DeepSeek vs Qwen: open model families compared (2026)

Most comparisons of these two open with benchmark charts. The questions that actually decide things are duller: what license are the weights under, which sizes exist, and will any of them fit on the machine in front of you? Both labs publish downloadable checkpoints while also selling hosted access to models they never release.

Disclosure: I build Outlier, a Mac app that runs models on-device, and six of its seven tiers are Qwen weights. The seventh is Gemma. I'm not a neutral party. Check the Qwen-favorable claims against the sources at the bottom — and I've been equally specific about where DeepSeek is the better answer, because on several axes it plainly is.

Licensing is the under-covered story

Licensing decides whether you can build a business on the weights, and hardly anyone writes about it. Both DeepSeek-V4-Pro and DeepSeek-V4-Flash state on their model cards that the repository and weights are under the MIT License. Qwen's current line is equally blunt — its Qwen3.5 project states "All our open-weight models are licensed under Apache 2.0" — and both licenses are OSI-approved, with neither imposing a use policy or an approval form. One honest caveat on the Qwen side: that Apache 2.0 sweep holds for the current Qwen3 generation the app ships, not for every checkpoint Alibaba has ever released. Qwen2.5-72B is under Alibaba's custom Tongyi Qianwen license, which adds a 100-million-monthly-active-user commercial cap, and Qwen2.5-3B is research-only — so check the license on the specific model you pull, not just the family.

Compare the alternatives. Meta's Llama Community License and Google's Gemma Terms of Use are custom vendor licenses with acceptable-use policies attached, and neither is open source in the OSI sense — those conditions travel downstream. I can speak firsthand to the Gemma side: our Quick tier is a Gemma model, and it's the only one of seven where shipping meant reading a use policy rather than dropping in a license header.

Sizes: a ladder versus two very large rungs

Qwen's open line covers 0.8B, 2B, 4B, 9B, 27B, 35B-A3B, 122B-A10B and 397B-A17B, released in waves between February 16 and April 22, 2026. That ladder lets an app match a checkpoint to a customer's hardware. Qwen3.6-27B is the interesting rung: dense, 64 layers, a hybrid Gated DeltaNet plus Gated Attention architecture, 262,144 tokens of native context extensible to roughly 1,010,000 via YaRN. Its model card bills it as "Flagship-Level Coding in a 27B Dense Model."

DeepSeek builds at another scale. V4-Flash is 284B total with 13B activated, 1M context, mixed FP4/FP8 with the MoE experts in FP4. V4-Pro is 1.6T total with 49B activated and the same 1M context. Sparse MoE keeps activated parameters low, but the full weight set still has to live somewhere. The asymmetry runs the other way too: Qwen's strongest models aren't open. qwen3.7-max, qwen3.7-plus and qwen3.8-max-preview are API-only, so the open family trails its own flagship.

One correction to the "DeepSeek is too big for a laptop" story, since it cuts against the recommendation below: DeepSeek's earlier R1 distill series — 1.5B, 7B, 8B, 14B, 32B and 70B, all MIT — runs on consumer Macs, with the 7B and 8B landing around 5–6 GB at 4-bit. The caveat is what they are: fine-tunes of Qwen2.5 and Llama 3 base models carrying DeepSeek's reasoning traces, not DeepSeek architectures, and a generation behind the V4 line. But if you want DeepSeek-lineage weights on a 16 GB machine, they exist and the license is as clean as V4's.

What runs on a Mac, and at what RAM

These are the exact checkpoints Outlier ships, quantized, on Apple Silicon. Qwen3.5-4B: 2.37 GB, 6 GB RAM, 71.7 tok/s on an M1 Ultra and about 32 tok/s on an M4 MacBook Air. Qwen3.5-9B: 5.04 GB, 12 GB RAM, 53.4 tok/s. Qwen3.6-27B: 15.13 GB, 24 GB RAM, 20.7 tok/s. Qwen3.6-35B-A3B: 19.0 GB, 24 GB RAM. Qwen3.5-397B-A17B: 209 GB, 64 GB RAM, 1.59 tok/s, streaming experts off SSD at ~11 GB peak RSS.

That last tier cuts against my own argument, so I'll say it plainly: expert streaming does let a 397B MoE run on a 64 GB desktop, so "too big for consumer hardware" isn't absolute. But 1.6T at any precision is past what I'd ship.

