Model Comparison
LongCat-Flash-Thinking vs Qwen3 VL 32B Thinking
LongCat-Flash-Thinking significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
LongCat-Flash-Thinking outperforms in 4 benchmarks (AIME 2025, BFCL-v3, GPQA, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 1 benchmark (MMLU-Redux).
LongCat-Flash-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-Thinking has 527.0B more parameters than Qwen3 VL 32B Thinking, making it 1597.0% larger.
Context Window
Maximum input and output token capacity
Only LongCat-Flash-Thinking specifies input context (128,000 tokens). Only LongCat-Flash-Thinking specifies output context (128,000 tokens).
Input Capabilities
Supported data types and modalities
Qwen3 VL 32B Thinking supports multimodal inputs, whereas LongCat-Flash-Thinking 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-Thinking
Qwen3 VL 32B Thinking
License
Usage and distribution terms
LongCat-Flash-Thinking 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
Both models were released on 2025-09-22.
They likely represent similar generations of model development.
Sep 22, 2025
6 months ago
Sep 22, 2025
6 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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FAQ
Common questions about LongCat-Flash-Thinking vs Qwen3 VL 32B Thinking