DeepSeek V3.2 vs Llama 4 Maverick
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 Llama 4 Maverick.
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...
Llama 4 Maverick
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
Technical Specifications
| Specification | DeepSeek V3.2 | Llama 4 Maverick |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 131,072 tokens | 1,048,576 tokens |
| Agent Suitability | N/A | N/A |
| Time to First Token (TTFT) | N/A | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2025-12-01 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V3.2
$0.23
Llama 4 Maverick
$0.15
Output Price per Million Tokens
DeepSeek V3.2
$0.34
Llama 4 Maverick
$0.60
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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.
Llama 4 Maverick Quirks & Gotchas
No developer gotchas reported.