Llama 4 Maverick vs DeepSeek V3.2
Meta's Llama 4 Maverick against DeepSeek's DeepSeek V3.2 — pricing, benchmarks, context, and best use cases compared side by side.
DeepSeek V3.2 leads on quality (Elo 1320 vs 1310) and is also 38% cheaper — a clear value winner. Llama 4 Maverick offers a larger context window (1M vs 128K).
| Input Price | $0.27/1M | $0.28/1M |
| Output Price | $0.85/1M | $0.42/1M |
| Blended Price | $0.56/1M | $0.35/1M |
| LMSYS Elo | 1310 | 1320 |
| Context Window | 1,000,000 | 128,000 |
| Provider | Meta | DeepSeek |
Pricing breakdown
When comparing LLM API pricing, Llama 4 Maverick charges $0.27 per 1M input tokens compared to DeepSeek V3.2's $0.28 — a 4% difference. For output tokens, DeepSeek V3.2 costs $0.42/1M versus $0.85/1M for Llama 4 Maverick. On a blended basis (averaging input and output), DeepSeek V3.2 comes in at $0.35/1M tokens versus $0.56/1M for Llama 4 Maverick.
Quality & benchmarks
On the LMSYS Chatbot Arena leaderboard — a crowd-sourced benchmark based on blind human preference voting — DeepSeek V3.2 scores 1320 Elo compared to Llama 4 Maverick's 1310, a 10-point advantage. While DeepSeek V3.2 has the edge, both models are competitive. DeepSeek V3.2 excels at budget general-purpose tasks, self-hosting, and cost-sensitive deployments, while Llama 4 Maverick is well-suited for open-source deployments, long-context tasks, and cost-sensitive applications.
Context window comparison
Llama 4 Maverick provides a significantly larger context window at 1M tokens compared to DeepSeek V3.2's 128K tokens — 7.8x more capacity for processing long documents, large codebases, or extended conversations. With 1M tokens, Llama 4 Maverick can handle entire books, repositories, or multi-document analysis in a single prompt.
Monthly cost estimate
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