Qwen3-Coder

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Qwen3-Coder

Qwen3-Coder is Alibaba Cloud's dedicated coding model, released on July 22, 2025. The flagship variant, Qwen3-Coder-480B-A35B-Instruct, is a mixture-of-experts model trained on 7.5 trillion tokens across 358 programming languages, with 70% of the training data being code. It supports 256K tokens of native context, extensible to 1M tokens with extrapolation, making it suited to repository-scale tasks and multi-file agentic workflows.

General
Release date	22 Jul 2025
Developer	Qwen / Alibaba Cloud
Type	Open-weight coding LLM (MoE)
License	Apache 2.0
GitHub	QwenLM/Qwen3-Coder
Hugging Face	Qwen3-Coder-480B-A35B-Instruct
Documentation	qwenlm.github.io/blog/qwen3-coder

Core Features

480B/35B MoE architecture: 480B total parameters with 35B active per token, using 160 experts with 8 activated per inference step.
256K native context: natively processes 256,000 tokens, with extrapolation support up to 1,000,000 tokens.
358 programming languages: trained on a broad code corpus covering mainstream and niche languages.
Agent RL post-training: long-horizon reinforcement learning trains the model to solve real-world tasks through multi-turn tool interactions.
Apache 2.0: weights are available for commercial use and fine-tuning.

Benchmarks

Benchmark	Score
SWE-Bench Verified	69.6%

SWE-Bench Verified scores are state-of-the-art among open models at release, comparable to Claude Sonnet 4.

Tools and Resources

Qwen3-Coder Blog Post: release announcement with benchmark details.
GitHub (QwenLM/Qwen3-Coder): model code and usage examples.
Hugging Face: model weights.
Qwen Code: open-source terminal agent built on Qwen3-Coder.
Together AI: hosted API access.

Ecosystem and Integrations

Paired with Qwen Code, an open-source terminal coding agent with GitHub Actions support.
Accessible via the Alibaba Cloud DashScope API using an OpenAI-compatible endpoint.
Available on Together AI, LM Studio, and Ollama for local and cloud inference.

To use Qwen3-Coder via API, get an API key on the Qwen API Platform. For local agentic coding, see the Qwen Code terminal agent.

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Qwen Qwen3-Coder AI technology Hackathon projects

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

AI Virtual Puppet Theater

AI Virtual Puppet Theater is an immersive AI powered browser experience that transforms users into live digital puppeteers through real time gesture control, facial tracking, and cinematic storytelling. The project combines computer vision, realtime animation, and interactive stage design to create a magical virtual theater where users control expressive 3D characters using natural human movements. Using webcam based hand tracking and facial detection, players can perform combat, emotional reactions, celebration animations, and cinematic interactions. The system tracks gestures, blinking, eye movement, facial expressions, and head motion to make puppet characters feel emotionally alive and deeply connected to the user. Characters react dynamically with expressive eyes, animated emotions, smooth movement interpolation, and theatrical body language. The platform includes a complete cinematic story mode featuring dramatic sequences between a King, Lion, Hero, and mysterious dark creature, brought to life through curtain transitions, realtime lighting, particle systems, and synchronized animation, all running directly inside the browser. The technical stack includes Next.js, TypeScript, Tailwind CSS, React Three Fiber, Three.js, Zustand, MediaPipe, TensorFlow.js, face-api.js, and Framer Motion, with strong focus on low latency interaction and immersive presentation quality.

