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LongCat-Flash-Chat vs Qwen3 VL 32B Thinking

LongCat-Flash-Chat and Qwen3 VL 32B Thinking are closely matched at 19.8 and 23.5 on the LLM Stats Score.

Meituan · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

LongCat-Flash-Chat and Qwen3 VL 32B Thinking are closely matched on the overall LLM Stats Score at 19.8 and 23.5.

In the 5 individual benchmarks reported for both models, LongCat-Flash-Chat wins 4; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose LongCat-Flash-Chat

  • you value its reported benchmark strengths — it wins 4 of 5 exact shared results

Choose Qwen3 VL 32B Thinking

  • your work emphasizes reasoning — it leads those capability indexes
  • 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
Core performance indexes
LLM Stats Score
19.8
#214
23.5
#189
Reasoning
19.7
#210
24.6
#170
Agents
9.1
#142
12.7
#112
Cost, coverage & limits
Benchmark wins
4 of 5
1 of 5
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
128,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

4 shared
Index
LongCat-Flash-Chat
Qwen3 VL 32B Thinking
Math
18.9#180
25.4#115
Writing
12.6#62
16.5#38
Finance
26.2#50
27.8#37
Legal
26.2#39
27.1#28
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for LongCat-Flash-Chat · 47 for Qwen3 VL 32B Thinking

5 shared

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.

Fri Oct 09 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground 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
Fri Oct 09 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

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

1.1 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

1.0 years 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?

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 and Qwen3 VL 32B Thinking are closely matched on the LLM Stats Score at 19.8 and 23.5. 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 capability indexes, individual benchmarks, pricing, and limits 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 LLM Stats Score (19.8 vs 23.5), 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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