LongCat-Flash-Thinking vs Particle-1
Particle-1 leads the LLM Stats Score 32.0 to 28.1. Particle-1 is 1.2x cheaper per token.
Meituan · Particle AI · Updated for 2026
Which is better?
Particle-1 leads the overall LLM Stats Score 32.0 to 28.1, ranking #135 overall.
In the 1 individual benchmarks reported for both models, Particle-1 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Particle-1 is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Particle-1 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose LongCat-Flash-Thinking
- you need open weights you can self-host or fine-tune
Choose Particle-1
- overall performance matters — it scores 32.0 and ranks #135 on LLM Stats
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
14 reported for LongCat-Flash-Thinking · 3 for Particle-1
LongCat-Flash-Thinking outperforms in 0 benchmarks, while Particle-1 is better at 1 benchmark (GPQA).
Particle-1 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-Thinking ($0.30/1M tokens) is 1.2x more expensive than Particle-1 ($0.25/1M tokens).
For output processing, LongCat-Flash-Thinking ($1.20/1M tokens) is 1.2x more expensive than Particle-1 ($1.00/1M tokens).
In conclusion, LongCat-Flash-Thinking is more expensive than Particle-1.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Particle-1 accepts 1,048,576 input tokens compared to LongCat-Flash-Thinking's 128,000 tokens. Particle-1 can generate longer responses up to 131,072 tokens, while LongCat-Flash-Thinking is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Particle-1 supports multimodal inputs, whereas LongCat-Flash-Thinking does not.
Particle-1 can handle both text and other forms of data like images, making it suitable for multimodal applications.
LongCat-Flash-Thinking
Particle-1
License
Usage and distribution terms
LongCat-Flash-Thinking is licensed under MIT, while Particle-1 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
LongCat-Flash-Thinking was released on 2025-09-22, while Particle-1 was released on 2026-10-28.
Particle-1 is 13 months newer than LongCat-Flash-Thinking.
Sep 22, 2025
1.1 years ago
Oct 28, 2026
1 weeks ago
1.1yr 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-Thinking is available from Meituan. Particle-1 is available from Particle AI.
LongCat-Flash-Thinking
Particle-1
Outputs Comparison
Judge for yourself.
Run your own prompts against LongCat-Flash-Thinking and Particle-1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about LongCat-Flash-Thinking vs Particle-1.