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?'})Playground
Try out LongCat Flash Thinking 2601 by Meituan. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.
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About LongCat Flash Thinking 2601
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.
What To Consider When Choosing a Provider
- Configuration: 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.
- 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.
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.
Frequently Asked Questions
What is the Re-thinking Mode in LongCat Flash Thinking 2601?
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.
How does 2601 improve on the original LongCat Flash Thinking?
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.
What is noise-resistant tool calling and why does it matter?
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.
What are the key benchmark results for 2601?
τ²-Bench: 88.2; AIME-25: 100.0; IMO-AnswerBench: 86.8; BrowseComp: 73.1. See the technical post for the published tables.
Is LongCat Flash Thinking 2601 open-source?
Yes. Weights and the technical write-up are published in the technical post.
How does 2601 differ from LongCat Flash Chat?
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.