LongCat Flash Thinking 2601
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?'})What To Consider When Choosing a Provider
Zero Data Retention
AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.Authentication
AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
Parallel multi-path thinking activates multiple simultaneous reasoning paths before synthesizing a final answer. This increases token consumption per response relative to single-path thinking models. Plan per-session cost budgets accordingly.
When to Use LongCat Flash Thinking 2601
Best For
Noise-resistant tool use:
Agentic pipelines operating against unreliable or real-world APIs where robustness matters
Olympiad-level math:
Competitive mathematics and problem solving where AIME/IMO benchmark performance is relevant
Agent search reasoning:
Complex search and information retrieval tasks that need BrowseComp-level reasoning
Parallel thinking paths:
Applications where reasoning diversity improves answer reliability
Consider Alternatives When
Hard latency constraints:
Single-path thinking suffices and response time is the bottleneck
Conversational chat only:
Use case doesn't need extended reasoning (use LongCat Flash Chat)
Cost-sensitive workloads:
Original LongCat Flash Thinking's benchmarks suffice at lower cost
Formal proof priority:
Lean4 verification is the primary requirement and the benchmark differences don't apply
Conclusion
LongCat Flash Thinking 2601 sharpens the original Flash Thinking model's reasoning with parallel multi-path synthesis and noise-resistant tool calling. It reports measurable gains on agent and mathematics benchmarks in Meituan's technical post. For production deployments where reasoning reliability under noisy real-world conditions matters, 2601 documents improvements over its predecessor.
FAQ
Re-thinking Mode activates multiple independent parallel reasoning paths simultaneously. A summary-synthesis stage then consolidates findings from all paths into a final answer. That structure spreads intermediate hypotheses across paths and reduces the risk of a single flawed chain dominating the output.
It adds parallel multi-path reasoning, noise-injected training for robustness, and benchmark gains: τ²-Bench increased to 88.2, AIME-25 reached 100.0, IMO-AnswerBench scored 86.8, and BrowseComp reached 73.1.
It means the model was trained on tool outputs that include failures, malformed payloads, and missing fields, not only clean responses. Meituan used multi-class noise during training to simulate those API conditions. The goal is steadier behavior when agents call unpredictable external services.
τ²-Bench: 88.2; AIME-25: 100.0; IMO-AnswerBench: 86.8; BrowseComp: 73.1. See the technical post for the published tables.
Yes. Weights and the technical write-up are published in the technical post.
Flash Chat is a direct-response conversational model optimized for speed and tool calling without extended thinking. 2601 activates deep reasoning chains, including parallel multi-path synthesis, and suits tasks that need deliberate analysis rather than fast conversational responses.
