Gemini wins on multimodality (native audio and video understanding) and Google-ecosystem integration; DeepSeek V4 wins on openness, price, and deployment freedom. Both offer 1M-token context — the difference is that DeepSeek's comes with downloadable MIT weights. Text-heavy workloads favor DeepSeek's economics; anything built around video, voice, or Workspace favors Gemini.
DeepSeek vs Gemini at a glance
| DeepSeek V4 | Gemini | |
|---|---|---|
| Vendor | DeepSeek (China) | Google (United States) |
| Open weights | Yes — downloadable | No |
| License | MIT — weights free to download, self-host, fine-tune, and use commercially | Proprietary — API and subscription access only; no weights (Gemma is Google's separate open-weight line) |
| Parameters | 1.6T (49B active) | Undisclosed |
| Context window | 1M tokens | — |
| Modalities | text | text, image, audio, video |
| Pricing | $1.74 in / $3.48 out per 1M tokens | Free tier via Google products, paid AI subscriptions, and per-token Vertex/AI Studio API pricing; Google's open-weight Gemma models are a separate, much smaller line. |
| Released | 2026-04-24 | — |
Specs and pricing verified July 2026.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
About Gemini
Gemini is Google's proprietary flagship family — natively multimodal across text, image, audio, and video, with unmatched distribution through Search, Android, and Workspace.
The weights are closed (Google's open-weight efforts ship separately as Gemma). Comparisons with open models hinge on the same trade: Gemini brings frontier multimodality and Google-scale integration; open-weight models bring self-hosting, fine-tuning, auditability, and dramatically lower per-token cost.
Choosing between them
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is DeepSeek V4 better than Gemini?
Gemini wins on multimodality (native audio and video understanding) and Google-ecosystem integration; DeepSeek V4 wins on openness, price, and deployment freedom. Both offer 1M-token context — the difference is that DeepSeek's comes with downloadable MIT weights. Text-heavy workloads favor DeepSeek's economics; anything built around video, voice, or Workspace favors Gemini.
Which is cheaper: DeepSeek V4 or Gemini?
DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Gemini: Free tier via Google products, paid AI subscriptions, and per-token Vertex/AI Studio API pricing; Google's open-weight Gemma models are a separate, much smaller line.. As an open-weight model, DeepSeek V4 can also be self-hosted, which caps cost at your own hardware.
Can I self-host DeepSeek V4 and Gemini?
DeepSeek V4: yes — weights are published under MIT. Gemini: no — it is a proprietary service with no downloadable weights.