DeepSeek V3.2 vs Llama 3.3 70B
DeepSeek's DeepSeek V3.2 against Meta's Llama 3.3 70B — pricing, benchmarks, context, and best use cases compared side by side.
DeepSeek V3.2 leads on quality (Elo 1320 vs 1240), while Llama 3.3 70B compensates with 71% lower pricing.
| Input Price | $0.28/1M | $0.10/1M |
| Output Price | $0.42/1M | $0.10/1M |
| Blended Price | $0.35/1M | $0.10/1M |
| LMSYS Elo | 1320 | 1240 |
| Context Window | 128,000 | 128,000 |
| Provider | DeepSeek | Meta |
Pricing breakdown
When comparing LLM API pricing, Llama 3.3 70B charges $0.10 per 1M input tokens compared to DeepSeek V3.2's $0.28 — a 64% difference. For output tokens, Llama 3.3 70B costs $0.10/1M versus $0.42/1M for DeepSeek V3.2. On a blended basis (averaging input and output), Llama 3.3 70B comes in at $0.10/1M tokens versus $0.35/1M for DeepSeek V3.2.
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 3.3 70B's 1240, a 80-point advantage. This is a substantial quality gap that will be noticeable across most tasks. DeepSeek V3.2 is best suited for budget general-purpose tasks, self-hosting, and cost-sensitive deployments, while Llama 3.3 70B is ideal for ultra-budget tasks, fine-tuning, and simple classification/extraction.
Context window comparison
Both DeepSeek V3.2 and Llama 3.3 70B offer a 128K-token context window, making them equally suited for processing large codebases, lengthy documents, and multi-turn conversations.
Monthly cost estimate
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