Qwen vs DeepSeek comes down to breadth versus focus: Qwen ships a sprawling, Apache 2.0-licensed lineup of open models in dozens of sizes with genuinely strong multilingual output, while DeepSeek keeps a tighter two-model stack and leans hard on cheap, visible-reasoning performance for coding and math. Both chat apps are free with no consumer subscription, so the real decision sits in the API and in what each model is actually good at.
Short answer: Qwen and DeepSeek are both free to chat with and cheap to call by API. Qwen wins on model choice, self-hosting flexibility, and multilingual writing; DeepSeek wins on lower output pricing and a visible reasoning trace that's genuinely useful for coding and math. Pick Qwen for variety and language coverage, DeepSeek for tight, cheap reasoning work.
Disclosure: AISagely may earn a commission if you buy through links on this page, at no extra cost to you. It never changes the verdict — I recommend what I would actually use, and I flag when the free tier is the smarter call.
I keep both open on different days depending on the job: Qwen when I need a translation, a multilingual draft, or want to try one of its smaller open models locally, DeepSeek when I want a fast, cheap answer to a coding or logic problem with the reasoning shown. In my testing this week I ran the same briefs through both to see where the gap actually sits, not where it sat a year ago. Here's what I found.
Quick verdict
If you write or ship product in more than one language, want the widest range of open-weight model sizes to pick from, or care about self-hosting options, Qwen is the stronger pick — its Apache 2.0 license means you can run its smaller models yourself with no API bill at all. If your main job is code, math, or structured reasoning and you want the cheapest possible output tokens plus a visible thinking trace to sanity-check answers, DeepSeek edges ahead. Neither charges for its consumer chat app, so trying both costs nothing.
Qwen vs DeepSeek at a glance
| Qwen | DeepSeek | |
|---|---|---|
| Best for | Multilingual writing, open-weight variety, self-hosting | Cheap coding/reasoning with a visible thinking trace |
| Consumer app price | Free — no paid subscription tier | Free — no paid subscription tier |
| API price (per 1M tokens) | Qwen-Flash: $0.05 in / $0.40 out | V4-Flash: $0.14 in / $0.28 out |
| Standout feature | Apache 2.0 open models from 0.6B to 480B+, strong at dozens of languages | DeepThink reasoning trace, lower output cost at scale |
| Limitation | Confusing, fast-moving model lineup (3.5/3.6/3.7 tiers overlap) | Only two consumer-facing model tiers; fewer open sizes to choose from |
Prices and model names change often on both sides, so confirm current numbers on Alibaba Cloud Model Studio’s pricing page and DeepSeek’s API pricing page before you commit. The figures here reflect what those pages showed when I wrote this, dated August 2026.
Pricing compared
Both chat apps are free with no paid consumer tier — that part isn't close. Qwen Chat at chat.qwen.ai and DeepSeek's chat app both let you talk to a capable model, upload files, and switch reasoning modes without ever hitting a paywall.
The gap shows up on the API. Qwen's cheapest text model, Qwen-Flash, runs $0.05 per million input tokens and $0.40 per million output tokens for prompts under 256K tokens, billed through Alibaba Cloud Model Studio. Its mid-tier Qwen-Plus model runs $0.40 in / $1.20 out per million tokens in the same context window. The current flagship, Qwen3.7-Max, lists at $2.50 in / $7.50 out per million tokens, though a 50% promotional rate was active when I checked, bringing it closer to $1.25/$3.75. DeepSeek's cheaper model, V4-Flash, runs $0.14 per million input tokens on a cache miss (just $0.0028 on a cache hit) and $0.28 per million output tokens. Its stronger V4-Pro model runs $0.435 in / $0.87 out per million tokens.
Line them up and neither tool wins outright on price — it splits by direction. Qwen-Flash undercuts DeepSeek V4-Flash on input tokens by nearly 3x ($0.05 vs $0.14), but DeepSeek is cheaper on output tokens ($0.28 vs $0.40), which matters more for anything that generates long responses, like drafting or summarizing. New Alibaba Cloud accounts also get a one-time 1-million-token trial per eligible model on the Singapore endpoint, good for 90 days, which DeepSeek doesn't offer in the same form. If your workload is prompt-heavy (large documents, big system prompts) Qwen-Flash is cheaper to run; if it's output-heavy (long-form generation), DeepSeek's flash tier wins per token.
Output quality: my test
I gave both the same brief: "Write a SQL query that returns the second-highest salary in each department from an employees table, then explain your approach in two sentences." I ran it on Qwen's free chat app (default model) and DeepSeek's free chat app (V4-Flash), no special settings on either.
Both returned correct, working SQL using a window function approach, and both explanations were accurate. DeepSeek's explanation was tighter — two clean sentences, no extra scaffolding. When I turned on its DeepThink mode and asked it to handle a tie for second-highest salary, it worked through the ambiguity step by step in a visible reasoning trace before landing on a DENSE_RANK() fix, which was genuinely useful for seeing where the logic could have broken. Qwen's answer to the same follow-up was also correct but didn't show its work in the same way — you get the fixed query without a visible reasoning path unless you're calling one of its thinking-mode models through the API.
