DeepSeek: DeepSeek V3.2 Speciale vs Meta: Llama 4 Maverick
Side-by-side comparison — pricing, context window, capabilities, and live leaderboard data.
Key differences
- Meta: Llama 4 Maverick has a 1049K-token context window — 6.4× larger than DeepSeek: DeepSeek V3.2 Speciale's 164K.
- Meta: Llama 4 Maverick is 48% cheaper per 1K input tokens than DeepSeek: DeepSeek V3.2 Speciale ($0.0001 vs $0.0003).
- DeepSeek: DeepSeek V3.2 Speciale is built by DeepSeek; Meta: Llama 4 Maverick is built by Meta.
- Meta: Llama 4 Maverick supports vision (image inputs); the other does not.
Specifications
| DeepSeek: DeepSeek V3.2 Speciale | Meta: Llama 4 Maverick | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context window | 164K | 1049K |
| Max output tokens | 8192 | 8192 |
| Input price / 1K tokens | $0.0003 | $0.0001 |
| Output price / 1K tokens | $0.0004 | $0.0006 |
| Vision support | No | Yes |
| Function calling | No | No |
| License | Open Source | Proprietary |
About DeepSeek: DeepSeek V3.2 Speciale
CN·OpenDeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized for maximum reasoning and agentic performance. It builds on DeepSeek Sparse Attention (DSA) for efficient long-context processing, then scales post-training reinforcement learning...
View DeepSeek: DeepSeek V3.2 Speciale reliability and benchmark history →About Meta: Llama 4 Maverick
US·ClosedLlama 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...
View Meta: Llama 4 Maverick reliability and benchmark history →Try them yourself
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