DeepSeek vs Qwen: Choosing Between China's Two Big Open-Weight Families
DeepSeek and Alibaba's Qwen are the two Chinese open-weight model families most often compared, but their strategies are opposites: one concentrates everything on a small number of high-value models, the other spreads into a full family covering every size and modality. Here is a side-by-side on pricing, context, Chinese, coding, speed, API, and fit.
The short answer
Choose DeepSeek if you —
- Mostly do code, maths, and analysis that needs multi-step reasoning
- Are budget-sensitive and want near-flagship reasoning at the lowest possible unit price
- Only need one or two models to cover everything and do not want to spend time on model selection
- Can live with the higher time-to-first-token that reasoning models bring
Choose Qwen if you —
- Need to size models per scenario: small on real-time paths, large for offline batch work
- Handle images, audio, or languages beyond Chinese and English
- Plan to self-host on limited GPU budget and need versions that actually fit
- Need cloud-vendor quota management, auditing, and compliance on the enterprise side
Side-by-side
| Item | DeepSeekDeepSeek AI | QwenAlibaba |
|---|---|---|
| Pricingcheck the official page | Official API pricing has long sat at the low end of the Chinese market; weights are open, so self-hosting costs only compute. | Official API priced by model size, with very cheap small models; also open weights, with far more size options for self-hosting. |
| Contextcheck the official page | The flagship models carry enough context for ordinary long documents and code; very long context is not the headline focus. | EdgeThe family includes versions built specifically for very long context, giving more room for whole books or long meeting archives. |
| Chinese | Solid Chinese reasoning and formal writing with clear logical chains — steady for analytical content. | Equally first-tier Chinese, with broader multilingual coverage that pays off beyond Chinese and English. |
| Coding | EdgeCode and mathematical reasoning are the long-running focus, with the better reputation for hard problems per unit of cost. | A dedicated code series lets you match model size to project size; the general-purpose versions lag the specialised ones on code. |
| Speed | Reasoning models emit their thinking before the answer, so time-to-first-token is noticeably higher in exchange for better reasoning. | EdgeSmall models respond very fast and suit real-time paths; larger versions are comparable to peers of the same class. |
| API | The official API is OpenAI-compatible — swap the base URL and you are in — and relay support is widespread. | Served through Alibaba Cloud with an OpenAI-compatible mode too, plus more complete enterprise plumbing (quotas, auditing, compliance). |
| Open weights | Weights are published under permissive terms, but with few size options; running the flagship locally demands serious VRAM. | EdgeEverything from laptop-sized models to flagships, plus vision and audio variants — far more room to pick a self-hosting target. |
| Best for | Developers chasing the strongest reasoning per unit of spend on code, maths, and complex analysis. | Teams that need to size models per scenario, want multimodal or multilingual coverage, or depend on cloud-vendor plumbing. |
Pricing, context limits, and model versions change often. This table describes structure and direction of difference, not exact figures — confirm on the vendor's own pricing page before you buy.
Two very different product strategies
Asking which of these is "stronger" is the wrong question. They never set out to do the same thing.
DeepSeek's approach is few and sharp: a restrained number of models, with resources concentrated on reasoning ability and cost efficiency. The question it wants to answer is — for the same dollar, who solves the harder problem? That focus built its reputation on code and maths, tasks that need multi-step reasoning, and it keeps its API pricing at the low end of the Chinese market.
Qwen's approach is full coverage: from models small enough to run on a laptop up to flagships, from pure text to vision and audio, plus a dedicated code series — a complete family. The question it wants to answer is — whatever your scenario and however much VRAM you have, there is a usable version here.
So step one is not to look at benchmarks. It is to decide whether you need one model that is strong enough, or a rack of models you can pick from.
Cost: there is more than unit price
Both official APIs are competitively priced, and comparing headline unit prices is not very useful — pricing changes often and tiers differ enormously. Three other calculations matter more.
