Alibaba Cloud

Alibaba's cloud computing and AI services platform.

📍 🇸🇬 Singapore7 models availableVisit Website →
7
Available Models
$0.63
Avg Input Price/M
$0.05
Cheapest Model
alibaba/qwen-turbo
$1.60
Most Expensive
alibaba/qwen-max

Features Overview

4
Vision Support
0
Advanced Reasoning
4
Caching Support
0
Computer Use

Privacy & Data Policy

Data Retention

No data retention

Location

🇸🇬 Singapore

All Alibaba Cloud Models

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Alibaba Cloud

qwen-plus

Context Window
131K tokens
Max Output
Unlimited
Input
$0.4/M tokens
Output
$1.20/M tokens

Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance. Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.

Alibaba Cloud

qwen3-coder-plus

Vision
Caching
Context Window
1.0M tokens
Max Output
66K tokens
Input
$1.00/M tokens
Output
$5.00/M tokens
Alibaba Cloud

qwen3-max

Vision
Caching
Context Window
262K tokens
Max Output
66K tokens
Input
$0.86/M tokens
Output
$3.44/M tokens

This is the best-performing model in the Qwen series. It is ideal for complex, multi-step tasks.

Alibaba Cloud

qwen-turbo

Context Window
1.0M tokens
Max Output
Unlimited
Input
$0.05/M tokens
Output
$0.2/M tokens

Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance. Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.

Alibaba Cloud

qwen-max

Context Window
33K tokens
Max Output
Unlimited
Input
$1.60/M tokens
Output
$6.40/M tokens

Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique ability to switch seamlessly between a thinking mode for complex reasoning and a non-thinking mode for efficient dialogue ensures versatile, high-quality performance. Significantly outperforming prior models like QwQ and Qwen2.5, Qwen3 delivers superior mathematics, coding, commonsense reasoning, creative writing, and interactive dialogue capabilities. The Qwen3-30B-A3B variant includes 30.5 billion parameters (3.3 billion activated), 48 layers, 128 experts (8 activated per task), and supports up to 131K token contexts with YaRN, setting a new standard among open-source models.

Alibaba Cloud

qwen3-coder-flash

Vision
Caching
Context Window
1.0M tokens
Max Output
66K tokens
Input
$0.3/M tokens
Output
$1.50/M tokens
Vision
Caching
Context Window
131K tokens
Max Output
66K tokens
Input
$0.2/M tokens
Output
$0.8/M tokens

Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and agentic tool use. Post-trained on instruction data, it demonstrates competitive performance across reasoning (AIME, ZebraLogic), coding (MultiPL-E, LiveCodeBench), and alignment (IFEval, WritingBench) benchmarks. It outperforms its non-instruct variant on subjective and open-ended tasks while retaining strong factual and coding performance.

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