DeepSeek R1 vs DeepSeek V3.2
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for DeepSeek R1 and DeepSeek V3.2.
DeepSeek R1
A premier reasoning model employing large-scale reinforcement learning. Displays specialized math, coding, and logical validation capabilities comparable to OpenAI's o1.
DeepSeek V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Technical Specifications
| Specification | DeepSeek R1 | DeepSeek V3.2 |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Context Window | 163,840 tokens | 131,072 tokens |
| Agent Suitability | 78/100 | N/A |
| Time to First Token (TTFT) | 1800 ms | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-01-20 | 2025-12-01 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek R1
$0.70
DeepSeek V3.2
$0.23
Output Price per Million Tokens
DeepSeek R1
$2.50
DeepSeek V3.2
$0.34
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
DeepSeek R1 Quirks & Gotchas
- ▸Reasoning model — not designed for high-frequency tool calling
- ▸Pair with a smaller model (V4 Flash) for routing and use R1 for complex reasoning only
DeepSeek V3.2 Quirks & Gotchas
No developer gotchas reported.