DeepSeek V4 vs Gemini 3.1 Pro
DeepSeek V4 vs Gemini 3.1 Pro: the full comparison
Here is a surprising fact: on SWE-bench Verified, the reference test for autonomous coding, DeepSeek V4-Pro and Gemini 3.1 Pro score exactly the same: 80.6%. An open-source Chinese model tied with Google's flagship. The difference is elsewhere: DeepSeek is open (MIT license) and about 14 times cheaper, while Gemini keeps a clear edge on multimodal (image, audio, video). Here is the honest comparison to choose based on your use case.
Head to head
DeepSeek V4 vs Gemini 3.1 Pro on the criteria that matter
Two models neck and neck on code and reasoning, but a gulf on price and openness.
Autonomous code: a perfect tie on SWE-bench
This is the most striking point. DeepSeek V4-Pro and Gemini 3.1 Pro both post 80.6% on SWE-bench Verified, the most respected benchmark for autonomous code editing. In other words, an open-source model reaches the exact level of Google's flagship on this task. On Terminal-Bench they are also nearly tied (67.9% versus 68.5%).
Price: DeepSeek is about 14 times cheaper
Via the API, Gemini 3.1 Pro costs $2 for input and $12 for output per million tokens. DeepSeek V4-Pro costs about $0.44 for input and $0.87 for output, roughly 14 times cheaper on output. And V4-Flash ($0.14 / $0.28) goes even lower. For an equivalent coding result, the DeepSeek bill is incomparably lighter.
Open source: DeepSeek is open, Gemini is closed
DeepSeek V4 is released under the MIT license: downloadable weights, self-hosting, adaptation, and commercial use are all free. Gemini 3.1 Pro is a closed service, available only through Google's API and products. For sovereignty, data control, and customization, DeepSeek offers freedom that Gemini does not.
Reasoning and knowledge: slight edge to Gemini
On the GPQA Diamond scientific reasoning test, Gemini 3.1 Pro leads (94.3% versus 90.1%). On MMLU general knowledge, Gemini is also ahead (93.6% versus 90.1%). The gaps are real but modest. DeepSeek stays in the leading pack, delivering that level as open source and far cheaper.
Multimodal: Gemini's clear advantage
This is Gemini's real strength. It natively handles text, image, audio, video, and code in a single model, whereas DeepSeek V4 focuses on text and code, with no native multimodal. If your work involves image analysis, audio transcription, or video understanding, Gemini is the obvious choice. For text and code, the gap disappears.
Context: both hold one million tokens
DeepSeek V4 handles up to 1M tokens of context. Gemini 3.1 Pro also accepts 1M tokens on input (with 64K output). Both can therefore process entire codebases or very long documents. Beware of "2 million token" rumors about Gemini: those refer to a 3.5 Pro version that is not yet available, not the current 3.1 Pro.
Privacy and sovereignty: neither one is French
A key point for use in Europe. Gemini hosts with Google (United States), while DeepSeek (official app) routes data through China, where France's CNIL has opened a review. Neither is a sovereign European solution. The real workaround on the DeepSeek side is self-hosting the open-source version. If European sovereignty is the priority, the alternative to look at is Mistral's Le Chat.
In short: which to choose?
Choose DeepSeek V4 if you want Gemini's code level at 1/14 of the price, open source, with self-hosting possible. Choose Gemini 3.1 Pro if multimodal (image, audio, video) is central, or if you are already in the Google ecosystem. For code and text on a controlled budget, DeepSeek is hard to beat.
Quick verdict
When to choose DeepSeek V4 over Gemini 3.1 Pro
Same code level, two opposite economic models.
Choose DeepSeek V4 when
- You want Gemini's code level at about 1/14 of the price
- Open source (MIT) and self-hosting matter
- Your use is mostly text and code
- You want free, powerful access without a subscription
Choose Gemini 3.1 Pro when
- Multimodal image, audio, and video is central
- You are already in the Google ecosystem
- A slight edge on GPQA and MMLU is a priority
- Cost per token is not a constraint
Benchmarks
DeepSeek V4 vs Gemini 3.1 Pro: the official numbers
Comparison based on data published by DeepSeek and Google. Tests with different methodologies are not placed in the same row.
On paper, DeepSeek V4-Pro and Gemini 3.1 Pro are two of the best coding models in the world, and on SWE-bench Verified they are a perfect tie (80.6%). The table below sticks to officially comparable benchmarks. We do not put measures of different scales side by side (for example, DeepSeek publishes a pass rate on LiveCodeBench while Google publishes an Elo score on a Pro variant: that is not the same axis, so we do not pit them against each other). What remains indisputable is the price gap and the open-source status.
Code: a perfect tie, 80.6% SWE-bench Verified for both
Output price: DeepSeek V4-Pro $0.87 vs Gemini 3.1 Pro $12 per M tokens (about 14x)
Open source: DeepSeek MIT, Gemini closed
Science: Gemini leads on GPQA Diamond (94.3% vs 90.1%)
Multimodal: Gemini handles image, audio, and video; DeepSeek focuses on text and code
Context: 1M input tokens for both
Comparison on official data
DeepSeek V4 vs Gemini 3.1 Pro
Head to head limited to officially comparable benchmarks.
| Benchmark | V4-Pro Max Open source MIT DeepSeek | V4-Flash Fast and free DeepSeek | Gemini 3.1 Pro Closed Google |
|---|---|---|---|
SWE-bench Verified Autonomous code editing | 80.6% | 79.0% | 80.6% |
Terminal-Bench Terminal operations | 67.9% | 56.9% | 68.5% |
GPQA Diamond Scientific reasoning | 90.1% | 88.1% | 94.3% |
MMLU General knowledge | 90.1% | 88.7% | 93.6% |
ARC-AGI-2 Abstract reasoning | Not published | - | 77.1% |
Context window Input tokens | 1M | 1M | 1M |
Input price Per million tokens | ~$0.44 | $0.14 | $2 |
Output price Per million tokens | ~$0.87 | $0.28 | $12 |
Multimodal Image, audio, video | Text and code | Text and code | Yes, native |
License Model openness | MIT (open) | MIT (open) | Closed |
Sources: official model cards from DeepSeek (April 2026, updated pricing) and Google (Gemini 3.1 Pro, February 2026). LiveCodeBench scores are omitted because the two vendors use different measures (pass rate vs Elo), not directly comparable.
Tied on code
An open-source model tied with Google's flagship
That DeepSeek V4-Pro matches Gemini 3.1 Pro's exact score on SWE-bench Verified (80.6%) is an event in itself. It means you can get the coding level of Google's best closed model with an open, self-hostable model billed at a fraction of the price. For development teams, the equation changes: no need to pay a premium rate for this level of coding performance.
- SWE-bench Verified: 80.6% for both models
- Terminal-Bench: 67.9% vs 68.5%, nearly tied
- DeepSeek V4 under the MIT license, self-hosting possible
- Output price about 14 times lower than Gemini
Where Gemini keeps the edge
Multimodal remains Gemini's strong ground
Let's be honest: on multimodal, Gemini 3.1 Pro clearly outpaces DeepSeek V4. Gemini natively understands image, audio, and video in a single model, whereas DeepSeek focuses on text and code. If your workflow involves analyzing images, transcribing audio, or understanding video, Gemini is the better choice. But as soon as it comes to text, code, and budget, DeepSeek takes the lead again.
- Gemini: text, image, audio, video, and code natively
- DeepSeek V4: excellent on text and code, no native multimodal
- For image or audio analysis, prefer Gemini
- For code and text on a small budget, prefer DeepSeek
Related pages
Continue the comparison
DeepSeek against the other major models, and the detail of each version.
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Same code level as Gemini, at a fraction of the price: try it
Open the chat and run DeepSeek V4 on a real coding problem or a long document. Free, no install. The best way to verify the SWE-bench tie is to test both on your own work.