LongCat-Flash-Thinking
A 560B-parameter open-source MoE reasoning model with domain-parallel training, DORA asynchronous RL, and 64.5% token reduction on AIME-25.
Independent third-party resource. Not affiliated with or endorsed by LongCat or Meituan.
Overview
LongCat-Flash-Thinking is a reasoning-optimized variant of the 560-billion-parameter LongCat-Flash MoE model family, released in September 2025. Unlike standard chat models that generate responses in a single pass, Flash-Thinking produces an extended Chain-of-Thought — reasoning through problems step by step before delivering the final answer. The publisher reports an outstanding result: reducing token consumption by 64.5% on AIME-25 compared to standard chain-of-thought approaches, without degrading accuracy. The upgraded Flash-Thinking-2601 variant (January 2026) introduces 'Heavy Thinking' mode that simultaneously explores 8 parallel reasoning paths, then synthesizes the most promising results. Flash-Thinking achieves a perfect score of 100.0 on AIME-25 (with heavy thinking) and 86.8 on IMO-AnswerBench, the current open-source state-of-the-art on that benchmark. It ranks first on BrowseComp (73.1) among all models, indicating strong agentic search capabilities.
Architecture
The core architectural innovation is domain-parallel training: instead of training a single model on a mixed dataset of STEM, code, and agentic tasks, the training process decouples optimization across these domains in parallel, then fuses the resulting expert models into a single nearly Pareto-optimal model. The DORA (Dynamic ORchestration for Asynchronous rollout) reinforcement learning framework supports this by coordinating training across more than 10,000 environments spanning over 20 domains, with a reported threefold training speedup over synchronous RL methods. The Heavy Thinking mode in Flash-Thinking-2601 activates 8 parallel reasoning chains simultaneously, explores divergent solution strategies, then consolidates the results into a final answer — a design the publisher describes as 're-thinking' rather than incremental refinement.
Training
The domain-parallel training scheme uses DORA to coordinate asynchronous RL rollouts across domain-specific environments. Each domain (STEM, Code, Agentic) trains under its own reward signal, then the experts are fused into a unified model. The Flash-Thinking-2601 upgrade added multi-environment robustness training, exposing the model to thousands of diverse environments during RL to reduce tool-use brittleness. The publisher reports that this approach significantly reduced the training cost to adapt the model to new tools compared to fine-tuning from scratch.
Key Benchmarks
Publisher-reported scores. Not independently verified by this site. Descriptions summarize what each benchmark measures.
| Benchmark | Score | What It Measures |
|---|---|---|
| AIME-25 | 100.0 (Heavy Thinking) | American Invitational Mathematics Examination 2025 — 15-question, 3-hour competition requiring advanced mathematical reasoning |
| IMO-AnswerBench | 86.8 (open-source SOTA) | International Mathematical Olympiad answer benchmark — tests formal mathematical reasoning on olympiad-level problems |
| BrowseComp | 73.1 (#1 all models) | Complex web browsing competition — measures ability to navigate, search, and synthesize information across multiple web pages |
| τ²-Bench | 88.2 (open-source SOTA) | Agentic tool-use benchmark across telecom and retail domains — measures real-world tool calling ability |
| LCB (LiveCodeBench) | 82.8 | Live coding benchmark with problems released after the model's training cutoff — measures genuine coding ability, not memorization |
Deployment
LongCat-Flash-Thinking is available via the LongCat API platform with a 256K maximum output limit and 128K context window. The 'Deep Thinking' function (re-thinking mode) is available for free on longcat.ai — the model activates when the deep thinking toggle is selected. Model weights are on HuggingFace (meituan-longcat/LongCat-Flash-Thinking) and GitHub (meituan-longcat/LongCat-Flash-Thinking) under the MIT License. Deployment follows the same SGLang/vLLM configuration as Flash-Chat: minimum one 8-GPU node (8x H20) for FP8, or two 8-GPU nodes (16x H800) for BF16.
Capabilities
Described in the cited primary documentation. Not an independent performance evaluation.
- 560B MoE reasoning model with long Chain-of-Thought.
- Domain-parallel training across STEM, Code, Agentic domains.
- DORA asynchronous RL: >3x speedup over synchronous methods.
- AIME-25 score 100.0 (Heavy Thinking mode); IMO-AnswerBench 86.8.
- 64.5% fewer tokens on AIME-25 without accuracy loss.
- Flash-Thinking-2601 upgrade adds Heavy Thinking and multi-environment robustness training.
Access & License
MIT License.
Documented context window: 128,000 tokens (per the cited publisher API documentation).
Sources
- LongCat-Flash-Thinking (arXiv:2509.18883)
Primary sourcePublished 2025-09-23Accessed 2026-07-27
Domain-parallel training, DORA RL (>3x speedup), 64.5% token reduction on AIME-25. Upgraded variant: Flash-Thinking-2601 (arXiv:2601.16725).
- LongCat-Flash-Thinking on HuggingFace
Primary sourceAccessed 2026-07-27
Model weights. MIT License.
- LongCat-Flash-Thinking GitHub Repository
Primary sourceAccessed 2026-07-27
MIT License. Model configuration and inference code.
FAQ
- Where can the primary source for LongCat-Flash-Thinking be found?
- The official GitHub repository (meituan-longcat) and HuggingFace page contain the primary documentation, model weights, and license for LongCat-Flash-Thinking.
- Is LongCat-Flash-Thinking available through the LongCat API?
- LongCat-Flash-Thinking is documented in the meituan-longcat GitHub organization. Refer to the publisher API documentation at longcat.chat for current API availability.
- Is this page an official LongCat or Meituan page?
- No. This page is published by an independent third-party site. It is not affiliated with, endorsed by, or sponsored by LongCat or Meituan.
- How often is this page updated?
- This page was last verified on 2026-07-27. Content is reviewed when new publisher documentation or model releases become available.
Independent third-party disclosure
This page is published by an independent third-party site. It is not affiliated with, endorsed by, sponsored by, or operated by Meituan, LongCat, or any of their affiliates. The content summarizes publicly-available primary documentation and does not represent the views of any referenced organization.
Last reviewed: 2026-07-27