Qwen3

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Qwen3

Qwen3 is the third-generation text model family from Alibaba Cloud's Qwen team, released on April 28, 2025. It covers six dense sizes (0.6B to 32B) and two MoE variants, all trained on approximately 36 trillion tokens across 119 languages. A key design choice is a unified thinking and non-thinking mode in every model, so developers can choose between step-by-step reasoning and fast single-pass responses without switching models.

General
Release date	28 Apr 2025
Developer	Qwen / Alibaba Cloud
Type	Open-weight text LLM family
License	Apache 2.0
GitHub	QwenLM/Qwen3
Hugging Face	huggingface.co/Qwen
Documentation	qwenlm.github.io/blog/qwen3

Core Features

Thinking/non-thinking mode: every model supports both step-by-step chain-of-thought reasoning and direct response generation within a single checkpoint.
Thinking budget: developers can set a token budget for the reasoning phase, allowing inference cost to be tuned per request.
Long context: models at 4B and above support 131,072-token context windows; 0.6B and 1.7B support 32,768 tokens.
Multilingual: pretrained on 119 languages and dialects.
Apache 2.0: all weights are released for commercial use, fine-tuning, and redistribution.

Model Variants

Variant	Total Params	Active Params	Context	Best for
Qwen3-0.6B	0.6B	0.6B	32K	Edge and on-device
Qwen3-1.7B	1.7B	1.7B	32K	Lightweight inference
Qwen3-4B	4B	4B	128K	Balanced performance
Qwen3-8B	8B	8B	128K	General tasks
Qwen3-14B	14B	14B	128K	Higher accuracy
Qwen3-32B	32B	32B	128K	Strong reasoning
Qwen3-30B-A3B	30B	3B	128K	Efficient MoE
Qwen3-235B-A22B	235B	22B	128K	Flagship MoE

Benchmarks

The flagship Qwen3-235B-A22B model scores:

AIME '24: 85.7
AIME '25: 81.5
LiveCodeBench v5: 70.7
BFCL v3: 70.8

Tools and Resources

Qwen3 Blog Post: official release notes and technical details.
Hugging Face (Qwen): download model weights and GGUF files.
Technical Report: arXiv paper covering architecture, training, and evaluation.
Ollama: run Qwen3 models locally.
Qwen API Platform: access models via OpenAI-compatible API.

Ecosystem and Integrations

Available on Hugging Face Hub in both standard and GGUF formats.
Accessible via Alibaba Cloud DashScope using an OpenAI-compatible endpoint.
Supported by Ollama, LM Studio, and major inference frameworks including vLLM and llama.cpp.
All sizes available for fine-tuning using standard supervised fine-tuning and RL pipelines.

Qwen3 weights are available immediately on Hugging Face. To access via API, generate a key on the Qwen API Platform and follow the Model Studio documentation.

Edit on GitHub

Qwen Qwen3 AI technology Hackathon projects

Discover innovative solutions crafted with Qwen Qwen3 AI technology, developed by our community members during our engaging hackathons.

AI-driven real-time web intelligence

Dominify AI Search is an AI-powered competitive intelligence platform purpose-built for GTM and Finance teams that need to make fast, data-driven decisions about prospects, vendors, and competitors. By entering a domain name, users receive a structured intelligence dossier within seconds, eliminating hours of manual research across Google, LinkedIn, and company websites. The platform runs a parallel ingestion pipeline that simultaneously scrapes Google News via Bright Data's SERP API and renders dynamic pricing pages using a Playwright browser capable of bypassing Cloudflare, CAPTCHAs, and JavaScript-heavy SPAs. This raw data is fed into two independent AI agents built on Qwen 3.5 via Alem AI: a GTM Agent that tracks buying signals like hiring velocity, leadership changes, expansion announcements, and marketing intensity; and a Finance Agent that analyzes pricing trends, discounting behavior, and financial stress indicators. Crucially, a third "Reality Check" agent cross-validates these outputs, flagging anomalies—such as aggressive marketing paired with price cuts and financial stress—to advise caution before engagement. The final output is a strongly-typed UnifiedCompanyDossier validated with Zod schemas. It delivers a buying intent score (0–100), financial health classification, stability score, vendor risk heatmap, anomaly flag, executive summary, and up to ten deduplicated signals, all compressed into a single actionable recommended action. Dossiers sync automatically to HubSpot CRM, push as rich alerts to Slack or Telegram, or can be consumed via a REST API. A multi-domain endpoint processes up to 50 companies at once for bulk portfolio monitoring, while an MCP server integrates the pipeline directly into Claude Desktop. Every component degrades gracefully, switching to deterministic heuristic logic when no LLM API key is configured to maintain full functionality for demos and development without external dependencies.

