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Qwen3.5-122b

Qwen3.5-122B-A10B is a powerful open-weight Mixture-of-Experts (MoE) model from Alibaba's Qwen team, featuring 122 billion total parameters with only 10 billion active per token for efficient performance.

How to Get Started

pip install requestsCode language: Bash (bash)
import requests


api_url = "https://api.regolo.ai/v1/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_REGOLO_KEY"
}
data = {
  "model": "qwen3.5-122b",
  "messages": [
    {
      "role": "user",
      "content": "If a train travels 60 km/h for 2 hours and then 80 km/h for 1.5 hours, what is the total distance covered?"
    }
  ],
  "reasoning_effort": "medium"
}

response = requests.post(api_url, headers=headers, json=data)
print(response.json())
Code language: Python (python)

Output 

{
  "id": "chatcmpl-8458b75f1bedcde1",
  "created": 1772819136,
  "model": "hosted_vllm/qwen3.5-122b",
  "object": "chat.completion",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "To find the total distance covered, we calculate the distance for each part of the trip and then add them together.nn**Formula:** $\text{Distance} = \text{Speed} \times \text{Time}$nn1.  **First part of the trip:**n    *   Speed: $60\text{ km/h}$n    *   Time: $2\text{ hours}$n    *   Distance: $60 \times 2 = 120\text{ km}$nn2.  **Second part of the trip:**n    *   Speed: $80\text{ km/h}$n    *   Time: $1.5\text{ hours}$n    *   Distance: $80 \times 1.5 = 120\text{ km}$nn3.  **Total distance:**n    *   $120\text{ km} + 120\text{ km} = 240\text{ km}$nnThe total distance covered is **240 km**.",
        "role": "assistant",
        "reasoning_content": "Thinking Process:nn1.  **Analyze the Request:** The user is asking for the total distance covered by a train given two segments of travel with different speeds and durations.nn2.  **Identify the Given Information:**n    *   Segment 1: Speed ($v_1$) = 60 km/h, Time ($t_1$) = 2 hours.n    *   Segment 2: Speed ($v_2$) = 80 km/h, Time ($t_2$) = 1.5 hours.nn3.  **Identify the Formula:** The fundamental formula for distance is $Distance = Speed \times Time$.nn4.  **Calculate Distance for Segment 1:**n    *   $d_1 = v_1 \times t_1$n    *   $d_1 = 60 \text{ km/h} \times 2 \text{ h}$n    *   $d_1 = 120 \text{ km}$nn5.  **Calculate Distance for Segment 2:**n    *   $d_2 = v_2 \times t_2$n    *   $d_2 = 80 \text{ km/h} \times 1.5 \text{ h}$n    *   Calculation: $80 \times 1 = 80$, $80 \times 0.5 = 40$. $80 + 40 = 120$.n    *   $d_2 = 120 \text{ km}$nn6.  **Calculate Total Distance:**n    *   $Total Distance = d_1 + d_2$n    *   $Total Distance = 120 \text{ km} + 120 \text{ km}$n    *   $Total Distance = 240 \text{ km}$nn7.  **Final Review:** Does the answer make sense?n    *   First part: 60 km/h for 2 hours -> 120 km. Correct.n    *   Second part: 80 km/h for 1.5 hours -> 120 km. Correct.n    *   Total: 120 + 120 = 240 km. Correct.nn8.  **Construct the Output:** Present the steps clearly to the user.n    *   State the formula.n    *   Calculate the first part.n    *   Calculate the second part.n    *   Sum them up.n    *   State the final answer.cwn"
      },
      "provider_specific_fields": {
        "stop_reason": null,
        "token_ids": null
      }
    }
  ],
  "usage": {
    "completion_tokens": 788,
    "prompt_tokens": 44,
    "total_tokens": 832
  }
}Code language: JSON / JSON with Comments (json)

Applications & use cases

  • Multimodal assistants that can understand text, images, and video in a single workflow.​
  • Document understanding and media analysis for reports, visual assets, and mixed-content knowledge bases.​
  • Customer support automation with multilingual coverage across a broad set of languages.
  • Coding copilots and software engineering assistants for development and debugging tasks.​
  • Agent-style systems that use reasoning and tool-oriented workflows to complete multi-step tasks.
  • Enterprise knowledge assistants for long-context search, synthesis, and internal documentation workflows.
  • Research and analysis tasks that benefit from a native 262K-token context and extended long-context configurations.

Model details

  • Category: Chat
  • Provider: Alibaba Cloud (Qwen)
  • Release Date: February 2026
  • Total Params: 122B
  • License: Apache 2.0
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