# FacePlugin vs Qwen 4 (Alibaba Qwen4)

FacePlugin records 1,840 monthly visits with -33.6% month-over-month growth and Contact for Pricing pricing. Qwen 4 (Alibaba Qwen4) records 35,101,353 monthly visits with -6.2% growth and Freemium pricing.

| Metric | [FacePlugin](https://openaitools.app/tool/faceplugin) | [Qwen 4 (Alibaba Qwen4)](https://openaitools.app/tool/qwen-4) |
| :--- | :--- | :--- |
| Website URL | https://faceplugin.com | https://qwen.ai |
| Categories | AI Face Recognition, AI Document Extraction, AI Image Segmentation | Large Language Models (LLMs), Open Source AI Models, AI Chatbot, AI Assistant |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 1,840 | 35,101,353 |
| MoM Growth Rate | -33.6% | -6.2% |
| Primary Market | Indonesia | Russia |
| Direct Visits | 718 | 25,020,350 |
| Review Score | 4.3 | 4.7 |
| Saved Count | 132 | 1,180 |
| Supported Platforms | Website | Website, API, Open Source |

## FacePlugin

FacePlugin targets organizations that need strong identity assurance but cannot or will not send biometric data to a third-party cloud. Instead of a metered verification API, it licenses SDKs that run on your own hardware: the server SDKs deploy on Linux with Docker and are air-gapped and VPC-friendly, while mobile SDKs for iOS, Android, Flutter, and React Native run face matching and document capture on-device, even offline. Because processing is local, biometric templates never leave your environment and there are no per-call charges.

The product lineup is modular. Face Recognition handles 1:1 verification and 1:N identification. Face Liveness Detection catches spoofing with printed photos, replayed video, and masks using passive and active checks. Deepfake Detection flags AI-generated or manipulated media before it reaches your systems. ID Document Recognition performs OCR and MRZ parsing on passports, driver's licenses, and ID cards, and ID Liveness confirms a document is physically genuine. The ID Verification SDK Suite ties capture, facial comparison, and liveness into a single eKYC workflow.

FacePlugin positions accuracy as independently validated, citing NIST FRVT benchmarking and iBeta Level 2 presentation attack detection certification. It markets to banking and financial services, healthcare, government, telecom, retail, and iGaming, with named use cases such as customer onboarding and eKYC, age verification, fraud and deepfake prevention, biometric access control, remote workforce verification, and account recovery with step-up authentication. Developer resources include documentation and blog content, and pricing is arranged through an enterprise demo.

**Pricing & Plans:** FacePlugin uses an enterprise licensing model with a request-a-demo flow rather than published self-serve tiers. The core promise is a one-time SDK license with unlimited on-premises inference and no per-call fees; exact pricing depends on the SDKs and deployment paths you choose.

- **Six modular identity SDKs:** Face recognition, face liveness, deepfake detection, ID document recognition, ID liveness, and a combined eKYC suite — pick only what you need.
- **True on-premises deployment:** Server SDKs run on your own Linux hardware or private cloud via Docker, with air-gapped and VPC-friendly options and no per-call charges.
- **On-device mobile SDKs:** iOS, Android, Flutter, and React Native SDKs run matching and capture locally, including offline, so nothing is uploaded.
- **Fraud and deepfake defense:** Layered passive and active liveness plus deepfake detection help stop presentation attacks and synthetic media.
- **Independently benchmarked accuracy:** Positioned with NIST FRVT evaluation and iBeta Level 2 certified presentation attack detection.

**Strengths:** Biometric data and templates never leave your own infrastructure; Unlimited on-premises inference with no per-call fees; Broad platform coverage including offline mobile SDKs; Independently benchmarked and certified liveness capabilities

**Limitations:** No self-serve trial or published price list — requires an enterprise demo; On-premises deployment means you handle infrastructure and updates yourself

[Profile (Markdown)](https://openaitools.app/tool/faceplugin.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 FacePlugin and Qwen 4 (Alibaba Qwen4) compare in monthly traffic?

FacePlugin has 1,840 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?

FacePlugin has a month-over-month growth rate of -33.6%; Qwen 4 (Alibaba Qwen4) has -6.2%.

### How does pricing compare?

FacePlugin: Contact for Pricing. FacePlugin uses an enterprise licensing model with a request-a-demo flow rather than published self-serve tiers. The core promise is a one-time SDK license with unlimited on-premises inference and no per-call fees; exact pricing depends on the SDKs and deployment paths you choose. 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 | [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) |
| 8 | [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) |
| 9 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Specializes in memory-efficient fine-tuning for open-weight models, but requires external infrastructure to deploy full production APIs. | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 10 | [Fireworks](https://openaitools.app/tool/fireworks-ai) | 659,687 | +6% | Contact for Pricing | Delivers exceptionally high-throughput inference for open-weight models, but focuses more on general open-source deployment than Qwen-specific tools. | [.md](https://openaitools.app/tool/fireworks-ai.md) |

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

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