DeepSeek R1 Distill Llama 8B vs DeepSeek R1 Distill Qwen 32B Comparison

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek R1 Distill Llama 8B outperforms in 0 benchmarks, while DeepSeek R1 Distill Qwen 32B is better at 4 benchmarks (AIME 2024, GPQA, LiveCodeBench, MATH-500).

DeepSeek R1 Distill Qwen 32B significantly outperforms across most benchmarks.

Sat Mar 14 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
Sat Mar 14 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Llama 8B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
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Model Size

Parameter count comparison

24.8B diff

DeepSeek R1 Distill Qwen 32B has 24.8B more parameters than DeepSeek R1 Distill Llama 8B, making it 308.5% larger.

DeepSeek
DeepSeek R1 Distill Llama 8B
8.0Bparameters
DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
8.0B
DeepSeek R1 Distill Llama 8B
32.8B
DeepSeek R1 Distill Qwen 32B

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 Llama 8B
Input- tokens
Output- tokens
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
Sat Mar 14 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 Distill Llama 8B

MIT

Open weights

DeepSeek R1 Distill Qwen 32B

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 Llama 8B

Jan 20, 2025

1.1 years ago

DeepSeek R1 Distill Qwen 32B

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

Larger context window (128,000 tokens)
Higher AIME 2024 score (83.3% vs 80.0%)
Higher GPQA score (62.1% vs 49.0%)
Higher LiveCodeBench score (57.2% vs 39.6%)
Higher MATH-500 score (94.3% vs 89.1%)

Detailed Comparison