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LongCat-Flash-Thinking-2601 vs Qwen3.8-27B

Qwen3.8-27B significantly outperforms across most benchmarks.

Meituan · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

LongCat-Flash-Thinking-2601 outperforms in 0 benchmarks, while Qwen3.8-27B is better at 2 benchmarks (GPQA, Humanity's Last Exam). Qwen3.8-27B significantly outperforms across most benchmarks.

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

Choose LongCat-Flash-Thinking-2601

  • you want predictable pricing at $0.30/M input and $1.20/M output

Choose Qwen3.8-27B

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Benchmark wins
0 of 2
2 of 2
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
128,000
Released
Jan 2026
Aug 2026
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

LongCat-Flash-Thinking-2601 outperforms in 0 benchmarks, while Qwen3.8-27B is better at 2 benchmarks (GPQA, Humanity's Last Exam).

Qwen3.8-27B significantly outperforms across most benchmarks.

Tue Aug 25 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

532.2B diff

LongCat-Flash-Thinking-2601 has 532.2B more parameters than Qwen3.8-27B, making it 1915.7% larger.

Meituan
LongCat-Flash-Thinking-2601
560.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-27B
27.8Bparameters
560.0B
LongCat-Flash-Thinking-2601
27.8B
Qwen3.8-27B

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Thinking-2601 specifies input context (128,000 tokens). Only LongCat-Flash-Thinking-2601 specifies output context (128,000 tokens).

Meituan
LongCat-Flash-Thinking-2601
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-27B
Input- tokens
Output- tokens
Tue Aug 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.8-27B supports multimodal inputs, whereas LongCat-Flash-Thinking-2601 does not.

Qwen3.8-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.

LongCat-Flash-Thinking-2601

Text
Images
Audio
Video

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Thinking-2601 is licensed under MIT, while Qwen3.8-27B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

LongCat-Flash-Thinking-2601

MIT

Open weights

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

LongCat-Flash-Thinking-2601 was released on 2026-01-14, while Qwen3.8-27B was released on 2026-08-14.

Qwen3.8-27B is 7 months newer than LongCat-Flash-Thinking-2601.

LongCat-Flash-Thinking-2601

Jan 14, 2026

7 months ago

Qwen3.8-27B

Aug 14, 2026

1 weeks ago

7mo 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-Thinking-2601 and Qwen3.8-27B side-by-side, then vote on the output you prefer.

LongCat-Flash-Thinking-2601
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about LongCat-Flash-Thinking-2601 vs Qwen3.8-27B.

Which is better, LongCat-Flash-Thinking-2601 or Qwen3.8-27B?

Qwen3.8-27B significantly outperforms across most benchmarks. LongCat-Flash-Thinking-2601 is made by Meituan and Qwen3.8-27B 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-Thinking-2601 compare to Qwen3.8-27B in benchmarks?

LongCat-Flash-Thinking-2601 scores AIME 2025: 99.6%, Tau2 Telecom: 99.3%, Tau2 Retail: 88.6%, LiveCodeBench: 82.8%, GPQA: 80.5%. Qwen3.8-27B scores MathVision: 94.6%, OmniDocBench 1.5: 91.1%, LiveCodeBench v6: 90.3%, CharXiv-R: 90.2%, GPQA: 89.2%.

What are the context window sizes for LongCat-Flash-Thinking-2601 and Qwen3.8-27B?

LongCat-Flash-Thinking-2601 supports 128K tokens and Qwen3.8-27B 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-Thinking-2601 and Qwen3.8-27B?

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-Thinking-2601 and Qwen3.8-27B?

LongCat-Flash-Thinking-2601 is developed by Meituan and Qwen3.8-27B is developed by Alibaba Cloud / Qwen Team.