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
Data sovereignty: the structural difference
Benchmarks move every release cycle; this difference does not. Gemini exists only as a hosted service — every prompt, document, and output is processed on Google’s infrastructure (and that of its cloud providers), under data-processing terms that Google writes and can change. DeepSeek V4 is a set of weights you can download: run it as a private LLM in your own VPC, on-premise, or fully air-gapped, and your data never crosses your network boundary at all.
| DeepSeek V4 | Gemini | |
|---|---|---|
| Where your data is processed | Wherever you deploy it — including air-gapped environments | Google’s hosted infrastructure only |
| Vendor visibility into your data | None when self-hosted | Governed by Google’s data-processing and retention terms |
| Fine-tune on proprietary data in-house | Yes — weights are yours to adapt | Not on your own infrastructure |
| Data-residency and compliance control | Full — you choose jurisdiction and hardware | Limited to the regions and terms the vendor offers |
| Exit and continuity risk | None — a model on your disks cannot be deprecated or repriced | Deprecation, repricing, and policy changes at vendor discretion |
For regulated industries — healthcare, finance, defense, government — this is often the deciding axis before quality enters the discussion: if the data cannot leave, the hosted option is disqualified at the compliance review, whatever the benchmarks say. This is also the gap BasedAI exists to close: open-weight capability, deployed where your data already lives.
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 better for data sovereignty: DeepSeek V4 or Gemini?
DeepSeek V4. As an open-weight model it can run entirely on infrastructure you control — your VPC, your datacenter, or fully air-gapped — so prompts, documents, and outputs never leave your environment, and no vendor retention or training policy applies. Gemini is only available as a hosted service: your data is processed on Google's infrastructure under its data-processing terms.
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.