Where Qwen pulled ahead was a different test entirely: I asked both to translate a short paragraph of product marketing copy into Spanish and Japanese and judged the result on how natural it read to a fluent speaker. Qwen's Spanish output read like something a native copywriter would actually write, and its Japanese version got register and formality right without me asking for it. DeepSeek's translations were accurate but noticeably more literal in both languages — correct, but the kind of correct that reads like a translation rather than original copy.
That split held across every test I ran: DeepSeek is sharper and more transparent on pure logic and code, Qwen is stronger the moment the task involves more than one language or you want a smaller model you can run yourself. Which one matters more depends entirely on what you're building.
Where Qwen wins
Model variety is Qwen's real edge. Alibaba has released Qwen3 in sizes from 0.6B parameters up through 480B+ mixture-of-experts models, most of them Apache 2.0 licensed — meaning you can download and self-host several of them for free instead of paying per token at all, something DeepSeek's more limited open lineup doesn't match at the same range. For anyone doing high-volume, repetitive API work where a smaller self-hosted model is good enough, that flexibility compounds fast; our guide to AI tools for coding beyond ChatGPT covers where an open-weight model like Qwen3-Coder fits into that kind of setup.
In my testing, Qwen's multilingual output was consistently more natural than DeepSeek's, which lines up with how the model family has been trained and benchmarked. If your work involves writing, translating, or supporting customers in more than one language, Qwen's chat app and its Qwen-Flash API tier cover a lot of ground for very little money.
Where DeepSeek wins
DeepSeek is the sharper, cheaper pick for pure reasoning work. Its DeepThink mode shows a step-by-step trace before landing on an answer, which caught an edge case in my SQL test that DeepSeek's plain mode and Qwen's default model both missed on the first pass. That visible reasoning is worth more than it sounds like once you're debugging a wrong answer instead of just accepting one — see how to use DeepSeek for how to turn it on.
Cost at the output end is DeepSeek's other advantage: V4-Flash's $0.28 per million output tokens beats Qwen-Flash's $0.40, and the cache-hit discount (down to $0.0028 per million input tokens on a repeated system prompt) makes DeepSeek noticeably cheaper for any workflow that reuses the same prompt structure over and over, like a coding agent calling the same instructions on every turn — our guide to using DeepSeek for coding walks through exactly that setup.
Who should choose which
- Choose Qwen if: you need multilingual output, want the widest range of open-weight model sizes, or plan to self-host a smaller model instead of paying per token. It's free to start: Qwen.
- Choose DeepSeek if: your work is mostly code, math, or structured reasoning, and you want the cheapest output tokens plus a visible trace you can actually check. It's free to start: Deepseek.
- Choose both if: you're fine running two free tools side by side — Qwen for anything multilingual or where you'd rather self-host, DeepSeek for anything technical where cheap, checkable reasoning matters. Our AI tool ratings page has more options if neither fully fits your workload.
Neither tool asks you to pay before you find out if it's any good — that's the real headline here, not which one is "better." Test both on your own workload before you touch the API billing page; our free AI tools roundup is a good place to start if you want more no-cost options to compare against these two.
How I tested
I ran identical prompts through Qwen's free chat app (default model) and DeepSeek's free chat app (V4-Flash, with a DeepThink follow-up) on the same day, covering a coding brief, a follow-up edge case, and a two-language translation task. I judged code on whether it ran correctly on the first try and handled the follow-up edge case, and judged translations on whether a fluent reader would call the result natural rather than literal. Pricing in this piece comes from each vendor's own documentation, checked the week of writing — see DeepSeek vs ChatGPT and DeepSeek vs Gemini for how DeepSeek stacks up against two other options.
Frequently Asked Questions
Is Qwen better than DeepSeek?
For multilingual writing, translation, and having a wide range of open-weight model sizes to choose from, Qwen is ahead. For cheap, visible-reasoning coding and math work, DeepSeek is ahead. "Better" depends on whether your work is mostly language-heavy or mostly logic-heavy.
Is DeepSeek cheaper than Qwen?
It depends on direction. Qwen-Flash is cheaper on input tokens ($0.05 vs $0.14 per million), but DeepSeek V4-Flash is cheaper on output tokens ($0.28 vs $0.40 per million). Both consumer chat apps are free with no subscription tier.
Can I switch from Qwen to DeepSeek?
Yes, easily — neither locks you in, and both chat apps are free to open and stop using anytime. Many people run both, using Qwen for multilingual or open-weight work and DeepSeek for cheap, checkable coding and reasoning tasks.
Which is easier for beginners, Qwen or DeepSeek?
Both chat apps are simple to open and start typing into, with no signup friction beyond a basic account. DeepSeek's DeepThink toggle is a genuinely beginner-friendly way to see how the model reached an answer; Qwen's learning curve mostly shows up if you start exploring its many model sizes on the API side.