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Available now on ModelsLab · Language Model

Qwen3.5 35B A3b35B Power, 3B Speed

Try Qwen3.5 35B A3bAPI Documentation

Run Qwen3.5 35B A3b

MoE Efficiency

3B Active Parameters

Activates 3B of 35B params per token for 5x faster throughput than dense models.

Multimodal Native

Vision-Language Unified

Handles text, images, reasoning with 262k context, extensible to 1M tokens.

Benchmark Leader

Top MMLU-Pro Scores

Hits 85.3% MMLU-Pro, 84.2% GPQA, strong in coding and agent tasks.

Examples

See what Qwen3.5 35B A3b can create

Copy any prompt below and try it yourself in the playground.

Code Review

“Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”

Math Proof

“Prove that the sum of the first n natural numbers is n(n+1)/2 using mathematical induction.”

JSON Parser

“Write a JavaScript function to safely parse JSON from user input and handle errors gracefully.”

Algorithm Explain

“Explain quicksort algorithm step-by-step with a small example array [5, 2, 9, 1, 5, 6].”

For Developers

A few lines of code.
Qwen3.5 35B A3b. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen3.5 35B A3b

Read the docs

Qwen3.5 35B A3b API provides access to Alibaba's 35B MoE LLM with 3B active parameters. Supports multimodal inputs and 262k context. Ideal for fast reasoning and tool use.

Achieves 60-100+ tokens/second on RTX 4090 due to MoE routing. Outpaces dense 27B models by 5x in throughput. Suited for real-time agents.

Serves as efficient alternative to dense 70B models with similar knowledge but lower compute. Matches Qwen3-VL in vision tasks. Open weights under Apache 2.0.

Native 262,144 tokens input, up to 65k output. Extensible to 1M with config. Stable for long agent workflows.

Scores 85.3% MMLU-Pro, 84.2% GPQA Diamond, 69.2% SWE-bench. Strong in coding, math, multilingual tasks across 201 languages.

Uses Gated Delta Networks with 256 MoE experts, 40 layers, GQA attention. Trained on 36T tokens. Multimodal from early fusion.

Ready to create?

Start generating with Qwen3.5 35B A3b on ModelsLab.

Try Qwen3.5 35B A3bAPI Documentation