Model Comparison

DeepSeek R1 Distill Qwen 7B vs DeepSeek-V3.2 (Thinking)

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Distill Qwen 7B outperforms in 0 benchmarks, while DeepSeek-V3.2 (Thinking) is better at 2 benchmarks (GPQA, LiveCodeBench).

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Mon May 11 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

677.4B diff

DeepSeek-V3.2 (Thinking) has 677.4B more parameters than DeepSeek R1 Distill Qwen 7B, making it 8889.5% larger.

DeepSeek
DeepSeek R1 Distill Qwen 7B
7.6Bparameters
DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
7.6B
DeepSeek R1 Distill Qwen 7B
685.0B
DeepSeek-V3.2 (Thinking)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek R1 Distill Qwen 7B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Mon May 11 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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 7B was released on 2025-01-20, while DeepSeek-V3.2 (Thinking) was released on 2025-12-01.

DeepSeek-V3.2 (Thinking) is 11 months newer than DeepSeek R1 Distill Qwen 7B.

DeepSeek R1 Distill Qwen 7B

Jan 20, 2025

1.3 years ago

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

5 months ago

10mo 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.

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (82.4% vs 49.1%)
Higher LiveCodeBench score (83.3% vs 37.6%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Qwen 7B
DeepSeek
DeepSeek-V3.2 (Thinking)

FAQ

Common questions about DeepSeek R1 Distill Qwen 7B vs DeepSeek-V3.2 (Thinking).

Which is better, DeepSeek R1 Distill Qwen 7B or DeepSeek-V3.2 (Thinking)?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek R1 Distill Qwen 7B is made by DeepSeek and DeepSeek-V3.2 (Thinking) is made by DeepSeek. 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 DeepSeek-V3.2 (Thinking) in benchmarks?

DeepSeek R1 Distill Qwen 7B scores MATH-500: 92.8%, AIME 2024: 83.3%, GPQA: 49.1%, LiveCodeBench: 37.6%. DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%.

What are the context window sizes for DeepSeek R1 Distill Qwen 7B and DeepSeek-V3.2 (Thinking)?

DeepSeek R1 Distill Qwen 7B supports an unknown number of tokens and DeepSeek-V3.2 (Thinking) supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.