# Qwen 4 (Alibaba Qwen4) vs Spatial.ai

Qwen 4 (Alibaba Qwen4) records 35,101,353 monthly visits with -6.2% month-over-month growth and Freemium pricing. Spatial.ai records 7,304 monthly visits with -32.1% growth and Paid pricing.

| Metric | [Qwen 4 (Alibaba Qwen4)](https://openaitools.app/tool/qwen-4) | [Spatial.ai](https://openaitools.app/tool/spatial) |
| :--- | :--- | :--- |
| Website URL | https://qwen.ai | https://www.spatial.ai |
| Categories | Large Language Models (LLMs), Open Source AI Models, AI Chatbot, AI Assistant | AI Marketing, AI Digital Marketing, AI Advertising, AI Real Estate |
| Pricing Model | Freemium | Paid |
| Monthly Visits | 35,101,353 | 7,304 |
| MoM Growth Rate | -6.2% | -32.1% |
| Primary Market | Russia | United States |
| Direct Visits | 25,020,350 | 2,895 |
| Review Score | 4.7 | 4.2 |
| Saved Count | 1,180 | 3 |
| Supported Platforms | Website, API, Open Source | Website |

## 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)

## Spatial.ai

Spatial.ai starts from a simple premise: two consumers with identical demographics can behave completely differently, so demographics alone are a weak basis for retail marketing and site selection. Its PersonaLive system classifies every U.S. household into 17 high-level groups and 80 behavioral segments derived from four dimensions of observed behavior — demographics, social media activity, mobile movement data, and credit card transactions. The company reports that this multi-source approach improves retail behavior prediction by an average of 17% and produces campaigns that perform up to 50% better.

The workflow runs in three steps. First, marketers identify their top segments in minutes by uploading CRM contact lists to see which segments are most valuable, or by uploading a visitation report — or simply drawing a polygon — to see which segments visit a location most. Second, they position the brand strategically using consumer spend data: comparing market share against competitors by segment and tracking how much each segment spends on different brands over time. Third, they build tailored multi-channel campaigns by viewing detailed segment portraits, personalizing ad imagery and copy, and exporting audiences to Meta, TikTok, Google, Snapchat, X, programmatic platforms, and direct mail.

Alongside PersonaLive, Spatial.ai offers Proximity for segmenting neighborhoods by social media behaviors and FollowGraph for quantifying consumer social interest at a geographic level. Typical applications include site selection, digital marketing, credit card market share analysis, and retention and expansion programs. The platform is used by more than 750 retailers, e-commerce brands, and agencies, and case studies cite clients including Colliers, HIFI Agency, AAA, SeaQuest, and Goodwill. Data is permissioned, anonymized, and aggregated, so the system describes behaviorally similar groups rather than identifying individuals.

**Pricing & Plans:** Paid subscription with three published tiers on monthly or annual billing. The Analyst is $500/month (or $425/month billed annually), The Media Buyer is $800/month (or $700/month annually), and The Strategist is $1,500/month (or $1,350/month annually). Contacts and locations are billed separately at the greater of the two counts, from $0 up to $500/month at unlimited scale. Each plan includes up to 5 users.

- **PersonaLive behavioral segments:** Every U.S. household is classified into 17 high-level groups and 80 behavioral segments built from demographics, social media activity, mobile movement data, and credit card transactions.
- **Segment your own data fast:** Upload CRM contact lists or a store visitation report, or draw a polygon, to see which segments drive the most value or visit a location most — typically in minutes.
- **Market share and spend analysis:** Compare your brand against competitors by segment and track how much each segment spends on different brands over time, using insights from 100M+ credit card transactions.
- **Cross-channel audience activation:** Export top segments for targeting across Meta, TikTok, Google, Snapchat, X, programmatic platforms, and direct mail, or enrich your CRM with demographic and segment codes.
- **Segment portraits and creative guidance:** Detailed segment portraits reveal which brands customers visit, who they follow on social, the best channels to reach them, and key demographic traits to guide ad imagery and copy.

**Strengths:** Behavioral segmentation combining social, mobile, and transaction data adds context that demographics alone miss; Self-serve segmentation from CRM files or visitation data delivers answers within minutes; Audiences export to every major ad channel plus direct mail from one platform; Transparent published pricing with 90-day opt-out and multiple case studies from real brands

**Limitations:** Coverage is limited to U.S. households, so non-U.S. brands are not served; Plans start at $500/month and contacts or locations are billed separately, which is steep for small retailers; Advanced competitive spend analysis requires the top Strategist tier

[Profile (Markdown)](https://openaitools.app/tool/spatial.md)

## Frequently Asked Questions

### How do Qwen 4 (Alibaba Qwen4) and Spatial.ai compare in monthly traffic?

Qwen 4 (Alibaba Qwen4) has 35,101,353 monthly visits, while Spatial.ai has 7,304. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Qwen 4 (Alibaba Qwen4) has a month-over-month growth rate of -6.2%; Spatial.ai has -32.1%.

### How does pricing compare?

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). Spatial.ai: Paid. Paid subscription with three published tiers on monthly or annual billing. The Analyst is $500/month (or $425/month billed annually), The Media Buyer is $800/month (or $700/month annually), and The Strategist is $1,500/month (or $1,350/month annually). Contacts and locations are billed separately at the greater of the two counts, from $0 up to $500/month at unlimited scale. Each plan includes up to 5 users.

## 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/qwen-4-vs-spatial

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