LongCat-Flash-Lite vs Qwen3 VL 32B Thinking Comparison
Comparing LongCat-Flash-Lite and Qwen3 VL 32B Thinking across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
LongCat-Flash-Lite outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 4 benchmarks (AIME 2025, GPQA, MMLU, MMLU-Pro).
Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
LongCat-Flash-Lite has 35.5B more parameters than Qwen3 VL 32B Thinking, making it 107.6% larger.
Context Window
Maximum input and output token capacity
Only LongCat-Flash-Lite specifies input context (256,000 tokens). Only LongCat-Flash-Lite specifies output context (128,000 tokens).
Input Capabilities
Supported data types and modalities
Qwen3 VL 32B Thinking supports multimodal inputs, whereas LongCat-Flash-Lite does not.
Qwen3 VL 32B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
LongCat-Flash-Lite
Qwen3 VL 32B Thinking
License
Usage and distribution terms
LongCat-Flash-Lite is licensed under MIT, while Qwen3 VL 32B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
LongCat-Flash-Lite was released on 2026-02-05, while Qwen3 VL 32B Thinking was released on 2025-09-22.
LongCat-Flash-Lite is 5 months newer than Qwen3 VL 32B Thinking.
Feb 5, 2026
1 months ago
4mo newerSep 22, 2025
5 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Qwen3 VL 32B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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