Model Comparison

LongCat-Flash-Lite vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

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.

Fri Apr 17 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
Fri Apr 17 2026 • llm-stats.com
Meituan
LongCat-Flash-Lite
Input tokens$0.10
Output tokens$0.40
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

35.5B diff

LongCat-Flash-Lite has 35.5B more parameters than Qwen3 VL 32B Thinking, making it 107.6% larger.

Meituan
LongCat-Flash-Lite
68.5Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
68.5B
LongCat-Flash-Lite
33.0B
Qwen3 VL 32B Thinking

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).

Meituan
LongCat-Flash-Lite
Input256,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Fri Apr 17 2026 • llm-stats.com

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

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

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.

LongCat-Flash-Lite

MIT

Open weights

Qwen3 VL 32B Thinking

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.

LongCat-Flash-Lite

Feb 5, 2026

2 months ago

4mo newer
Qwen3 VL 32B Thinking

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.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Meituan
Larger context window (256,000 tokens)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs
Higher AIME 2025 score (83.7% vs 63.2%)
Higher GPQA score (73.1% vs 66.8%)
Higher MMLU score (88.7% vs 85.5%)
Higher MMLU-Pro score (82.1% vs 78.3%)

Detailed Comparison

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

FAQ

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

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. LongCat-Flash-Lite 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-Lite scores MATH-500: 96.8%, MMLU: 85.5%, CMMLU: 82.5%, MMLU-Pro: 78.3%, Tau2 Retail: 73.1%. 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-Lite supports 256K 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-Lite is developed by Meituan and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.