thinking Mode. It activates multiple parallel reasoning paths simultaneously before a summary-synthesis stage consolidates them into a final answer. Different paths may identify different intermediate results, and the synthesis stage selects or combines conclusions across paths. LongCat Flash Thinking 2601 is the first open-source model to make this mode publicly available.
Noise resistance is a specific training focus in 2601. Meituan trained the model with injected multi-class noise simulating real-world API failure conditions: malformed responses, incomplete data, and service interruptions. This makes it more reliable in agentic tool-use deployments where the model encounters degraded or unreliable external services mid-task. Benchmark results reflect this focus: τ²-Bench (agentic tool use) improved to 88.2, AIME-25 (competitive mathematics) reached 100.0, IMO-AnswerBench scored 86.8, and BrowseComp (agent search) reached 73.1. Details are in the technical post.