Model Comparison

DeepSeek R1 Distill Qwen 32B vs DeepSeek-V3 0324

DeepSeek R1 Distill Qwen 32B shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek R1 Distill Qwen 32B outperforms in 3 benchmarks (AIME 2024, LiveCodeBench, MATH-500), while DeepSeek-V3 0324 is better at 1 benchmark (GPQA).

DeepSeek R1 Distill Qwen 32B shows notably better performance in the majority of benchmarks.

Tue Mar 31 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Mar 31 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
DeepSeek
DeepSeek-V3 0324
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

638.2B diff

DeepSeek-V3 0324 has 638.2B more parameters than DeepSeek R1 Distill Qwen 32B, making it 1945.7% larger.

DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
DeepSeek
DeepSeek-V3 0324
671.0Bparameters
32.8B
DeepSeek R1 Distill Qwen 32B
671.0B
DeepSeek-V3 0324

Context Window

Maximum input and output token capacity

Only DeepSeek R1 Distill Qwen 32B specifies input context (128,000 tokens). Only DeepSeek R1 Distill Qwen 32B specifies output context (128,000 tokens).

DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
DeepSeek
DeepSeek-V3 0324
Input- tokens
Output- tokens
Tue Mar 31 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 32B is licensed under MIT, while DeepSeek-V3 0324 uses MIT + Model License (Commercial use allowed).

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek R1 Distill Qwen 32B

MIT

Open weights

DeepSeek-V3 0324

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while DeepSeek-V3 0324 was released on 2025-03-25.

DeepSeek-V3 0324 is 2 months newer than DeepSeek R1 Distill Qwen 32B.

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.2 years ago

DeepSeek-V3 0324

Mar 25, 2025

1.0 years ago

2mo 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
Larger context window (128,000 tokens)
Higher AIME 2024 score (83.3% vs 59.4%)
Higher LiveCodeBench score (57.2% vs 49.2%)
Higher MATH-500 score (94.3% vs 94.0%)
DeepSeek
Higher GPQA score (68.4% vs 62.1%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Qwen 32B
DeepSeek
DeepSeek-V3 0324

FAQ

Common questions about DeepSeek R1 Distill Qwen 32B vs DeepSeek-V3 0324

DeepSeek R1 Distill Qwen 32B shows notably better performance in the majority of benchmarks. DeepSeek R1 Distill Qwen 32B is made by DeepSeek and DeepSeek-V3 0324 is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek R1 Distill Qwen 32B scores MATH-500: 94.3%, AIME 2024: 83.3%, GPQA: 62.1%, LiveCodeBench: 57.2%. DeepSeek-V3 0324 scores MATH-500: 94.0%, MMLU-Pro: 81.2%, GPQA: 68.4%, AIME 2024: 59.4%, LiveCodeBench: 49.2%.
DeepSeek R1 Distill Qwen 32B supports 128K tokens and DeepSeek-V3 0324 supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (MIT vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.