klarixa-tricortex-amd-hackathon

.### Technical Architecture & Core Overview Tricortex is an enterprise-grade, infrastructure-agnostic AI orchestration core engineered to execute complex multi-model reasoning pipelines. Built using the pydantic-ai framework, the system enforces a strict, non-blocking asynchronous lifecycle over autonomous agents. Rather than deploying volatile, free-form execution loops, Tricortex establishes structural operational boundaries through narrow API integration gates, explicit dynamic schema enforcement, and robust human-in-the-loop validation milestones. ### Cross-Platform Hardware Validation To demonstrate absolute operational resilience, the entire orchestration layer has been cross-validated and stress-tested under high-performance AMD compute cluster configurations. This cross-hardware implementation ensures that the system's token routing, latency management loops, and memory distribution handling remain highly stable across varied cloud infrastructures—such as Vultr nodes—preventing critical Out-Of-Memory (OOM) faults during heavy multi-model execution. ### Multi-Model Brain Integration Tricortex decouples the core reasoning layer from a single provider by leveraging a highly adaptive, multi-model backend topography: 1. Google DeepMind Ecosystem: Integrates advanced Gemini models via Google AI Studio to anchor real-time, context-aware routing decisions. 2. Alibaba Qwen Specialized Intelligence: Dynamically injects specialized model intelligence optimized for precise vertical logic across complex domains like finance, law, and medicine. ### Key Architectural Pillars * Asynchronous Emulation: Implements secure, in-memory execution tracking to simulate dense tensor processing latencies natively, ensuring total code reliability. * Model Context Protocol (MCP) Integration: Bridges the gap between remote LLM environments and local system runtime operations, allowing secure context pathway mapping without exposing raw database states.

AuditForge

Enterprise AI deployments are moving fast, but the systems those agents touch — codebases, APIs, databases — are still audited manually, slowly, and inconsistently. AuditForge changes that. AuditForge is a multi-agent compliance audit platform built on Google Gemini. Security teams upload their artifacts — a codebase, an OpenAPI spec, a database schema, a cloud config — and AuditForge dispatches specialized Gemini agents to analyze them against OWASP API Top 10, HIPAA Technical Safeguards, and SOC2 Common Criteria. Gemini's long-context window is the core advantage: rather than scanning files in isolation, the analysis agent reads an entire codebase at once, catching cross-file vulnerabilities that line-by-line tools miss entirely. Every finding is standardized — severity, evidence with the exact file and line, AI-generated remediation with a code example, and a direct mapping to the compliance clause it violates. When the audit is ready to hand off, AuditForge generates a cryptographically signed PDF report that maps every issue to its regulatory reference — the kind of document a CISO or external auditor can act on without a translator. Adding support for new system types — Terraform, Kubernetes, CI/CD pipelines — requires only a new connector module; the policy engine and report layer need no changes. Every action is recorded in a tamper-evident, cryptographically chained audit trail, making the audit of the audit verifiable too. The frontend runs natively on desktop and in the browser from a single Kotlin Multiplatform codebase. Server-Sent Events stream findings in real time as the scan runs. AuditForge makes compliance auditing something engineers can run themselves, security teams can trust, and regulators can read.

Repo-Insight : Graph-RAG Knowledge for IBM Bob

Repo-Insight parses any Python repository into a FalkorDB property graph and exposes 22 MCP tools that IBM Bob calls directly during bug-fixing tasks. When a test fails, Bob uses our deterministic stack-trace localizer to pinpoint the exact function responsible — no semantic guessing, no false positives — then narrows the blast radius from dozens of suspects to only the functions that actually executed during the failure, eliminating 83% of irrelevant context. Instead of reading full source files, Bob queries compact function fingerprints that describe each function in ~30 tokens versus ~500 for raw source, cutting total token consumption by 15× on real bugs in production codebases like Django. The result is an agent that navigates 500,000-line repositories with the precision of a senior engineer who already knows the codebase — finding the right file, reading only what matters, and fixing the bug in a fraction of the cost.

Code Janitor

Code Janitor is an AI-powered code review and refactoring platform that helps developers analyze, understand, and improve their codebases from a single unified interface. By combining large language models with traditional static analysis tools such as ESLint, it automatically detects bugs, security vulnerabilities, performance bottlenecks, and code smells, while generating optimized refactored code, inline documentation, and clear explanations for every recommendation. One of its standout features, Graphify, converts repositories into interactive dependency graphs that visually map relationships between files, modules, and components, making it easier to understand complex architectures and onboard new contributors. For frontend projects, Code Janitor also provides live preview capabilities so developers can instantly see the impact of their changes. Designed to support multiple AI providers, including IBM watsonx.ai foundation models such as Granite, the platform offers both flexibility and cost efficiency for code analysis tasks. Code Janitor was developed with assistance from IBM BOB, IBM’s AI-powered development assistant, enabling rapid prototyping and a robust, extensible architecture. Whether used by students, open-source contributors, or professional engineering teams, Code Janitor streamlines debugging, documentation, and refactoring, transforming software maintenance into a faster, smarter, and more interactive experience.