DeepSeek V3.2 vs MiniMax M1
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 V3.2 and MiniMax M1.
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...
MiniMax M1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
Technical Specifications
| Specification | DeepSeek V3.2 | MiniMax M1 |
|---|---|---|
| Provider | DeepSeek | MiniMax |
| Context Window | 131,072 tokens | 1,000,000 tokens |
| Agent Suitability | N/A | N/A |
| Time to First Token (TTFT) | N/A | N/A |
| Deployment Model | self hostable | managed api |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2025-12-01 | 2025-06-17 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V3.2
$0.23
MiniMax M1
$0.40
Output Price per Million Tokens
DeepSeek V3.2
$0.34
MiniMax M1
$2.20
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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 V3.2 Quirks & Gotchas
No developer gotchas reported.
MiniMax M1 Quirks & Gotchas
No developer gotchas reported.