What modalities does Qwen3 VL 235B A22B Instruct accept?

The model accepts interleaved sequences of text, images, and video frames within a single context window of up to 262.1K tokens.

How does DeepStack improve vision-language alignment?

DeepStack fuses feature maps from multiple depth levels of the Vision Transformer, shallow layers capture low-level detail while deeper layers encode abstract semantics. Combining these gives the language model richer visual grounding information than single-layer ViT representations.

What is the difference between MRoPE and standard positional encoding for video?

Enhanced interleaved MRoPE assigns distinct positional axes to the temporal (time), height, and width dimensions of video inputs, giving the model an explicit spatial-temporal coordinate system. This improves its ability to reason about where and when events occur within a video sequence.

Can this model perform GUI automation tasks?

Yes. The model is trained to parse GUI screenshots, identify interactive elements (buttons, forms, navigation), and plan multi-step actions for PC or mobile application automation.

What OCR languages and conditions are supported?

The model covers OCR in 32 languages and has been evaluated for robustness in low-light, blurred, and tilted-text conditions.

How does Qwen3 VL 235B A22B Instruct differ from Qwen3-VL-Thinking?

The Instruct variant is designed for direct instruction following and is generally faster and more cost-effective. The Thinking variant adds extended step-by-step reasoning traces optimized for complex STEM and compositional visual reasoning problems.

Is the context window of 262.1K tokens shared across text, image, and video tokens together?

Yes. The limit of 262.1K tokens applies to the combined sequence of text tokens and visual tokens (image patches, video frames encoded as tokens) in an interleaved context.

Alibaba Cloud

Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is Alibaba Cloud's multimodal vision-language model supporting interleaved text, images, and video over a native context of 262.1K tokens, with architectural improvements in spatial-temporal modeling and agentic GUI interaction.

View API reference

Input and output price: Prices from: Input $0.20, Output $0.88, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'alibaba/qwen3-vl-instruct',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out Qwen3 VL 235B A22B Instruct by Alibaba Cloud. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

Qwen3 VL 235B A22B Instruct

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

Alibaba Cloud

Legal:Terms•Privacy

131K

129K

0.5 s

42 tps

$0.40/M

$1.60/M

—

09/23/2025

Novita AI

Legal:Terms•Privacy

131K

33K

0.7 s

59 tps

$0.30/M

$1.50/M

—

09/23/2025

DeepInfra

Legal:Terms•Privacy

262K

0.6 s

17 tps

$0.20/M

$0.88/M

Read$0.11/M

—

09/23/2025

Getting started

Call Qwen3 VL 235B A22B Instruct through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-vl-instruct',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Qwen3 VL 235B A22B Instruct request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-vl-instruct',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
    temperature: 0.5,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `alibaba/qwen3-vl-instruct`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Qwen3 VL 235B A22B Instruct supports up to `262,144` output tokens.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 262K-token context window
Image	—	URL, base64, Uint8Array	—	—	Sent as image parts in messages; counts as input tokens

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK alibaba provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-vl-instruct',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['alibaba', 'novita'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Image input

Send images alongside text as message parts. Images count as input tokens.

image-input.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-vl-instruct',
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image.' },
          { type: 'image', image: 'https://example.com/photo.jpg' },
        ],
      },
    ],
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'alibaba/qwen3-vl-instruct',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by Alibaba Cloud

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

alibaba/qwen3.8-max-0902

991K

3.9 s

50 tps

$2/M

$6/M

Read$0.25/M

Write$2.50/M

—

09/01/2026

alibaba/qwen3.8-flash

991K

1.1 s

96 tps

$0.16/M

$0.47/M

Read$0.02/M

Write$0.20/M

—

08/26/2026

alibaba/qwen3.8-27b

0.6 s

133 tps

$0.10/M

$0.40/M

Read$0.01/M

Write$0.63/M

—

08/14/2026

alibaba/qwen3.8-max

2.5 s

154 tps

$2/M

$6/M

Read$0.25/M

Write$2.50/M

—

08/02/2026

alibaba/qwen3.7-flash

991K

2.5 s

140 tps

$0.03/M+2 more

$0.13/M+2 more

Read$0.006/M

Write$0.04/M

—

07/28/2026

alibaba/qwen3.7-plus

2.0 s

58 tps

$0.40/M+1 more

$1.60/M+1 more

Read$0.08/M

Write$0.50/M

—

06/02/2026

Copy link to headingAbout Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is the general-purpose variant in the Qwen3-VL model family, built on a mixture-of-experts (MoE) architecture with 235 billion total parameters and approximately 22 billion active per token. Its context window of 262.1K tokens accommodates interleaved sequences of text, images, and video frames, making it practical for reasoning across large multimodal documents without segmenting input.

