Qwen vs DeepSeek
As of 2026-10-13, DeepSeek (DeepSeek: DeepSeek V4 Flash 0731, 8.1/10) is among the best models in our daily benchmark. Qwen is not among them today — we evaluate over 350 models and present the strongest.
Qwen and DeepSeek are the two model families that have pushed prices down the hardest. Both are Chinese, both are open, and both make the cost argument against AI almost disappear.
Side by side
| Qwen | DeepSeek | |
|---|---|---|
| In today’s benchmark | Not among today’s best | DeepSeek: DeepSeek V4 Flash 0731 |
| Score | — | 8.1/10 |
| Language | — | 8.7/10 |
| Speed | — | 452 t/s |
| Input price/1M | $2.00 | $0.02 |
What separates them?
DeepSeek has been strongest on reasoning, code and maths. Qwen covers a wider range of sizes — from small models that run on a laptop to very large ones — and often has better multilingual coverage. On Scandinavian languages it varies between versions, which is exactly why we measure daily: the table above shows who leads right now.
The same caveat applies to both
Both are developed in China, and if you use the vendor’s own cloud, data is processed outside the EU. To use them with sensitive data you have to run the models yourself or through a European provider — both are open, so that is genuinely possible.
Frequently asked questions
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How to get more value from this comparison
Use this comparison as a practical decision aid, not as an advertising list. HvilkenAI is built to make AI selection easier when the market changes quickly and model names alone say little about real quality.
Start with the task you actually need to solve. A model that is good at short chat is not automatically best for long documents, code, customer-facing text, research or work involving sensitive information. Consider language quality, price, speed, stability and how much editing the answer requires before choosing a subscription or API.
We evaluate over 350 AI models and present the most useful signals in a format that works for everyday users, students, businesses and developers. The exact calculation is proprietary, but the assessment is based on standardised tasks, updated benchmark data and editorial review of whether the recommendations make sense in practical use.
For important decisions, run a small test of your own: use the same prompt, language and expected format in two or three relevant models. Look at precision, source handling, tone, speed and how easy the answer is to verify. That makes the benchmark a time-saving starting point, not a final answer you must follow blindly.
Use the links on the page as well. One page rarely gives the whole picture: the benchmark shows today’s numbers, the AI test translates your need into concrete recommendations, and the privacy/terms pages explain which data and limitations you should consider. Used together, these sources make the choice more robust than following a model name from social media.
If the result will be used at work, document why the tool was chosen: which task it solves, which alternatives were considered, which data should not be shared and how the answer will be quality-checked. That makes it easier to switch model later if price, quality or provider terms change.
When a page is included in the sitemap, it should also stand on its own as a useful explanation. That is why we emphasise practical choices, concrete control questions and clear limits for how data can be used.
The goal is that the reader can move on with a safer choice after a few minutes, without already knowing every model or provider.
Pay particular attention to whether the recommendation fits the language, price and risk in your own situation.
In short: choose what you can verify.
When free is enough
Choose free or low-cost first when the task is simple, risk is low and you can verify the answer yourself.
When paid is worth it
Paid models can be worth the cost when documents are long, precision matters or you save real working time.
When to wait
If the need is unclear, start with the test and benchmark before committing to a provider.