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LongCat-Flash-Chat vs Qwen3 VL 32B Thinking

LongCat-Flash-Chat significantly outperforms across most benchmarks.

Meituan · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

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.

Based on current benchmark, pricing, and model metadata for 2026.

Choose LongCat-Flash-Chat

  • you want the strongest raw capability — it leads on 4 of 5 shared benchmarks

Choose Qwen3 VL 32B Thinking

  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

LongCat-Flash-Chat
Meituan
Qwen3 VL 32B Thinking
Alibaba Cloud / Qwen Team
Benchmark wins
4 of 5
1 of 5
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
128,000
—
Released
Aug 2025
Sep 2025
License
MIT
Apache 2.0

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.

Mon Aug 24 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

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
Mon Aug 24 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

12 months ago

Qwen3 VL 32B Thinking

Sep 22, 2025

11 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

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against LongCat-Flash-Chat and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

LongCat-Flash-Chat
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

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

Which is better, LongCat-Flash-Chat or 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.

How does LongCat-Flash-Chat compare to Qwen3 VL 32B Thinking in benchmarks?

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%.

What are the context window sizes for LongCat-Flash-Chat and Qwen3 VL 32B Thinking?

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.

What are the main differences between LongCat-Flash-Chat and Qwen3 VL 32B Thinking?

Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes LongCat-Flash-Chat and Qwen3 VL 32B Thinking?

LongCat-Flash-Chat is developed by Meituan and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

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