DeepSeek R1 Distill Qwen 1.5B vs DeepSeek R1 Distill Qwen 14B Comparison

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek R1 Distill Qwen 1.5B outperforms in 0 benchmarks, while DeepSeek R1 Distill Qwen 14B is better at 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500).

DeepSeek R1 Distill Qwen 14B significantly outperforms across most benchmarks.

Fri Mar 13 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
Fri Mar 13 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 1.5B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
DeepSeek
DeepSeek R1 Distill Qwen 14B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

13.0B diff

DeepSeek R1 Distill Qwen 14B has 13.0B more parameters than DeepSeek R1 Distill Qwen 1.5B, making it 731.5% larger.

DeepSeek
DeepSeek R1 Distill Qwen 1.5B
1.8Bparameters
DeepSeek
DeepSeek R1 Distill Qwen 14B
14.8Bparameters
1.8B
DeepSeek R1 Distill Qwen 1.5B
14.8B
DeepSeek R1 Distill Qwen 14B

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 1.5B

MIT

Open weights

DeepSeek R1 Distill Qwen 14B

MIT

Open weights

Release Timeline

When each model was launched

Both models were released on 2025-01-20.

They likely represent similar generations of model development.

DeepSeek R1 Distill Qwen 1.5B

Jan 20, 2025

1.1 years ago

DeepSeek R1 Distill Qwen 14B

Jan 20, 2025

1.1 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

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Key Takeaways

Higher AIME 2024 score (80.0% vs 52.7%)
Higher GPQA score (59.1% vs 33.8%)
Higher LiveCodeBench score (53.1% vs 16.9%)
Higher MATH-500 score (93.9% vs 83.9%)

Detailed Comparison