Model Comparison

DeepSeek R1 Distill Qwen 7B vs LongCat-Flash-Thinking

LongCat-Flash-Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek R1 Distill Qwen 7B outperforms in 0 benchmarks, while LongCat-Flash-Thinking is better at 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500).

LongCat-Flash-Thinking significantly outperforms across most benchmarks.

Thu Jun 04 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

552.4B diff

LongCat-Flash-Thinking has 552.4B more parameters than DeepSeek R1 Distill Qwen 7B, making it 7249.1% larger.

DeepSeek
DeepSeek R1 Distill Qwen 7B
7.6Bparameters
Meituan
LongCat-Flash-Thinking
560.0Bparameters
7.6B
DeepSeek R1 Distill Qwen 7B
560.0B
LongCat-Flash-Thinking

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).

DeepSeek
DeepSeek R1 Distill Qwen 7B
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Thu Jun 04 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek R1 Distill Qwen 7B

MIT

Open weights

LongCat-Flash-Thinking

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 7B was released on 2025-01-20, while LongCat-Flash-Thinking was released on 2025-09-22.

LongCat-Flash-Thinking is 8 months newer than DeepSeek R1 Distill Qwen 7B.

DeepSeek R1 Distill Qwen 7B

Jan 20, 2025

1.4 years ago

LongCat-Flash-Thinking

Sep 22, 2025

8 months ago

8mo 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

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Key Takeaways

DeepSeek

No standout differentiators in the data we have for this pair.

Meituan
Larger context window (128,000 tokens)
Higher AIME 2024 score (93.3% vs 83.3%)
Higher GPQA score (81.5% vs 49.1%)
Higher LiveCodeBench score (79.4% vs 37.6%)
Higher MATH-500 score (99.2% vs 92.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Qwen 7B
Meituan
LongCat-Flash-Thinking

FAQ

Common questions about DeepSeek R1 Distill Qwen 7B vs LongCat-Flash-Thinking.

Which is better, DeepSeek R1 Distill Qwen 7B or LongCat-Flash-Thinking?

LongCat-Flash-Thinking significantly outperforms across most benchmarks. DeepSeek R1 Distill Qwen 7B is made by DeepSeek and LongCat-Flash-Thinking is made by Meituan. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek R1 Distill Qwen 7B compare to LongCat-Flash-Thinking in benchmarks?

DeepSeek R1 Distill Qwen 7B scores MATH-500: 92.8%, AIME 2024: 83.3%, GPQA: 49.1%, LiveCodeBench: 37.6%. LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%.

What are the context window sizes for DeepSeek R1 Distill Qwen 7B and LongCat-Flash-Thinking?

DeepSeek R1 Distill Qwen 7B supports an unknown number of tokens and LongCat-Flash-Thinking supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Who makes DeepSeek R1 Distill Qwen 7B and LongCat-Flash-Thinking?

DeepSeek R1 Distill Qwen 7B is developed by DeepSeek and LongCat-Flash-Thinking is developed by Meituan.