LongCat-Flash-Chat vs LongCat-Flash-Thinking
LongCat-Flash-Thinking leads the LLM Stats Score 28.7 to 19.8. LongCat-Flash-Chat and LongCat-Flash-Thinking cost the same.
Meituan · Meituan · Updated for 2026
Which is better?
LongCat-Flash-Thinking leads the overall LLM Stats Score 28.7 to 19.8, ranking #148 overall.
In the 10 individual benchmarks reported for both models, LongCat-Flash-Thinking wins 8; this is a narrower head-to-head signal than the composite indexes.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose LongCat-Flash-Chat
- you want predictable pricing at $0.30/M input and $1.20/M output
Choose LongCat-Flash-Thinking
- overall performance matters — it scores 28.7 and ranks #148 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 8 of 10 exact shared results
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for LongCat-Flash-Chat · 14 for LongCat-Flash-Thinking
LongCat-Flash-Chat outperforms in 2 benchmarks (MMLU-Pro, SWE-Bench Verified), while LongCat-Flash-Thinking is better at 8 benchmarks (AIME 2025, GPQA, LiveCodeBench, MATH-500, Tau2 Airline, Tau2 Retail, Tau2 Telecom, ZebraLogic).
LongCat-Flash-Thinking significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, LongCat-Flash-Chat ($0.30/1M tokens) costs the same as LongCat-Flash-Thinking ($0.30/1M tokens).
For output processing, LongCat-Flash-Chat ($1.20/1M tokens) costs the same as LongCat-Flash-Thinking ($1.20/1M tokens).
In conclusion, LongCat-Flash-Chat and LongCat-Flash-Thinking cost the same.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
LongCat-Flash-Thinking has 0.0B more parameters than LongCat-Flash-Chat, making it 0.0% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 128,000 tokens. Both models can generate responses up to 128,000 tokens.
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
LongCat-Flash-Chat was released on 2025-08-29, while LongCat-Flash-Thinking was released on 2025-09-22.
LongCat-Flash-Thinking is 1 month newer than LongCat-Flash-Chat.
Aug 29, 2025
1.1 years ago
Sep 22, 2025
1.0 years ago
3w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
LongCat-Flash-Chat is available from Meituan. LongCat-Flash-Thinking is available from Meituan.
LongCat-Flash-Chat
LongCat-Flash-Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against LongCat-Flash-Chat and LongCat-Flash-Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about LongCat-Flash-Chat vs LongCat-Flash-Thinking.