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Qwen3-MT

Qwen3-MT is a machine translation model developed by Alibaba Cloud's Qwen team, released on July 25, 2025. It is fine-tuned from Qwen3 with a lightweight Mixture-of-Experts backbone and trained on trillions of multilingual tokens spanning formal, technical, and conversational text. The model covers 92 major languages and prominent dialects, reaching over 95% of the global population.

General
Release date25 Jul 2025
DeveloperQwen / Alibaba Cloud
TypeMachine translation model (MoE fine-tune)
LicenseCommercial API
DocumentationAlibaba Cloud Model Studio
APIDashScope via Qwen API Platform

Core Features

  • 92 languages: covers major world languages and prominent dialects, reaching 95% of the global population.
  • Terminology control: allows custom terminology dictionaries to keep brand names, technical terms, and product names consistent.
  • Domain prompting: a domain hint lets the model adapt output style for legal, medical, technical, or conversational text.
  • Translation memory: integrates past translation pairs so repeated segments stay consistent across large documents.
  • Competitive pricing: priced at $0.5 per million tokens, significantly lower than dense large models for translation workloads.

Performance

Qwen3-MT outperforms comparably-sized models on translation benchmarks, including GPT-4.1-mini and Gemini-2.5-Flash, while remaining competitive with larger models like GPT-4.1 and Gemini-2.5-Pro on translation quality metrics.

Tools and Resources

Ecosystem and Integrations

  • Served through Alibaba Cloud DashScope, accessible with the OpenAI-compatible endpoint or the native DashScope SDK.
  • Supports batch translation for high-volume document workflows.
  • Term dictionaries and translation memory integrate via API request parameters, requiring no custom fine-tuning.

Get started by generating an API key on the Qwen API Platform and following the Model Studio translation guide.

Edit on GitHub

Qwen Qwen3-MT AI technology Hackathon projects

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

ROCKET — AI Performance Researcher for AMD

ROCKET — AI Performance Researcher for AMD

ROCKET is a multi-agent system (Profiler → Planner → Implementer → Validator) that takes any PyTorch model, profiles it on AMD MI300X, hypothesizes which optimizations will help, applies them from a bounded toolbox of five transformations (dtype_cast, torch_compile, sdpa_attention, input_padding, kv_cache_config), validates output correctness, and measures the speedup — completely on its own. On a real AMD Instinct MI300X (ROCm 7) running Qwen2.5-7B-Instruct at batch=8, prompt 256 + new 512, ROCKET achieved a measured 2.93× throughput speedup: 62.59 → 183.47 tokens per second. The agent tried 5 tools and kept 1 (bf16 cast); correctly reverted the four that didn't beat the validator's 2% keep threshold — exactly the judgment a senior performance engineer would apply. How this is different from adjacent submissions: - ROCmPort AI translates CUDA → ROCm code. ROCKET makes the model FAST. - ReplayLab records GPU experiments. ROCKET acts on them. - Sakana's AI Scientist optimizes model accuracy. ROCKET optimizes throughput on AMD silicon; the question every AMD developer asks first that no one had automated.

ROCm-Sentinel: Performance & Security Copilot

ROCm-Sentinel: Performance & Security Copilot

ROCm-Sentinel is a comprehensive solution designed to eliminate friction in the transition to the AMD ecosystem. Unlike conventional code translators, our project utilizes an agentic graph (LangGraph) to perform a deep analysis of the original software infrastructure. The system identifies digital signatures and language patterns—such as CUDA-specific dependencies—to evaluate migration viability before any code is modified. The core of the project features a specialized "Performance & Security" agent that does not just patch incompatibilities; it suggests proactive improvements to leverage AMD hardware fully. Furthermore, we include an automated audit loop: the system executes the code in Docker containers within a ROCm environment, analyzes error logs, and iteratively corrects the code to ensure total fidelity with the original. With ROCm-Sentinel, migration evolves from a technical risk into a strategic optimization.

AI Resume Intelligence System

AI Resume Intelligence System

AI Resume Intelligence System is a comprehensive end-to-end resume processing and job matching platform powered by advanced AI techniques. The system leverages vector embeddings, semantic search, and intelligent matching algorithms to analyze resumes and find the most relevant job opportunities. Key features include automated resume parsing using APILayer API with local fallback, intelligent job categorization using AMD-backed Fireworks LLMs, ATS scoring for candidate evaluation, and smart job recommendations based on semantic similarity. The system integrates multiple AI services including Fireworks API (minimax-m2p7, deepseek-v3p2 models), ChromaDB for vector storage, and real-time job search via SerpAPI. Built with Flask web framework, the application provides a user-friendly interface for uploading resumes and receiving personalized job matches. The system demonstrates production-ready deployment on Railway platform with robust error handling, platform-specific compatibility, and cost-efficient AI processing using AMD GPU infrastructure through Fireworks API. This project showcases practical AI application in HR tech, combining vector embeddings with semantic search to solve real-world recruitment challenges while maintaining scalability and cost-effectiveness.

AndesOps AI: Multi-Agent Tender Intelligence

AndesOps AI: Multi-Agent Tender Intelligence

Overview Public procurement in Chile (Mercado Público) involves thousands of documents with complex legal and technical requirements. For most companies, analyzing these opportunities is slow, risky, and expensive. AndesOps AI solves this by deploying a "Virtual Board of Experts"—an agentic workflow that processes tenders in seconds. The Solution: The "Expert Round Table" Unlike simple RAG systems, AndesOps AI uses an Agentic Orchestration Layer where specialized AI agents collaborate: ⚖️ Legal & Compliance Agent: Scans administrative bases for critical deadlines and compliance gaps. 🏗️ Technical Architect Agent: Evaluates the feasibility of requirements against the company's stack and experience. 📊 Strategy & ROI Agent: Calculates potential profitability and competitive risks. 🧠 Orchestrator: Consolidates findings into a Strategic Fit Score (0-100) and a professional report. Technical Stack & AMD Integration Backend: FastAPI (Python) with asynchronous parallel agent execution. Frontend: Next.js 14 + Tailwind CSS (Enterprise-grade UI). AI Engine: Multi-agent system designed to run high-performance inference. AMD Advantage: Optimized for AMD Instinct™ GPUs using ROCm. By leveraging AMD's massive memory bandwidth, AndesOps AI can process hundreds of pages of bid documents simultaneously, providing near-instant feedback for time-sensitive public bids. Business Value 90% Reduction in manual analysis time. Risk Mitigation: Early detection of "hidden" legal requirements that could lead to disqualification. Increased Win Rate: AI-driven proposal drafting that highlights the company's competitive advantages. 4. Technology & Category Tags (Etiquetas) AI Agents Agentic Workflows ROCm AMD Developer Cloud FastAPI Next.js GovTech Llama-3 5. Submission Checklist (Criterios de Evaluación) Application of Tech: Mention that the project is built to leverage ROCm for high-throughput document processing. Originality: Emphasize the "Expert Panel" approach over a single-agentchatbot

Qwen3-MT