Model Comparison

LongCat-Flash-Thinking-2601 vs Qwen2.5 VL 72B Instruct

Comparing LongCat-Flash-Thinking-2601 and Qwen2.5 VL 72B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

LongCat-Flash-Thinking-2601 and Qwen2.5 VL 72B Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
Meituan
LongCat-Flash-Thinking-2601
Input tokens$0.30
Output tokens$1.20
Best providerMeituan
Alibaba Cloud / Qwen Team
Qwen2.5 VL 72B Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

488.0B diff

LongCat-Flash-Thinking-2601 has 488.0B more parameters than Qwen2.5 VL 72B Instruct, making it 677.8% larger.

Meituan
LongCat-Flash-Thinking-2601
560.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 VL 72B Instruct
72.0Bparameters
560.0B
LongCat-Flash-Thinking-2601
72.0B
Qwen2.5 VL 72B Instruct

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
Qwen2.5 VL 72B Instruct
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen2.5 VL 72B Instruct supports multimodal inputs, whereas LongCat-Flash-Thinking-2601 does not.

Qwen2.5 VL 72B Instruct 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

Qwen2.5 VL 72B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

LongCat-Flash-Thinking-2601 is licensed under MIT, while Qwen2.5 VL 72B Instruct uses tongyi-qianwen.

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

LongCat-Flash-Thinking-2601

MIT

Open weights

Qwen2.5 VL 72B Instruct

tongyi-qianwen

Open weights

Release Timeline

When each model was launched

LongCat-Flash-Thinking-2601 was released on 2026-01-14, while Qwen2.5 VL 72B Instruct was released on 2025-01-26.

LongCat-Flash-Thinking-2601 is 12 months newer than Qwen2.5 VL 72B Instruct.

LongCat-Flash-Thinking-2601

Jan 14, 2026

3 months ago

11mo newer
Qwen2.5 VL 72B Instruct

Jan 26, 2025

1.2 years ago

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

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Key Takeaways

Meituan
Larger context window (128,000 tokens)
Alibaba Cloud / Qwen Team

Qwen2.5 VL 72B Instruct

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Meituan
LongCat-Flash-Thinking-2601
Alibaba Cloud / Qwen Team
Qwen2.5 VL 72B Instruct

FAQ

Common questions about LongCat-Flash-Thinking-2601 vs Qwen2.5 VL 72B Instruct

LongCat-Flash-Thinking-2601 (Meituan) and Qwen2.5 VL 72B Instruct (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
LongCat-Flash-Thinking-2601 scores AIME 2025: 99.6%, Tau2 Telecom: 99.3%, Tau2 Retail: 88.6%, LiveCodeBench: 82.8%, GPQA: 80.5%. Qwen2.5 VL 72B Instruct scores DocVQA: 96.4%, Android Control Low_EM: 93.7%, ChartQA: 89.5%, OCRBench: 88.5%, AI2D: 88.4%.
LongCat-Flash-Thinking-2601 supports 128K tokens and Qwen2.5 VL 72B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs tongyi-qianwen). See the full comparison above for benchmark-by-benchmark results.
LongCat-Flash-Thinking-2601 is developed by Meituan and Qwen2.5 VL 72B Instruct is developed by Alibaba Cloud / Qwen Team.