Language Models

DeepSeek V3.2 vs Gemini 3 Pro

DeepSeek V3.2 vs Gemini 3 Pro. Open-weight value against Google's 1M-token multimodal frontier model. Cost, context and capability compared.

DeepSeek V3.2 and Gemini 3 Pro sit at opposite ends of the cost-capability curve. DeepSeek is the open-weight value model — a 671B MoE with sparse attention, strong text and code reasoning, ultra-low pricing and full self-hostability. It's ideal for high-volume, cost-sensitive, text-heavy workloads where you want control.

Gemini 3 Pro is the closed multimodal frontier model with a 1M-token context and leading reasoning scores across math, science and competitive coding. It wins decisively on multimodality, context length and peak quality; DeepSeek wins decisively on cost and openness. For text and code at scale on a budget, DeepSeek is hard to beat; for multimodal, very-long-context, top-quality work, Gemini 3 Pro leads.

Which should you choose?

DeepSeek V3.2if you want cheap, open-weight text and code reasoning at scale.
Gemini 3 Proif you need multimodality, 1M-token context or top quality.

The verdict

Gemini 3 Pro wins on multimodality, context size and peak quality; DeepSeek V3.2 wins on cost, openness and text/code value. Pick by whether you need scale-and-multimodality or maximum value.

Watch the comparison

DeepSeek V3.2 vs Gemini 3 Pro — video reviews on YouTubeWatch hands-on tests and side-by-side demos

Frequently asked questions

Is DeepSeek multimodal?

No — DeepSeek V3.2 handles text and code; Gemini 3 Pro adds native image, video and audio understanding.

Which is cheaper?

DeepSeek V3.2, by a wide margin, with open weights for self-hosting.

Sources & further reading

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