Model Comparison

LongCat-Flash-Chat vs Qwen3 VL 32B Thinking

LongCat-Flash-Chat significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

LongCat-Flash-Chat outperforms in 4 benchmarks (GPQA, IFEval, MMLU, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 1 benchmark (AIME 2025).

LongCat-Flash-Chat significantly outperforms across most benchmarks.

Thu Apr 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
Meituan
LongCat-Flash-Chat
Input tokens$0.30
Output tokens$1.20
Best providerMeituan
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

527.0B diff

LongCat-Flash-Chat has 527.0B more parameters than Qwen3 VL 32B Thinking, making it 1597.0% larger.

Meituan
LongCat-Flash-Chat
560.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
560.0B
LongCat-Flash-Chat
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Chat specifies input context (128,000 tokens). Only LongCat-Flash-Chat specifies output context (128,000 tokens).

Meituan
LongCat-Flash-Chat
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 32B Thinking supports multimodal inputs, whereas LongCat-Flash-Chat 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-Chat

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Chat 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.

LongCat-Flash-Chat

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

LongCat-Flash-Chat was released on 2025-08-29, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 1 month newer than LongCat-Flash-Chat.

LongCat-Flash-Chat

Aug 29, 2025

7 months ago

Qwen3 VL 32B Thinking

Sep 22, 2025

6 months ago

3w newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

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Key Takeaways

Meituan
Larger context window (128,000 tokens)
Higher GPQA score (73.2% vs 73.1%)
Higher IFEval score (89.6% vs 87.8%)
Higher MMLU score (89.7% vs 88.7%)
Higher MMLU-Pro score (82.7% vs 82.1%)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs
Higher AIME 2025 score (83.7% vs 61.3%)

Detailed Comparison

AI Model Comparison Table
Feature
Meituan
LongCat-Flash-Chat
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking

FAQ

Common questions about LongCat-Flash-Chat vs Qwen3 VL 32B Thinking

LongCat-Flash-Chat significantly outperforms across most benchmarks. LongCat-Flash-Chat is made by Meituan and Qwen3 VL 32B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
LongCat-Flash-Chat scores MATH-500: 96.4%, MMLU: 89.7%, IFEval: 89.6%, ZebraLogic: 89.3%, HumanEval: 88.4%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.
LongCat-Flash-Chat supports 128K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
LongCat-Flash-Chat is developed by Meituan and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.