Qwen vs DeepSeek: Best Open/Free AI Model in 2026?

Open-weight AI has become the pragmatic default for anyone who wants frontier-class quality without frontier prices. In 2026 two Chinese labs dominate the conversation: Alibaba's Qwen and the model family from DeepSeek. Both ship a genuinely free chat app, publish downloadable weights, and undercut Western APIs by an order of magnitude, so the question is less "which is cheaper" and more "which fits your workload."

Quick comparison

Qwen

Qwen is the broadest of the two families. The chat app at chat.qwen.ai is free with no hard usage caps and lets you switch between specialized variants — Qwen-VL for vision, Qwen-Coder for programming, and Qwen-Math for reasoning — on top of a general assistant. It reads images and PDFs, searches the web, and can drive simple agentic workflows out of the box. The August 2026 flagship, Qwen 3.8 Max, is a 2.4-trillion-parameter mixture-of-experts model (about 95B active) with a 1-million-token context window and native text-plus-vision input.

Where Qwen really pulls ahead is breadth. It supports 119+ languages, which makes it the obvious pick for translation, localization, and any non-English-first team. The Qwen-Coder line is among the strongest open-weight coding options, frequently matching top closed models on generation and debugging benchmarks, and the vision capability means you can hand it screenshots, diagrams, and scanned documents rather than plain text alone.

The honest limits are about openness at the top end. Alibaba has committed to releasing open weights for Qwen 3.8 Max and a smaller Qwen3.8-27B, but at launch those flagship weights had not yet landed on Hugging Face and no license had been named — so the fully open story currently applies to the smaller models. Everything under roughly 35B parameters ships under the permissive Apache 2.0 license (commercial use, fine-tuning, and redistribution allowed with no royalty), but if you specifically need self-hostable weights for the very largest model, you may be waiting or settling for a mid-size checkpoint. The flagship API is also the pricier of the two here.

Price: Free via the chat.qwen.ai web and mobile apps. API on Alibaba Cloud Model Studio: Qwen 3.8 Max at $2.00 in / $6.00 out per million tokens; mid-tier Qwen3.5 Plus around $0.40 / $2.40; Qwen3.5 Flash at $0.10 / $0.40; and Qwen3.7 Flash from as low as $0.03 per million input tokens. Sub-35B weights are downloadable for free under Apache 2.0.

DeepSeek

DeepSeek has built its reputation on reasoning and coding, and the V4 generation (released April 2026) doubles down. It exposes two open-weight mixture-of-experts models: deepseek-v4-pro (1.6T total / 49B active) and the lean deepseek-v4-flash (284B / 13B active), both with a 1-million-token context window. The older deepseek-chat and deepseek-reasoner IDs were retired on 24 July 2026, so V4 is the current baseline. Free access is available at chat.deepseek.com, and new API accounts get 5 million free tokens to start.

On hard benchmarks DeepSeek is arguably the strongest open-weight family right now. V4-Pro-Max posts roughly 80.6% on SWE-bench Verified, 87.5 on MMLU-Pro, and 90.1 on GPQA Diamond — numbers that rival or beat several closed frontier models on reasoning tasks. For developers building agents, code assistants, or math-heavy pipelines, that combination of quality and near-throwaway pricing is hard to argue with. Licensing is also cleaner than Qwen's: V4-Pro, V4-Flash, V3.2, V3.1, and R1 all carry the permissive MIT license on both code and weights, with checkpoints on Hugging Face (V4-Flash is ~160 GB, V4-Pro ~865 GB on disk).

The limits are real, though. V4 is text-only — there is no native vision — so image and document workloads are off the table without a separate model. Independent reviewers note a relatively high hallucination rate that can hurt agent reliability, and factual recall still trails the best closed models. Some V4 benchmark figures are vendor-reported "internal claims" pending third-party reproduction, so treat the headline scores with a little caution. Finally, the hosted API routes traffic through servers in China, which is a genuine data-sovereignty concern for regulated or sensitive workloads — self-hosting the open weights is the mitigation.

Price: Free via chat.deepseek.com, plus 5M free API tokens per new account. API: V4 Flash at $0.14 in / $0.28 out per million tokens; V4 Pro at roughly $0.435 in / $0.87 out after a permanent 75% price cut. All V4 and recent weights are downloadable for free under the MIT license.

Which should you pick?

Both are excellent, and for pure text reasoning or coding the pricing gap makes DeepSeek the value leader while Qwen wins on versatility. If you want one assistant that handles languages, images, PDFs, and code in a single free app, Qwen is the safer all-rounder. If your work is text-in, text-out reasoning and code — and you want the most permissive license — DeepSeek is tough to beat on cost per unit of quality.

• Pick Qwen if… you need multilingual coverage (119+ languages), vision and document input, a variant for every job, or a genuinely uncapped free chat app.
• Pick DeepSeek if… you want top-tier reasoning and coding at rock-bottom API prices, clean MIT-licensed weights to self-host, and don't need image understanding.

FAQ

Are Qwen and DeepSeek free? Yes. Both offer free chat apps — chat.qwen.ai and chat.deepseek.com — with no upfront cost. Their APIs are paid but very cheap, and DeepSeek gives new accounts 5 million free tokens. Downloading and self-hosting the open weights costs nothing beyond your own hardware.

Are they really open source? They are open-weight rather than fully open-source. DeepSeek's V4 and recent models ship under the permissive MIT license on Hugging Face. Qwen's smaller models (under ~35B) are Apache 2.0, while weights and licensing for the very largest 2026 flagship were still rolling out at the time of writing.

What about data and privacy? Both are run by China-based companies, and DeepSeek's hosted API routes traffic through servers in China, which raises data-sovereignty questions for sensitive workloads. The privacy-preserving option for either family is to download the open weights and run them locally with tools like vLLM or Ollama, keeping your data entirely on your own infrastructure.