The hidden cost of reasoning models. A model that emits its thinking before answering consumes far more tokens than one that answers directly. However low the unit price, multiply usage by three to five and total cost can end up higher. Always run your own real workload and measure actual consumption instead of reading the price table.
Savings from right-sizing. Within one application, not every request needs the flagship. Classification, extraction, and format conversion are fine on small models; using a large one there is pure waste. Qwen's family gives more room here — small models for simple tasks, switching to a large one only for real reasoning, which pulls total cost down substantially.
The self-hosting calculation. Low-frequency use is almost always cheaper on the official API, because idle GPUs cost far more than tokens. Self-hosting only makes sense at genuinely large volume, or when data cannot leave your network — and remember to price in the ops effort.
Chinese and multilingual
Both are first-tier in Chinese and indistinguishable on everyday tasks. DeepSeek's Chinese leans analytical — steady at explanations and reports with clear logical chains. Qwen covers more ground, with a clear advantage on languages beyond Chinese and English.
If your product serves multilingual users, that is a real dividing line. If you only ship Chinese, ignore this row.
Coding: general model versus specialist
DeepSeek treats code and maths as its primary target, and it has the better reputation for solving hard programming problems per unit of cost — the direct reason many developers make it the default.
Qwen's answer is a dedicated code series. Pick the right version and it performs; use a general-purpose version for code and the results are noticeably weaker. That creates a practical problem: you have to know which version to reach for. A large family is both an advantage and an overhead.
Access and enterprise plumbing
Both APIs offer an OpenAI-compatible mode, which usually means existing code only needs a new base URL and key — migration cost is close to zero. Relay and gateway support is good for both.
The difference is enterprise plumbing. Qwen is served through Alibaba Cloud, so quota management, usage auditing, invoicing, and compliance flows are all off the shelf — friendlier for teams that have to go through procurement. DeepSeek's onboarding is lighter, with individual developers signing up and going, but the enterprise-side scaffolding is comparatively simple.
There is one more point domestic teams care about: both are directly available inside mainland China with no extra network arrangements. Compared with overseas models that need a relay to call reliably, that is a real engineering advantage — one fewer failure point and one fewer uncontrolled cost.
Our recommendation
If your work centres on code, maths, and complex analysis, and you want one model to cover everything, choose DeepSeek. It is low-maintenance and cost-effective.
If you need to size models per scenario, want multimodal or multilingual coverage, or need full cloud plumbing on the enterprise side, choose Qwen. The breadth of the family is the point.
Most teams end up using both. Put them behind one gateway and route by task type: simple work to small models, hard reasoning to the strong one, multimodal to the specialist. The cost reduction from that pattern is usually larger than whatever you gain by agonising over vendors — see the model routing entry and our AI API gateway explainer for how to set it up.
FAQ
- Both are open source — does that mean the same thing?
- Both publish open weights you can download and self-host, but the licence terms and commercial restrictions are not identical and change between versions. Before shipping a commercial product, read the licence file for the specific version you plan to use rather than relying on the word 'open'.
- Is self-hosting worth it?
- It depends on volume. Low-frequency use is almost always cheaper on the official API — idle GPUs cost far more than tokens. Self-hosting only makes sense when your call volume is genuinely large or your data cannot leave the network. Do not forget to price in the ops effort.
- Which is easier to use from inside China?
- Both are directly available domestically with no extra network setup, which is their biggest practical advantage over overseas models. For procurement, Alibaba Cloud's compliance and invoicing flow is more mature; individual developers can sign up for either directly.
- Can they replace GPT and Claude?
- On Chinese content, ordinary coding, and analysis, the gap has narrowed to the point most workloads cannot tell. Where a gap remains is long-horizon agent tasks and cutting-edge multimodal work. The sensible approach is routing by task type rather than picking one winner.
- Why doesn't the table list exact prices and context lengths?
- Both vendors ship model versions and pricing updates frequently, and hard numbers would mislead within weeks. The table describes structural differences only — check the official pricing page and model docs for current figures.