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LongCat Flash Thinking

LongCat Flash Thinking is Meituan's 560B MoE reasoning model. It combines Lean4 formal proof capability, agentic tool use, and an ARC-AGI score of 50.3 in a single architecture.

ReasoningTool Use
index.ts
import { streamText } from 'ai'


const result = streamText({
  model: 'meituan/longcat-flash-thinking',
  prompt: 'Why is the sky blue?'
})
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About LongCat Flash Thinking

LongCat Flash Thinking unifies deep thinking, tool calling, and formal mathematical reasoning in a single architecture. The reasoning process itself can invoke tools mid-thought rather than thinking first and calling tools as a separate phase.

The Agentic Reasoning Framework implements dual-path inference. The model evaluates each task and autonomously chooses between direct reasoning and tool-augmented reasoning based on complexity. Callers don't configure this routing. The model applies tool invocation where it helps and direct reasoning where it doesn't, without caller overhead.

Benchmarks at release: ARC-AGI 50.3 (abstract pattern reasoning), LiveCodeBench 79.4 (competitive programming), τ²-Bench 74.0 on agent tool use at release (as reported), MiniF2F-test 67.6 pass@1 (formal mathematical proof via Lean4). Meituan reported a 64.5% token efficiency improvement in agentic tool-use settings with 90% task accuracy retention. For methodology and updates, see the technical post.