Strengths by task, and language coverage

On coding, Qwen is where I have numbers. Our Code tier — Qwen3.6-27B — measures 0.866 on HumanEval, and on a blind slice of SWE-bench Verified the local 27B came in around 45% (18 of 40). In a 54-prompt head-to-head against Claude Opus it matched the rubric on 98.9% of checks — our runs, our quantizations.

On breadth, Qwen is ahead: open Qwen3-VL for vision, Qwen3-Omni for text/audio/image/video in with text and audio out, plus ASR, TTS and embedding models. DeepSeek's generosity runs a different direction — DeepGEMM, DeepEP, FlashMLA, 3FS and DeepSeek-OCR are all MIT, infrastructure that quietly made other people's inference faster, local inference included.

On language coverage I'll be honest rather than useful. I have no primary source publishing a verified language list or an apples-to-apples multilingual eval for either family, and I won't invent one. Both labs are Chinese and both treat Chinese and English as first-class. If a lower-resource language decides it, test both yourself.

DeepSeek and Qwen, line by line

Where each family genuinely wins

DeepSeek wins on license simplicity at the frontier. Releasing a 1.6T-parameter checkpoint under MIT is unusual. Nobody else at that scale hands over weights with a license that short.

DeepSeek wins on hosted price, decisively. deepseek-v4-flash is $0.14 per million input tokens on a cache miss and $0.0028 on a cache hit, with $0.28 output — the cache-hit path is fifty times cheaper, which matters for workloads that repeat a long prefix. deepseek-v4-pro runs $0.435 / $0.003625 / $0.87 on the same axes.

DeepSeek wins on user data control. Its terms document an in-product "Improve the model for everyone" toggle plus an email opt-out, and assign output rights: "We assign any rights, title, and interests—if any—in the Outputs of the Services to you." Qwen documents no equivalent toggle; its policy trains on de-identified User Content and Feedback under a legitimate-interests basis, offering only generic rights to object or withdraw consent by DPO email.

Qwen wins on the ladder, the modalities and the cadence. Eight open sizes in one quarter is an upgrade path, not a dead end. Qwen Code, its Apache-2.0 terminal agent, deliberately speaks to "OpenAI, Anthropic, Gemini, and Qwen APIs. Any third-party provider or local model (Ollama / vLLM)," and states it doesn't train on your prompts or code. Qwen Studio ships native clients on Web, iOS, Android, macOS and Windows.

Both carry caveats, and the Alibaba one runs the opposite way to what you might assume. The $2.5 / $7.5 and $0.4 / $1.6 figures above are Alibaba's list rates; it is currently discounting off them, not adding to them. qwen3.7-max is running at 50% off, which puts the effective rate at about $1.25 in / $3.75 out today — still several times deepseek-v4-flash, so the price ordering above doesn't change. That discount is a published limited-time offer rather than an open-ended one: Alibaba's docs give the window as 22:30 on 2026-05-26 through 23:59:59 on 2026-07-31, Beijing time, while pricing aggregators report the Model Studio promo lapsing earlier, around 2026-07-23. Check the console for the live rate before you budget, and note that when the offer ends the price rises to the printed list rate. A separate 20% discount on qwen3.7-plus has already expired — it ran only to 2026-07-02, so $0.4 / $1.6 is what you pay. DeepSeek marks deepseek-chat and deepseek-reasoner deprecated on 2026/07/24 15:59 UTC and reserves the right to adjust prices.

Which family fits your constraint

Pick DeepSeek if you run server-side inference at volume and price per token dominates, if you want frontier-scale open weights and the hardware to serve them, or if a documented training opt-out is something your compliance review looks for.

Pick Qwen if you need a current-generation open model that fits consumer hardware, if Apache 2.0's patent grant matters to your legal team, if you want open vision or speech models rather than text alone, or if you want a desktop client today.

Pick neither hosted service if the data can't leave your jurisdiction. DeepSeek stores personal data in the PRC; Qwen stores and processes it in Singapore and Mainland China. Running the weights yourself is the only version where that question doesn't arise — which is why Outlier exists, and why it's Qwen inside. What we don't do: web search, image generation, mobile, voice mode, Windows or Linux, cloud sync.