AluminatiEye

AluminatiEye is a GPU Cloud Intelligence Oracle built to help AI teams make smarter infrastructure decisions in an increasingly complex GPU market. Today, AI builders face fragmented cloud providers, constantly changing GPU pricing, infrastructure shortages, and limited visibility into which provider is the best fit for a workload. Teams often spend hours comparing vendors, researching companies, monitoring pricing, and evaluating risk before deploying models. AluminatiEye creates a unified intelligence layer across the GPU ecosystem. The platform collects and analyzes data from multiple GPU cloud providers and public sources to generate actionable infrastructure insights. Key capabilities include: • Live Pricing – Tracks GPU pricing across multiple cloud vendors in real time. • Arbitrage Detection – Finds cost-saving opportunities between providers. • Market Intelligence – Aggregates news, sentiment, regulations, and competitive signals. • Risk Scores – Evaluates providers based on reliability, growth, uptime, and market health. • Cost Calculator – Estimates infrastructure spending. • Recommender – Suggests optimal GPUs and providers for training, fine-tuning, inference, and image generation workloads. • Oracle Engine – Combines all signals into a single recommendation. Built using Bright Data's web intelligence infrastructure, AluminatiEye transforms raw infrastructure data into strategic recommendations that help organizations reduce costs, mitigate risk, and make faster infrastructure decisions. Our vision is to become the intelligence layer for the GPU economy, giving founders, engineers, researchers, and AI teams a single source of truth for cloud infrastructure decisions.

Reddit Intelligence Swarm

User provides a query to our system, then we extract a predefined number of relevant Reddit posts and comments using BrightData's API, extracting the information to a PostgreSQL database information, then using three agents running locally, Planner, Researcher, Summarizer and Critic we extract relevant information from the data to provide an answer to the user that satisfies their query. The data is extracted from Brightdata's Reddit APIs for Posts and Comments, using filters that extract the data according to recency. This data is stored in PostgreSQL database and queries to extract relevant information are created by planner and researcher, the data is converted to a logical answer by summarizer and critic is used to evaluate the response.

ZippoAI Live Intelligence Agent

ZippoAI Live Intelligence Agent is a local-first AI platform that combines Bright Data's live web intelligence with persistent AI memory to deliver real-time business insights. Traditional AI assistants often rely on static knowledge and cannot accurately answer questions about recent market changes, competitor activity, regulatory updates, or emerging risks. ZippoAI solves this by combining live public web data with an intelligent memory layer. When a user submits a research query, ZippoAI first checks its local PostgreSQL cache and Qdrant semantic memory to reuse previously collected intelligence. If the information is missing or outdated, the platform automatically retrieves fresh public web data through Bright Data SERP APIs. This approach reduces costs, improves response times, and minimizes unnecessary external requests. The retrieved data is analyzed by an AI reasoning engine and transformed into structured intelligence reports containing executive summaries, key signals, recommendations, confidence indicators, and source references. Instead of manually reviewing dozens of websites, users receive concise, actionable insights that support faster decision-making. ZippoAI is designed for competitor monitoring, market research, go-to-market intelligence, compliance awareness, supplier assessment, and strategic planning. Every research result can be stored in memory, creating a continuously growing knowledge base that becomes more valuable over time. A dedicated Live Research interface allows users to investigate companies, competitors, industries, and market events using both historical knowledge and live web intelligence. This combination of memory, reasoning, and real-time data provides a unique advantage over traditional AI assistants.

