# The Full Stack (fullstackdeeplearning.com) vs Qwen 4 (Alibaba Qwen4)

The Full Stack (fullstackdeeplearning.com) records 46,082 monthly visits with +6.6% month-over-month growth and Free (educational content) pricing. Qwen 4 (Alibaba Qwen4) records 35,101,353 monthly visits with -6.2% growth and Freemium pricing.

| Metric | [The Full Stack (fullstackdeeplearning.com)](https://openaitools.app/tool/fullstackdeeplearning-com) | [Qwen 4 (Alibaba Qwen4)](https://openaitools.app/tool/qwen-4) |
| :--- | :--- | :--- |
| Website URL | https://fullstackdeeplearning.com | https://qwen.ai |
| Categories | AI Course, Large Language Models (LLMs), Prompt Engineering, AI Developer Tools | Large Language Models (LLMs), Open Source AI Models, AI Chatbot, AI Assistant |
| Pricing Model | Free (educational content) | Freemium |
| Monthly Visits | 46,082 | 35,101,353 |
| MoM Growth Rate | +6.6% | -6.2% |
| Primary Market | United States | Russia |
| Direct Visits | 26,064 | 25,020,350 |
| Review Score | 4.6 | 4.7 |
| Saved Count | 1 | 1,180 |
| Supported Platforms | Website | Website, API, Open Source |

## The Full Stack (fullstackdeeplearning.com)

Full Stack Deep Learning began as a course teaching engineers to take deep learning from notebook to production, and it has grown into a broader educational hub. The site hosts complete course materials — lecture pages, slides, lab notebooks, and video walkthroughs — for both its Deep Learning Course and its LLM Bootcamp, so learners can follow along at their own pace without paying for a seat.

The curriculum is deliberately full-stack rather than model-centric. The Deep Learning Course covers development infrastructure, experiment management, troubleshooting and testing, data management and annotation, deployment, continual learning, and model monitoring, plus team and project management and ethics. The LLM Bootcamp goes into prompt engineering, augmented language models, LLMOps, and user experience for language interfaces, and includes build-along project walkthroughs.

Beyond courses, the site publishes a blog, hosts a manifesto on building AI products collaboratively, and maintains reference material such as a cloud GPU pricing guide. Because the materials are released publicly and remain accessible, the site functions as a long-lived library rather than a time-boxed bootcamp, and its audience spans individual developers, teams, and educators looking for structured material.

**Pricing & Plans:** The published course material on fullstackdeeplearning.com is available free of charge. There is no conventional subscription or per-seat pricing listed on the site; the project has historically been supported by sponsors and community interest. Any future paid programs would be announced on the site.

- **Free LLM Bootcamp materials:** Lecture pages and resources covering prompt engineering, augmented language models, LLMOps, and language UI/UX.
- **Full Deep Learning Course archive:** Multi-year course content on infrastructure, data management, deployment, monitoring, and ML team organization.
- **Working with AI Agents:** Current material on collaborating with and building agent-based systems, including a perspective on multiplayer AI.
- **Cloud GPU reference:** A maintained guide to cloud GPU options that helps teams choose compute for training and inference.
- **Blog and community resources:** Articles and updates aimed at practitioners building AI products, with a newsletter to follow new releases.

**Strengths:** Course materials are open and free to follow at your own pace; Covers the full product lifecycle, not just model training; Maintained across multiple years, so it documents how practice evolved; Includes hands-on labs and build-along project walkthroughs

**Limitations:** Some flagship courses date from 2021-2023, so tooling specifics age; Not an interactive cohort programme, so there is no instructor feedback; Sparse on formal certification or structured assessments

[Profile (Markdown)](https://openaitools.app/tool/fullstackdeeplearning-com.md)

## Qwen 4 (Alibaba Qwen4)

Qwen 4 (also known as Qwen4 or Tongyi Qianwen 4) is Alibaba Cloud's fourth-generation foundation model family and multimodal AI assistant platform accessible at qwen.ai and chat.qwen.ai. Formally unveiled in training at the Hangzhou Apsara Conference on September 22, 2026 and detailed on stage at Apsara Amsterdam on October 29, 2026, Qwen 4 spearheads Alibaba's full-stack AI roadmap that scales toward 5 to 10 trillion parameters in the upcoming Qwen 4.5 and Qwen 5 generations.

