Model Comparison

DeepSeek-R1 vs DeepSeek-V3.2-Exp

Comparing DeepSeek-R1 and DeepSeek-V3.2-Exp across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 and DeepSeek-V3.2-Exp 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

DeepSeek-V3.2-Exp costs less

For input processing, DeepSeek-R1 ($0.55/1M tokens) is 2.0x more expensive than DeepSeek-V3.2-Exp ($0.27/1M tokens).

For output processing, DeepSeek-R1 ($2.19/1M tokens) is 5.3x more expensive than DeepSeek-V3.2-Exp ($0.41/1M tokens).

In conclusion, DeepSeek-R1 is more expensive than DeepSeek-V3.2-Exp.*

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

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek-R1
Input tokens$0.55
Output tokens$2.19
Best providerDeepSeek
DeepSeek
DeepSeek-V3.2-Exp
Input tokens$0.27
Output tokens$0.41
Best providerNovita
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

14.0B diff

DeepSeek-V3.2-Exp has 14.0B more parameters than DeepSeek-R1, making it 2.1% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
DeepSeek
DeepSeek-V3.2-Exp
685.0Bparameters
671.0B
DeepSeek-R1
685.0B
DeepSeek-V3.2-Exp

Context Window

Maximum input and output token capacity

DeepSeek-V3.2-Exp accepts 163,840 input tokens compared to DeepSeek-R1's 131,072 tokens. DeepSeek-R1 can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Thu Apr 16 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

MIT

Open weights

DeepSeek-V3.2-Exp

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while DeepSeek-V3.2-Exp was released on 2025-09-29.

DeepSeek-V3.2-Exp is 8 months newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.2 years ago

DeepSeek-V3.2-Exp

Sep 29, 2025

6 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

Provider Availability

DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks. DeepSeek-V3.2-Exp is available from Novita.

DeepSeek-R1

deepseek logo
DeepSeek
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.85/1MOutput Price:Output: $2.50/1M
together logo
Together
Input Price:Input: $7.00/1MOutput Price:Output: $7.00/1M
fireworks logo
Fireworks
Input Price:Input: $8.00/1MOutput Price:Output: $8.00/1M

DeepSeek-V3.2-Exp

novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $0.41/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
DeepSeek
Larger context window (163,840 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1
DeepSeek
DeepSeek-V3.2-Exp

FAQ

Common questions about DeepSeek-R1 vs DeepSeek-V3.2-Exp

DeepSeek-R1 (DeepSeek) and DeepSeek-V3.2-Exp (DeepSeek) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%.
DeepSeek-V3.2-Exp is 2.0x cheaper for input tokens. DeepSeek-R1 costs $0.55/M input and $2.19/M output via deepseek. DeepSeek-V3.2-Exp costs $0.27/M input and $0.41/M output via novita.
DeepSeek-R1 supports 131K tokens and DeepSeek-V3.2-Exp supports 164K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 164K), input pricing ($0.55 vs $0.27/M). See the full comparison above for benchmark-by-benchmark results.