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LongCat-Flash-Thinking vs Qwen3.8-Flash-Next

Qwen3.8-Flash-Next significantly outperforms across most benchmarks.

Meituan · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

LongCat-Flash-Thinking outperforms in 0 benchmarks, while Qwen3.8-Flash-Next is better at 1 benchmark (GPQA). Qwen3.8-Flash-Next significantly outperforms across most benchmarks.

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

Choose LongCat-Flash-Thinking

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

Choose Qwen3.8-Flash-Next

  • you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

LongCat-Flash-Thinking
Meituan
Qwen3.8-Flash-Next
Alibaba Cloud / Qwen Team
Benchmark wins
0 of 1
1 of 1
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
128,000
—
Released
Sep 2025
Aug 2026
License
MIT
Qwen Community License 1.0

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

LongCat-Flash-Thinking outperforms in 0 benchmarks, while Qwen3.8-Flash-Next is better at 1 benchmark (GPQA).

Qwen3.8-Flash-Next significantly outperforms across most benchmarks.

Wed Aug 26 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

380.0B diff

LongCat-Flash-Thinking has 380.0B more parameters than Qwen3.8-Flash-Next, making it 211.1% larger.

Meituan
LongCat-Flash-Thinking
560.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
180.0Bparameters
560.0B
LongCat-Flash-Thinking
180.0B
Qwen3.8-Flash-Next

Context Window

Maximum input and output token capacity

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

Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
Input- tokens
Output- tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.8-Flash-Next supports multimodal inputs, whereas LongCat-Flash-Thinking does not.

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

LongCat-Flash-Thinking

Text
Images
Audio
Video

Qwen3.8-Flash-Next

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Thinking is licensed under MIT, while Qwen3.8-Flash-Next uses Qwen Community License 1.0.

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

LongCat-Flash-Thinking

MIT

Open weights

Qwen3.8-Flash-Next

Qwen Community License 1.0

Open weights

Release Timeline

When each model was launched

LongCat-Flash-Thinking was released on 2025-09-22, while Qwen3.8-Flash-Next was released on 2026-08-26.

Qwen3.8-Flash-Next is 11 months newer than LongCat-Flash-Thinking.

LongCat-Flash-Thinking

Sep 22, 2025

11 months ago

Qwen3.8-Flash-Next

Aug 26, 2026

0 days ago

11mo 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 and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.

LongCat-Flash-Thinking
✓ Preferred
Qwen3.8-Flash-Next
Open in Playground

FAQ

Common questions about LongCat-Flash-Thinking vs Qwen3.8-Flash-Next.

Which is better, LongCat-Flash-Thinking or Qwen3.8-Flash-Next?

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

LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%. Qwen3.8-Flash-Next scores MathVision: 95.7%, LiveCodeBench v6: 91.9%, GPQA: 91.7%, CharXiv-R: 90.6%, RealWorldQA: 88.5%.

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

LongCat-Flash-Thinking supports 128K tokens and Qwen3.8-Flash-Next 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 and Qwen3.8-Flash-Next?

Key differences include multimodal support (no vs yes), licensing (MIT vs Qwen Community License 1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes LongCat-Flash-Thinking and Qwen3.8-Flash-Next?

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

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