Three architectural innovations distinguish Qwen3-VL from prior generations. Enhanced interleaved Multimodal Rotary Position Embedding (MRoPE) improves spatial and temporal modeling across visual inputs, giving Qwen3 VL 235B A22B Instruct a stronger sense of object positions within images and event ordering within video. DeepStack integration fuses multi-level Vision Transformer (ViT) features from shallow, middle, and deep layers to tighten alignment between visual tokens and language tokens, improving grounding precision. Text-based temporal alignment for video replaces the prior T-RoPE approach with explicit textual timestamp grounding, enabling more reliable event localization within long video sequences.

Qwen3 VL 235B A22B Instruct extends its vision capabilities to agentic scenarios: it can parse GUI screenshots, understand layout and interactive elements, and plan actions for PC or mobile automation workflows. Optical character recognition (OCR) covers 32 languages and handles challenging conditions including low light, blurred text, and tilted documents. On standard multimodal benchmarks including MMMU and visual-math evaluations (MathVista, MathVision), Qwen3 VL 235B A22B Instruct reports competitive results against other frontier vision-language models.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: For applications that process video or multi-image inputs, confirm that your selected provider's serving infrastructure supports large multimodal payloads at your target throughput before routing production traffic.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use Qwen3 VL 235B A22B Instruct

Best for

Multilingual document intelligence: Pipelines that require OCR across 32 languages under varied image quality conditions
GUI automation and screen reading: Agents that interpret application screenshots to plan and execute UI actions
Long video comprehension: Tasks that need precise event localization and temporal reasoning over extended sequences
Multi-image analysis: Comparing product photographs, reviewing multiple chart pages, or cross-referencing figures across a document
Spatial grounding: Reasoning tasks that require accurate 2D or 3D grounding of objects within images

Consider alternatives when

Extended visual reasoning traces: Consider Qwen3-VL-Thinking when STEM and compositional visual reasoning need step-by-step traces
Text-only workloads: A text-only model will provide lower cost and faster throughput when vision isn't used
Latency-critical basic tasks: Simple instruction following without complex visual analysis doesn't need this model's scale

Copy link to headingConclusion

Qwen3 VL 235B A22B Instruct is a capable general-purpose vision-language model for production workflows that mix text with images, video, and documents. Its architectural improvements in spatial-temporal modeling and GUI-reading make it broadly applicable across document processing, video analysis, and screen-based automation, while the multimodal context window of 262.1K tokens accommodates inputs that would otherwise require splitting.

Copy link to headingFrequently Asked Questions

What modalities does Qwen3 VL 235B A22B Instruct accept?
The model accepts interleaved sequences of text, images, and video frames within a single context window of up to 262.1K tokens.
How does DeepStack improve vision-language alignment?
DeepStack fuses feature maps from multiple depth levels of the Vision Transformer, shallow layers capture low-level detail while deeper layers encode abstract semantics. Combining these gives the language model richer visual grounding information than single-layer ViT representations.
What is the difference between MRoPE and standard positional encoding for video?
Enhanced interleaved MRoPE assigns distinct positional axes to the temporal (time), height, and width dimensions of video inputs, giving the model an explicit spatial-temporal coordinate system. This improves its ability to reason about where and when events occur within a video sequence.
Can this model perform GUI automation tasks?
Yes. The model is trained to parse GUI screenshots, identify interactive elements (buttons, forms, navigation), and plan multi-step actions for PC or mobile application automation.
What OCR languages and conditions are supported?
The model covers OCR in 32 languages and has been evaluated for robustness in low-light, blurred, and tilted-text conditions.
How does Qwen3 VL 235B A22B Instruct differ from Qwen3-VL-Thinking?
The Instruct variant is designed for direct instruction following and is generally faster and more cost-effective. The Thinking variant adds extended step-by-step reasoning traces optimized for complex STEM and compositional visual reasoning problems.
Is the context window of 262.1K tokens shared across text, image, and video tokens together?
Yes. The limit of 262.1K tokens applies to the combined sequence of text tokens and visual tokens (image patches, video frames encoded as tokens) in an interleaved context.

Your use is subject to Alibaba Cloud's Terms & Privacy Policies.

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Qwen3 VL 235B A22B Instruct

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Image input

Tool calling

Copy link to headingMore models by Alibaba Cloud

Copy link to headingAbout Qwen3 VL 235B A22B Instruct

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Qwen3 VL 235B A22B Instruct

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions