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LongCat-Flash-Chat vs Qwen3.5-2B

LongCat-Flash-Chat leads the LLM Stats Score 20.0 to 4.1.

Meituan · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

LongCat-Flash-Chat leads the overall LLM Stats Score 20.0 to 4.1, ranking #188 overall.

In the 3 individual benchmarks reported for both models, LongCat-Flash-Chat wins 3; 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

  • overall performance matters — it scores 20.0 and ranks #188 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results

Choose Qwen3.5-2B

  • you want the most recent training data — it shipped Mar 2026

At a glance

The differences that matter most.

LongCat-Flash-Chat
Meituan
Qwen3.5-2B
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
20.0
#188
4.1
#292
Reasoning
19.9
#183
5.6
#281
Agents
9.3
#119
-0.7
#162
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
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
Qwen3.5-2B
Math
19.2#163
4.0#278
Tool use
9.7#125
-0.2#171
Finance
26.6#44
3.7#171
Legal
26.6#34
3.7#160
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for LongCat-Flash-Chat · 20 for Qwen3.5-2B

3 shared

LongCat-Flash-Chat outperforms in 3 benchmarks (GPQA, IFEval, MMLU-Pro), while Qwen3.5-2B is better at 0 benchmarks.

LongCat-Flash-Chat significantly outperforms across most benchmarks.

Fri Sep 04 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

558.0B diff

LongCat-Flash-Chat has 558.0B more parameters than Qwen3.5-2B, making it 27900.0% larger.

Meituan
LongCat-Flash-Chat
560.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.5-2B
2.0Bparameters
560.0B
LongCat-Flash-Chat
2.0B
Qwen3.5-2B

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
Qwen3.5-2B
Input- tokens
Output- tokens
Fri Sep 04 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3.5-2B supports multimodal inputs, whereas LongCat-Flash-Chat does not.

Qwen3.5-2B can handle both text and other forms of data like images, making it suitable for multimodal applications.

LongCat-Flash-Chat

Text
Images
Audio
Video

Qwen3.5-2B

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Chat is licensed under MIT, while Qwen3.5-2B 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

Qwen3.5-2B

Apache 2.0

Open weights

Release Timeline

When each model was launched

LongCat-Flash-Chat was released on 2025-08-29, while Qwen3.5-2B was released on 2026-03-02.

Qwen3.5-2B is 6 months newer than LongCat-Flash-Chat.

LongCat-Flash-Chat

Aug 29, 2025

1.0 years ago

Qwen3.5-2B

Mar 2, 2026

6 months ago

6mo 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 Qwen3.5-2B side-by-side, then vote on the output you prefer.

LongCat-Flash-Chat
✓ Preferred
Qwen3.5-2B
Open in Playground

FAQ

Common questions about LongCat-Flash-Chat vs Qwen3.5-2B.

Which is better, LongCat-Flash-Chat or Qwen3.5-2B?

LongCat-Flash-Chat leads the LLM Stats Score 20.0 to 4.1. LongCat-Flash-Chat is made by Meituan and Qwen3.5-2B 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 Qwen3.5-2B in benchmarks?

LongCat-Flash-Chat scores MATH-500: 96.4%, MMLU: 89.7%, IFEval: 89.6%, ZebraLogic: 89.3%, HumanEval: 88.4%. Qwen3.5-2B scores MMLU-Redux: 79.6%, IFEval: 78.6%, C-Eval: 73.2%, Global PIQA: 69.3%, MMLU-Pro: 66.5%.

What are the context window sizes for LongCat-Flash-Chat and Qwen3.5-2B?

LongCat-Flash-Chat supports 128K tokens and Qwen3.5-2B 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 Qwen3.5-2B?

Key differences include LLM Stats Score (20.0 vs 4.1), 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 Qwen3.5-2B?

LongCat-Flash-Chat is developed by Meituan and Qwen3.5-2B is developed by Alibaba Cloud / Qwen Team.

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