Model Comparison

DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Qwen 14BWhich is better in 2026?

Comparing DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Qwen 14B across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Qwen 14B — which is better?

DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and DeepSeek R1 Distill Qwen 14B (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose DeepSeek-V3.2 (Non-thinking) if…

  • you want the most recent training data — it shipped Dec 2025

Choose DeepSeek R1 Distill Qwen 14B if…

  • you are already invested in the DeepSeek ecosystem
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Qwen 14B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

670.2B diff

DeepSeek-V3.2 (Non-thinking) has 670.2B more parameters than DeepSeek R1 Distill Qwen 14B, making it 4528.4% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
DeepSeek
DeepSeek R1 Distill Qwen 14B
14.8Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
14.8B
DeepSeek R1 Distill Qwen 14B

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
DeepSeek
DeepSeek R1 Distill Qwen 14B
Input- tokens
Output- tokens
Sun Jun 07 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-V3.2 (Non-thinking)

MIT

Open weights

DeepSeek R1 Distill Qwen 14B

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while DeepSeek R1 Distill Qwen 14B was released on 2025-01-20.

DeepSeek-V3.2 (Non-thinking) is 11 months newer than DeepSeek R1 Distill Qwen 14B.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

6 months ago

10mo newer
DeepSeek R1 Distill Qwen 14B

Jan 20, 2025

1.4 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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
DeepSeek

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

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Non-thinking)
DeepSeek
DeepSeek R1 Distill Qwen 14B

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs DeepSeek R1 Distill Qwen 14B.

Which is better, DeepSeek-V3.2 (Non-thinking) or DeepSeek R1 Distill Qwen 14B?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and DeepSeek R1 Distill Qwen 14B (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.2 (Non-thinking) compare to DeepSeek R1 Distill Qwen 14B in benchmarks?

DeepSeek R1 Distill Qwen 14B scores MATH-500: 93.9%, AIME 2024: 80.0%, GPQA: 59.1%, LiveCodeBench: 53.1%.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and DeepSeek R1 Distill Qwen 14B?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and DeepSeek R1 Distill Qwen 14B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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