Nexus-Intel — Enterprise Intelligence Platform

NexusIntel is an advanced, multi-track intelligence platform designed to deliver comprehensive, real-time insights on any target company. It leverages Bright Data’s scraping ecosystem and Claude AI to run three parallel data pipelines. Orchestrated via LangGraph, the system synthesizes vast amounts of live web data into a unified, actionable C-suite executive brief. GTM (Go-To-Market) Intelligence: Focuses on competitive positioning and buying signals. By scraping competitor pricing, analyzing live job postings (to infer strategic pivots), and monitoring SERP for vendor-switch intent, Claude generates concrete action items for marketing and sales leaders. Evaluates financial health and regulatory risk. This track monitors target pricing changes, extracts alternative data signals (like headcount velocity and news sentiment), and scrapes regulatory databases to generate a comprehensive 0-10 financial risk score. Acts as an autonomous threat hunter. It monitors threat surfaces (data breaches, CVEs, GitHub exposures), tracks compliance changes (CISA, NIST, GDPR), and conducts vendor risk assessments. Crucially, it features an AI agentic loop that autonomously investigates high-severity threats and triggers automated webhook alerts for critical vulnerabilities. Once the three parallel pipelines complete their analysis, a specialized synthesis agent correlates the findings. It produces a high-level executive summary, identifies the top three cross-departmental priorities, and maps hidden connections between the tracks (e.g., identifying that a sudden hiring surge is linked to a vulnerability in an exposed tech stack). Users can interact with the platform via a cyberpunk-themed Web UI, a robust REST API (allowing modular execution of individual tracks or full reports), and webhook integrations for real-time critical alerts. Bright Data Infrastructure: Relies heavily on the SERP API, Web Unlocker, Scraping Browser, and Web Scraper API to bypass anti-bot measures.

ConsumerIQ: Validate Demand Before You Build

ConsumerIQ is a demand validation engine for founders launching physical consumer products. The most expensive founder mistake isn't building badly. It's building something the market never asked for. Studies at CB Insight shows that roughly 43% of founders worldwide fail because their product has no market need, meaning no one actually wants to buy it. Inventory, supplier deposits, packaging, and launch ad budgets all get committed long before a single sale is proven. ConsumerIQ catches that risk before a dollar is spent on production. Founders submit a product concept, category, target market, and audience through a guided onboarding form. ConsumerIQ then maps the category to relevant marketplace and social data sources and scrapes real signals like competitor listings, reviews, complaints, pricing, SERP results, and social trends, using Bright Data's marketplace datasets, SERP API, and scraping browser across Amazon, Walmart, Etsy, and social platforms. The signals feed a hybrid AI pipeline. A local GPU stack runs Llama 3.2 3B for ReAct agent loops, fastembed MiniLM-L12 for embeddings, and Qwen 3.5 0.8B for CJK to EN translation and compliance preprocessing, with higher-order data synthesis to the dashboard powered by the Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled model served via featherless.ai. Signals persist in Postgres with pgvector and a Cognee knowledge graph for semantic memory. A Go ingest service, FastAPI admin layer, Redis queues, and Celery workers (split into inference and scraping pools) orchestrate the end-to-end pipeline behind an NGINX gateway. The output is a founder-ready dashboard across four sections (Market, Demand, Competitors, Launch) plus an interactive agent chat for follow-up questions. The final deliverable is a clear verdict: Build, Pivot, Stop, or Refine. One input. Real market data. One clear decision, built for founders who need an answer, not another dashboard.