DeepSeek: R1 Distill Qwen 32B
DeepSeek R1 Distill Qwen 32B is a distilled large language model based on [Qwen 2.5 32B](https://huggingface.co/Qwen/Qwen2.5-32B), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). It outperforms OpenAI's o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.\n\nOther benchmark results include:\n\n- AIME 2024 pass@1: 72.6\n- MATH-500 pass@1: 94.3\n- CodeForces Rating: 1691\n\nThe model leverages fine-tuning from DeepSeek R1's outputs, enabling competitive performance comparable to larger frontier models.
Context Window
33K
Input Price
$0.2900/M tokens
Output Price
$0.2900/M tokens
Modality
text->text
Cost Example: Processing 10,000 input tokens and generating 1,000 output tokens would cost approximately $0.003190
~25K
Words Equivalent
~16
Pages of Text
~328
Code Functions
DeepSeek: DeepSeek V3.2 Speciale
$0.2700/M tokens · 164K context
DeepSeek: DeepSeek V3.2
$0.2500/M tokens · 164K context
DeepSeek: DeepSeek V3.2 Exp
$0.2100/M tokens · 164K context
DeepSeek: DeepSeek V3.1 Terminus (exacto)
$0.2100/M tokens · 164K context
DeepSeek: DeepSeek V3.1 Terminus
$0.2100/M tokens · 164K context
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