The Qwen 4 series is organized into four core deployment tiers—Qwen4-Max (flagship frontier reasoning model), Qwen4-Plus (balanced enterprise API workhorse), Qwen4-Flash (low-latency, high-throughput model), and Qwen4-27B (a 27-billion-parameter open-weight model engineered to run locally on consumer PCs and single-GPU workstations)—alongside specialized variants spotted in official Qwen Code repositories such as qwen4-coder-plus and qwen4-omni-120b-think. Architecturally, Qwen 4 builds on the foundation previewed in August 2026's Qwen3.8-Flash-Next: a hybrid Gated DeltaNet + Gated Attention sparse Mixture-of-Experts (MoE) design (125B total parameters with just 6B active per token and a 51B n-gram embedding lookup layer) paired with a 1-million-token context window and Recursive Self-Improvement (RSI) post-training.

For developers, enterprises, and everyday users, the Qwen platform offers a completely free web and mobile workspace at chat.qwen.ai featuring Deep Research, Web Dev Artifacts, live search, and native image/video/audio generation (Qwen-Image 3.1 and Qwen-Audio 3.1), while open-weight checkpoints are distributed on Hugging Face, ModelScope, and Ollama alongside commercial API hosting on Alibaba Cloud Model Studio.

**Pricing & Plans:** Free multimodal web & mobile chat at chat.qwen.ai; Apache 2.0 open-weight local models (Qwen4-27B & Qwen3.8-Flash-Next architecture preview) free on Hugging Face and Ollama; commercial API on Alibaba Cloud Model Studio (~$0.15 / $0.47 per 1M tokens on Flash tiers and ~$2.00 / $6.00 per 1M tokens on Max tiers).

- **Four-Tier Qwen 4 Model Lineup (Max, Plus, Flash & 27B):** Covers every compute envelope from the multi-trillion-scale Qwen4-Max flagship and Qwen4-Plus/Flash APIs to the local PC-friendly Qwen4-27B open-weight checkpoint, plus qwen4-coder-plus and qwen4-omni-120b-think.
- **Hybrid Gated DeltaNet + Sparse MoE & N-Gram Memory:** Pioneered in the Qwen3.8-Flash-Next architecture preview, combining ultra-lean active parameter routing (6B active out of 125B) with a 51B n-gram embedding layer that trades compute for instant memorized lookup.
- **1-Million-Token Context Window Across Modalities:** Sizes the Qwen 4 generation at a full 1M-token context window across text, code, images, audio, and video for repository-wide coding and cross-document reasoning.
- **Recursive Self-Improvement (RSI) & Agentic Tool Use:** Trained via multi-cycle automated Recursive Self-Improvement across coding, math, and hardware EDA workflows—demonstrating 10,000+ autonomous tool calls in chip design and software engineering.
- **Free Qwen Chat Workspace & Open-Weight Ecosystem:** Provides free access at chat.qwen.ai with Web Dev Artifacts, Deep Research, Qwen-Image 3.1, and Qwen-Audio 3.1, plus one-command local deployment via Ollama, vLLM, and Hugging Face.

**Strengths:** Complete 4-tier ecosystem spanning cloud flagships (Qwen4-Max/Plus/Flash) and consumer-GPU open weights (Qwen4-27B); Breakthrough Gated DeltaNet + 51B n-gram memory MoE architecture delivers frontier quality at a fraction of active inference compute; 1-million-token context window and native multimodal support across text, code, vision, audio, and video; Free full-featured web assistant at chat.qwen.ai with no mandatory subscription wall for everyday research and coding

