LongCat-Flash-Thinking vs Qwen3 VL 4B Thinking Comparison
Comparing LongCat-Flash-Thinking and Qwen3 VL 4B Thinking across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
LongCat-Flash-Thinking outperforms in 5 benchmarks (AIME 2025, BFCL-v3, GPQA, MMLU-Pro, MMLU-Redux), while Qwen3 VL 4B Thinking is better at 0 benchmarks.
LongCat-Flash-Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, LongCat-Flash-Thinking ($0.30/1M tokens) is 3.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, LongCat-Flash-Thinking ($1.20/1M tokens) is 1.2x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, LongCat-Flash-Thinking is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
LongCat-Flash-Thinking has 556.0B more parameters than Qwen3 VL 4B Thinking, making it 13900.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to LongCat-Flash-Thinking's 128,000 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while LongCat-Flash-Thinking is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Thinking supports multimodal inputs, whereas LongCat-Flash-Thinking does not.
Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
LongCat-Flash-Thinking
Qwen3 VL 4B Thinking
License
Usage and distribution terms
LongCat-Flash-Thinking is licensed under MIT, while Qwen3 VL 4B 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
5 months ago
Sep 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.
Provider Availability
LongCat-Flash-Thinking is available from Meituan. Qwen3 VL 4B Thinking is available from DeepInfra. The availability of providers can affect quality of the model and reliability.
LongCat-Flash-Thinking
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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