Roboflow

QwenQwenReleased Feb 2026Apache 2.0

Qwen3.5 122B A10B

Qwen3.5 122B A10B has no score on the current Vision Evals. On the earlier Vision Evals board (last updated July 9, 2026) it passed 76.1% of visual understanding tasks (#9 of 77).

It is an open-weight model from Qwen under the Apache 2.0 license, and free for commercial use.

Qwen3.5 122B A10B Interactive Demo

Model settings

Thinking level

Max output tokens

Default 65,536 · max 65,536

Sign in to adjust thinking and output length per run.

Results appear here. Add an image or pick an example to run Qwen3.5 122B A10B.

Qwen3.5 122B A10B Vision Evals

Qwen3.5 122B A10B has not yet been evaluated on the current benchmark. The results below are from the legacy version of Vision Evals, our previous benchmark. See the current Vision Evals

Visual Understanding

77 models · 67 tasks
HighestLowest
This model#9 of 7776.12% pass rate · better than 86%
Score76.12%pass rate across 67 tasks
Speed1.77savg response per task
Cost$0.0003 / task$0.260 in · $2.08 out / 1M
Tokens1.2K / task1.2K in · 7 out
Score key:≥75%40–74%<40%
CategoryPassedScore
Object Understanding13 / 14
92.9%
Defect Detection13 / 15
86.7%
Document Understanding7 / 9
77.8%
Spatial Understanding14 / 19
73.7%
Object Counting4 / 10
40%

Scores based on a single evaluation run · Methodology

View all legacy Vision Evals results →

Qwen3.5 122B A10B Overview

Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.

Qwen3.5 122B A10B Pricing

Qwen3.5 122B A10B costs $0.260 per 1M input tokens and $2.08 per 1M output tokens.

Input$0.260 / 1M tokens
Output$2.08 / 1M tokens

Pricing updated Nov 6, 2026

Deploy Qwen3.5 122B A10B with an API

Qwen3.5 122B A10B runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.

Connect your agent to Roboflow (once)

Add the Roboflow MCP server

claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp

Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.

Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).

Deploy this workflow to your Roboflow workspace to use it.

Integrate the Roboflow "Qwen3.5 122B A10B" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/qwen3-5-122b-a10b-captioning
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value } } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.

With the Roboflow MCP connected, call `workflows_get` on "qwen3-5-122b-a10b-captioning" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.

Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.

Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.
Installpip install inference-sdk

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workflow
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="YOUR_API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="your-workspace",
  workflow_id="qwen3-5-122b-a10b-captioning",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Deploy this workflow to your Roboflow workspace to use it.

// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/qwen3-5-122b-a10b-captioning', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"}
    }
  })
});

const result = await response.json();
console.log(result);

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/qwen3-5-122b-a10b-captioning' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"}
  }
}'

Qwen3.5 122B A10B License

Apache-2.0 · Permissive license

Qwen3.5 122B A10B is released under the Apache 2.0 license, so it's commercially usable out of the box: you can ship it in closed-source, commercial products, including self-hosted deployments, without paying a license fee or open-sourcing your code. The Qwen3.5 122B A10B license is permissive, so no separate commercial license is required.

Commercial use
Permitted. Because Apache-2.0 is permissive, Qwen3.5 122B A10B can ship inside paid products and internal systems with no commercial license and no revenue threshold.
Modification
Permitted. Fine-tuning, quantizing, and distilling are all allowed, and your code changes can stay closed. Files you change must be marked as changed.
Redistribution
Permitted with attribution. Ship the Apache-2.0 license text and any NOTICE file alongside the weights or derived code.

Apache-2.0 grants an express patent license that terminates if you bring a patent claim over the work, and it disclaims warranties. Validate Qwen3.5 122B A10B on your own data before you depend on it in production.

Read the full Apache 2.0 license ↗

Do I need a commercial license for Qwen3.5 122B A10B?

This is the straightforward case: a permissive license is the best technical solution and you are free to deploy Qwen3.5 122B A10B commercially without open-sourcing your own code.

Do not hesitate to reach out with questions for your commercial project. Our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

Talk to sales

This model is released under the Apache License 2.0, a permissive open-source license that allows commercial use, modification, distribution, and patent use.

Yes. Under the terms of the Apache 2.0 license, you can freely use this model for commercial purposes, including in proprietary products. You must retain the copyright notice and disclaimers when redistributing.

License information is provided as a guide and is not legal advice.

Qwen3.5 122B A10B Details

Details

Categoryopen
ModalityMultimodal
Context Window256K
Parameters122B
LicenseApache-2.0
Release DateFeb 2026

Vision Tasks

CaptioningChart Question AnsweringClassificationDocument Question AnsweringImage TaggingMulti-Label ClassificationOCRObject DetectionVision LanguageVisual Question Answering

Usage

Past 30 Days

Price vs. performance

Estimated cost per task vs. Visual Understanding score, for this model and others ranked near it. Upper-left is the sweet spot (high quality, low cost). Based on Vision Evals (legacy) results.

11 of 11 models plotted

ModelScoreMedian tokensEst. cost / taskCompare
GoogleGemini 3.5 Flash79.1%1.4K$0.0043Compare
AnthropicClaude Fable 579.1%2.9K$0.041Compare
OpenAIGPT-5.4 Mini77.6%1.9K$0.0015Compare
OpenAIGPT-5.477.6%1.7K$0.0052Compare
OpenAIGPT-5.577.6%1.7K$0.011Compare
QwenQwen3.5 122B A10B(this model)76.1%1.2K$0.0003—
OpenAIGPT-5.6 Terra76.1%1.5K$0.0033Compare
OpenAIGPT-5.6 Sol76.1%1.5K$0.0058Compare
GoogleGemini 3.1 Pro75.8%1.1K$0.0024Compare
GoogleGemini 3 Flash74.6%1.4K$0.0014Compare
OpenAIGPT-5 Mini73.1%1.8K$0.0006Compare

Frequently Asked Questions About Qwen3.5 122B A10B Vision

Yes. Qwen3.5 122B A10B accepts image input, and on Roboflow's previous vision benchmark it passed 76.1% of visual understanding tasks (#9 of 77). You can test it on your own image in the demo above.

Yes. The demo on this page runs Qwen3.5 122B A10B in the free Roboflow Playground: upload an image and see results in seconds. A free account unlocks unlimited runs.