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Model ID: qwen/qwen3.5-35b-a3b | Parameters: 35B (A3B MoE) | Released: 2026-02-26

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Overview

Qwen 3.5 35B (A3B) is a mixture-of-experts (MoE) large language model designed to deliver strong performance with efficient inference. It uses sparse activation, where only a subset of parameters is activated per token, enabling a better balance between capability and serving cost. Compared to smaller models such as 9B variants, it offers improved reasoning, coding, and analytical performance for more complex workloads. The model supports long-context inference up to 262K tokens and multimodal input (text and image), making it suitable for advanced assistants, backend automation, multimodal understanding, and large-scale inference systems.

Air API Playground

Try the model in the playground.

Deploy with Container

Deploy with AIR Container.

API Usage Guide

Learn how to use the API.

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Pricing

InputOutput
$0.1625 / 1M tokens$1.3 / 1M tokens

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Key Features

  • Mixture-of-Experts (MoE, A3B) architecture with sparse activation for efficient scaling
  • Stronger reasoning and coding performance compared to smaller models such as 9B variants
  • Supports long-context inference up to 262,144 tokens
  • Multimodal capability with text and image input support (max 1 image per request; sending 2+ images returns a 400 error)
  • Strong multilingual understanding and generation
  • Efficient inference through sparse expert activation
  • Compatible with high-throughput serving engines such as vLLM
  • Provided in FP8 format for efficient deployment

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Use Cases

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Complex Q&A

Handle complex multi-step reasoning and analytical queries. 
Compare transformer-based models and mixture-of-experts models
in terms of scalability and efficiency.

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Advanced Code Generation

Generate production-level code and system design explanations. 
Design a scalable distributed task queue system using Python
and explain key components.

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Deep Analysis & Summarization

Perform deeper document understanding and insight extraction.

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Visual Understanding

Analyze images and extract insights through multimodal reasoning.

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Parameters

ParameterTypeRequiredDefaultDescription
messagesarrayRequired-List of messages for chat-based generation
max_tokensintegerOptional-Maximum tokens to generate
temperaturenumberOptional1Sampling temperature (0.0-2.0)
top_pnumberOptional1Nucleus sampling threshold
frequency_penaltynumberOptional0Penalty for token frequency
presence_penaltynumberOptional0Penalty for token presence
streambooleanOptionalfalseEnable streaming responses

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Model Details

PropertyValue
Context Length262,144
Max Output Length262,144
Quantizationfp8
Input Modalitiestext, image (max 1 image per request)
Output Modalitiestext
Supported Featurestools, reasoning, streaming, vision, json_mode, logprobs
Sampling Parametersmin_p, temperature, presence_penalty, repetition_penalty, stop, top_p, top_k, frequency_penalty, seed

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Quick Start

1

Get your API key

Generate an API key from your AirCloud account.
2

Run the code

Replace YOUR_API_KEY with your actual key and choose your preferred language.
import requests

response = requests.post(
    "https://external.aieev.cloud:5007/ai/api/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen/qwen3.5-35b-a3b",
        "messages": [{"role": "user", "content": "Hello!"}],
        "temperature": 0.7
    }
)

result = response.json()
print(result["choices"][0]["message"]["content"])

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Tags

open-source conversational 35B reasoning multilingual moe high-performance