Model Comparison

DeepSeek-V3.2 (Non-thinking) vs DeepSeek-V3.2 (Thinking)

Comparing DeepSeek-V3.2 (Non-thinking) and DeepSeek-V3.2 (Thinking) across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and DeepSeek-V3.2 (Thinking) 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

For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) costs the same as DeepSeek-V3.2 (Thinking) ($0.28/1M tokens).

For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) costs the same as DeepSeek-V3.2 (Thinking) ($0.42/1M tokens).

In conclusion, DeepSeek-V3.2 (Non-thinking) and DeepSeek-V3.2 (Thinking) cost the same.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed May 13 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

0.0M diff

DeepSeek-V3.2 (Thinking) has 0.0B more parameters than DeepSeek-V3.2 (Non-thinking), making it 0.0% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
685.0B
DeepSeek-V3.2 (Thinking)

Context Window

Maximum input and output token capacity

Both models have the same input context window of 131,072 tokens. DeepSeek-V3.2 (Thinking) can generate longer responses up to 65,536 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Wed May 13 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-V3.2 (Thinking)

MIT

Open weights

Release Timeline

When each model was launched

Both models were released on 2025-12-01.

They likely represent similar generations of model development.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

5 months ago

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

5 months 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

Provider Availability

DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. DeepSeek-V3.2 (Thinking) is available from DeepSeek.

DeepSeek-V3.2 (Non-thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek

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

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-V3.2 (Thinking)

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs DeepSeek-V3.2 (Thinking).

Which is better, DeepSeek-V3.2 (Non-thinking) or DeepSeek-V3.2 (Thinking)?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and DeepSeek-V3.2 (Thinking) (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-V3.2 (Thinking) in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%.

Is DeepSeek-V3.2 (Non-thinking) cheaper than DeepSeek-V3.2 (Thinking)?

Both models cost $0.28 per million input tokens.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and DeepSeek-V3.2 (Thinking)?

DeepSeek-V3.2 (Non-thinking) supports 131K 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.