AlibabaChat

Qwen3 30B A3B (Free)

qwen3-30b-a3b:free

Qwen3 30B A3B (Free) by Alibaba.

Context: 41.0K tokens
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Pricing

Input$0 / 1M

Output$0 / 1M

Cache Write$0 / 1M

Cache Read$0 / 1M

Prompt cache writes and reads are included at no additional cost.

Quick Start

Select an endpoint and copy a working example for this model.

Endpoint:

python

from openai import OpenAI client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.apertis.ai/v1") response = client.chat.completions.create(    model="qwen3-30b-a3b:free",    messages=[        {"role": "user", "content": "Hello!"}    ],    max_tokens=1024,    temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(#     model="qwen3-30b-a3b:free",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs

Common7 params

modelmessagesmax_tokenstemperaturetop_pstreamtools

Extended4 params

reasoning_effortstream_optionsthinkingextra_body

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

qwen3-30b-a3b:free

Decision guidance

Fit the request contract before production.

Use this model when

The workload fits the text or chat tasks shown in this model's current catalog record.
The input fits within the published 41.0K-token context record, with output and system overhead budgeted separately.

Check before production

Confirm the production client uses one of the listed request surfaces: /v1/chat/completions, /v1/responses, /v1/messages.
Estimate a representative request from the current Free price fields instead of extrapolating from a tiny prompt.
Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.

Compare with Other Models

See how this model compares to others from the same provider.

Qwen3.8 27B

Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.

Context: 262K
Input: $0.45/M
Output: $3.20/M

Qwen3.8 2.4T A95B

Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.

Context: 262K
Input: $1.80/M
Output: $5.40/M

Qwen3.8 Max

Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series and the general-availability successor to Qwen3.8 Max Preview. It is a multimodal reasoning model designed for complex tasks across reasoning, visual understanding, coding, and agentic workflows. As the production-ready top tier of the Qwen3.8 family, it is well suited for advanced problem solving, multimodal analysis, software engineering, and long-running tool-driven applications.

Context: 1M
Input: $2.00/M
Output: $6.00/M

Qwen Plus 2025-07-28 Thinking

Qwen Plus 0728 is a hybrid reasoning model built on the Qwen3 foundation, featuring a 1M-token context window and a balanced trade-off between performance, speed, and cost.

Context: 1M
Input: $0.40/M
Output: $4.00/M