**Limitations:** Staged late-2026 rollout following Apsara Hangzhou (Sept 22) and Amsterdam (Oct 29) means flagship Qwen4-Max weights and APIs release progressively after the Qwen3.8-Flash-Next architecture preview; Largest Qwen4-Max and upcoming 5–10T parameter Qwen 4.5/5 models require cloud APIs or multi-node enterprise GPU clusters; Brand split between international Qwen (qwen.ai / Model Studio) and domestic Tongyi Qianwen can confuse first-time API adopters

[Profile (Markdown)](https://openaitools.app/tool/qwen-4.md)

## Frequently Asked Questions

### How do The Full Stack (fullstackdeeplearning.com) and Qwen 4 (Alibaba Qwen4) compare in monthly traffic?

The Full Stack (fullstackdeeplearning.com) has 46,082 monthly visits, while Qwen 4 (Alibaba Qwen4) has 35,101,353. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

The Full Stack (fullstackdeeplearning.com) has a month-over-month growth rate of +6.6%; Qwen 4 (Alibaba Qwen4) has -6.2%.

### How does pricing compare?

The Full Stack (fullstackdeeplearning.com): Free (educational content). The published course material on fullstackdeeplearning.com is available free of charge. There is no conventional subscription or per-seat pricing listed on the site; the project has historically been supported by sponsors and community interest. Any future paid programs would be announced on the site. Qwen 4 (Alibaba Qwen4): Freemium. Free multimodal web & mobile chat at chat.qwen.ai; Apache 2.0 open-weight local models (Qwen4-27B & Qwen3.8-Flash-Next architecture preview) free on Hugging Face and Ollama; commercial API on Alibaba Cloud Model Studio (~$0.15 / $0.47 per 1M tokens on Flash tiers and ~$2.00 / $6.00 per 1M tokens on Max tiers).

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | Provides state-of-the-art open-weight reasoning models and low-cost APIs, but with less emphasis on multimodal Alibaba ecosystem tools. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Provides robust developer tools and multimodal APIs, but relies on Google's closed Gemini models rather than open-weight alternatives. | [.md](https://openaitools.app/tool/google-ai-studio.md) |
| 3 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Aggregates hundreds of models behind one API gateway, but functions as a router rather than hosting native Qwen infrastructure. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 4 | [BigModel](https://openaitools.app/tool/bigmodel) | 5,700,686 | -17% | Freemium | Offers competing Chinese foundation model APIs and agent builders, but specializes in Zhipu AI's GLM architecture. | [.md](https://openaitools.app/tool/bigmodel.md) |
| 5 | [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | -13.5% | Freemium, Paid | Provides lightweight open-weight models for local deployment, but offers smaller parameter sizes compared to top-tier Qwen variants. | [.md](https://openaitools.app/tool/gemma-open-models-by-google.md) |
| 6 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Delivers ultra-fast inference speeds using custom LPU hardware, but supports a more limited selection of open models. | [.md](https://openaitools.app/tool/groq.md) |
| 7 | [DeepLearning.AI](https://openaitools.app/tool/deeplearning-ai) | 2,562,512 | -7.2% | Freemium | AI engineering courses, certificates, and the world's biggest AI newsletter | [.md](https://openaitools.app/tool/deeplearning-ai.md) |
| 8 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Offers robust serverless APIs for open-source models, but lacks native training integrations specific to Alibaba's ecosystem. | [.md](https://openaitools.app/tool/together-ai.md) |
| 9 | [Tencent Hunyuan](https://openaitools.app/tool/tencent-hunyuan-large-model) | 1,485,463 | -1.2% | Contact for Pricing | Provides comparable multimodal foundation model APIs from a major tech giant, but utilizes Tencent's proprietary Hunyuan architecture. | [.md](https://openaitools.app/tool/tencent-hunyuan-large-model.md) |
| 10 | [The Rundown AI](https://openaitools.app/tool/the-rundown-ai) | 1,018,070 | +28.6% | Free | The top AI news and how to apply it, in five minutes a day | [.md](https://openaitools.app/tool/the-rundown-ai.md) |

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Source: https://openaitools.app/compare/fullstackdeeplearning-com-vs-qwen-4

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