Alibaba の Qwen-Omni Turbo は、自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし向けに設計されたAIモデルです。33K のコンテキストウィンドウ、毎秒 90 トークンの生成速度、知能指数 74/100（1Mトークンあたり入力$0.3・出力$1.2）を実現しています。
Technical Specifications
- Provider
- Alibaba
- Category
- flagship
- Release Date
- 2025-01
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- API Pay-as-you-go
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text, audio
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- qwen-omni-turbo-instruct
- Supported SDK & API Protocol
- Alibaba REST API (OpenAI-Compatible)
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.3/1M in) across full 33K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.03/1M tokens
- Computer Use & Browser GUI Agent
- Supported (Screen coordinate grounding, browser & desktop GUI action loops)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- API Prompt Engineering & RAG Distillation
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed Alibaba Cloud API
- System Card & Safety Framework
- Alibaba Production Safety & Alignment Verified
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Qwen-Omni Turbo Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~358 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Qwen-Omni Turbo:
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 69%
- HumanEval
- 76%
- Chatbot Arena Elo
- 1235pts
Strengths
- 33K のコンテキストウィンドウと最大 8K トークンの出力容量（MMLU-Pro 69% · Arena Elo 1235）
- 毎秒 90 トークンの高速スループットと、初回トークン到達時間（TTFT）わずか 380ms の低遅延
- 優れたAPI経済性（1Mトークンあたり入力$0.3・出力$1.2）に加え、プロンプトキャッシュや構造化JSON出力・関数呼び出しに完全対応
Limitations & Trade-offs
- クラウド専用のプロプライエタリモデルのため、モデル重みをダウンロードしてオフライン実行することはできません
- 極めて高度な専門科学の証明問題では、上位の大型推論特化モデルの方がより深い洞察を提供する場合があります
Editorial Assessment
知能指数 74/100、33K コンテキスト、毎秒 90 トークンの速度を兼ね備えた Qwen-Omni Turbo は、Alibaba のラインナップにおいて非常に実用性の高い主力モデルです。
Best for: 自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし、エンタープライズRAG基盤、スケーラブルな本番API統合に最適。
Compare Qwen-Omni Turbo
Frequently Asked Questions about Qwen-Omni Turbo
How much does the Qwen-Omni Turbo API cost per 1M tokens?+
Qwen-Omni Turbo by Alibaba is priced at $0.3 per 1M input tokens and $1.2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.03 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Qwen-Omni Turbo pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Qwen-Omni Turbo's context window?+
Qwen-Omni Turbo features a 33K tokens context window, which equals approximately 24,576 English words or ~66 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Qwen-Omni Turbo locally on my own GPU (Open Weights)?+
No, Qwen-Omni Turbo is a proprietary closed-weights model hosted by Alibaba. It cannot be downloaded for local GPU execution and must be accessed via Alibaba's official API or cloud platform.
Does Alibaba train on data sent to the Qwen-Omni Turbo API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (Alibaba Terms). Full commercial ownership of generated outputs. Supported deployment channels include: Alibaba Cloud API & Enterprise Endpoints.
How does Qwen-Omni Turbo perform on coding and reasoning benchmarks?+
Qwen-Omni Turbo achieves a composite Intelligence Index of 74/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 69% on MMLU-Pro, and 1235 pts on Chatbot Arena Elo.
What is Qwen-Omni Turbo best used for?+
Qwen-Omni Turbo is best suited for: 自然な音声合成（TTS）、リアルタイム音声対話、高精度な文字起こし、エンタープライズRAG基盤、スケーラブルな本番API統合に最適。. Assessment: 知能指数 74/100、33K コンテキスト、毎秒 90 トークンの速度を兼ね備えた Qwen-Omni Turbo は、Alibaba のラインナップにおいて非常に実用性の高い主力モデルです。
Alibaba Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Alibaba's flagship reasoning tier:
Qwen3.8 Max Preview
Qwen3.7 Plus
Qwen3.7 Max
Qwen3.6 Max Preview
Qwen3.6 Plus
Qwen3.6 Flash
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Qwen-Omni Turbo:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.