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2c:I[4704,[],"IconMark"]
29:T117c,# Qwen3 VL Flash â€” Vision-Language Model API

## What is Qwen3 VL Flash?

Qwen3 VL Flash is a fast, cost-efficient vision-language model (VLM) developed by Alibaba Cloud. It accepts text, image, and video inputs together, reasoning across all modalities in a single API call. Built on the Qwen3-VL architecture â€” which includes Interleaved-MRoPE, DeepStack ViT fusion, and text-timestamp alignment â€” it brings frontier multimodal understanding to high-volume production workloads without the premium pricing of larger models.

With a 262,144-token context window, Qwen3 VL Flash can process entire multi-page documents, long-form conversations with image history, or extended video sequences in a single request. The model uniquely offers two inference modes: thinking mode, which activates a chain-of-thought reasoning pipeline for complex tasks, and non-thinking mode for rapid, direct-answer responses.

## Key Features

- **262K token context window** â€” process long documents, multi-image threads, and video sequences without chunking
- **Dual inference modes** â€” thinking mode for deep reasoning (up to 81,920 CoT tokens); non-thinking mode for high-throughput batch tasks
- **Multimodal input** â€” accepts text, images, and video frames in a single prompt
- **Advanced OCR** â€” supports 32 languages including rare scripts; handles low-light, rotated, and blurred documents
- **Visual agent support** â€” recognizes GUI elements, understands layout, and can drive multi-step automation tasks
- **Ultra-low cost** â€” tiered input pricing starting at $0.05 per 1M tokens, ideal for large-scale deployments

## Best Use Cases

**Document Intelligence**: Extract structured data from invoices, receipts, forms, and multi-page PDFs with multilingual OCR support.

**E-Commerce Automation**: Tag product images, extract attributes, and generate descriptions from catalog photos at scale.

**UI Parsing and Automated Testing**: Convert screenshots to code, audit interface layouts, or build visual QA pipelines over app screenshots.

**Visual Question Answering**: Answer questions over charts, tables, diagrams, and scientific figures for analytics and research tooling.

**Security and Inspection**: Detect anomalies in facility images, perform shelf audits, or automate visual compliance checks.

**Video Understanding**: Analyze instructional, surveillance, or media footage with precise temporal grounding.

## Prompt Tips and Output Quality

For best results with Qwen3 VL Flash, be specific about the output format you need. For OCR tasks, instruct the model to return structured JSON or markdown tables. For reasoning tasks, prefix your prompt with Chain-of-thought: or Step-by-step: to activate the thinking pipeline automatically. When sending images, use high-resolution inputs (at least 800px on the short edge) for OCR accuracy. For batch processing, non-thinking mode is recommended â€” it delivers faster responses and lower latency at equivalent cost.

Use system instructions to set the response language if you need multilingual output. For agent workflows, include a description of the UI layout or available tools in your system prompt.

## FAQs

**Does Qwen3 VL Flash support video inputs?**
Yes â€” it can process video frames within its 262K token context using text-timestamp alignment for precise temporal reasoning.

**What is the difference between thinking and non-thinking mode?**
Thinking mode enables chain-of-thought reasoning for complex, multi-step tasks. Non-thinking mode returns direct answers faster, making it ideal for high-volume batch workloads.

**How many images can I send per request?**
Each image consumes up to 16,384 tokens. With a 262K context window, you can send many images per call, subject to total token limits.

**What languages does the OCR support?**
The model supports 32 languages for OCR, including rare and ancient characters, with robustness to blur, tilt, and low-light conditions.

**How does Qwen3 VL Flash compare to GPT-4o Vision?**
Qwen3 VL Flash offers significantly lower per-token cost while maintaining competitive accuracy on document understanding and visual QA benchmarks. GPT-4o Vision may have an edge in open-ended creative tasks.

**Can I fine-tune this model?**
The Flash variant is a cloud-hosted model available via API. Fine-tuning is supported through Alibaba Cloud Model Studio, not directly via the Segmind API endpoint.2a:T878,## How to Use Qwen3 VL Flash

Qwen3 VL Flash is a vision-language model that accepts a text prompt, an optional image, and returns a detailed response. Here is how to get the best results across different use cases.

### Text-Only Tasks

For pure text tasks â€” summarization, analysis, writing â€” simply fill in the **Prompt** field and leave **Image** blank. Use clear, direct instructions and specify your desired output format:

```
Summarize the following legal clause in plain English and highlight any risks: [paste text]
```

### Image Analysis and Visual QA

Attach an image and write a focused prompt describing what you want to extract or understand:

- OCR: "Extract all text from this invoice and return it as a JSON object with keys: vendor, date, total, line_items."
- Chart reading: "What is the highest revenue quarter shown in this bar chart? Return the value and year."
- Product tagging: "List the product attributes visible in this image: color, material, style, and any visible labels."

### Reasoning and Complex Tasks

For multi-step or analytical tasks, ask the model to reason step by step:

```
Step by step, analyze this architectural floor plan and identify potential accessibility issues for wheelchair users.
```

This naturally triggers the model's thinking mode for deeper analysis.

### Document Processing at Scale

For batch OCR or document extraction pipelines, use concise prompts and request structured output formats (JSON, CSV, markdown tables). Non-thinking mode is implicitly used when your prompt is direct and does not require reasoning chains.

### Video and Multi-Image Analysis

When sending video frames or multiple images, provide a guiding question and reference frame numbers or timestamps in your prompt to help the model ground its answers temporally.

### Tips for Best Output Quality

- Use high-resolution images for OCR tasks (800px minimum on the short edge)
- Specify output format explicitly (JSON, list, table, markdown table)
- For multilingual documents, mention the source language in your prompt
- For GUI automation tasks, describe the target action clearly1e:["$","$L28",null,{"route":"qwen3-vl-flash","modelDetails":{"title":"Qwen 3 VL Flash","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-5506.webp","type":"textToText","model_type":"SO","is_depreciated":false,"meta_description":"Fast, affordable vision-language API with 262K context for OCR, visual QA, and multimodal document analysis.","seo_description":"$29","guide":"$2a","pricing_guide":null,"artifacts":[],"default_image_output":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-example-output.txt","default_response":null,"parameters":{"prompt":{"isRequired":true,"affectsPricing":false,"type":"str","description":"Text prompt or question for model analysis and reasoning.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":0,"component":"textarea","displayValue":"Analyze this invoice image. Extract the key details: vendor name, date, line items with prices, and total amount. Return the information in a structured format.","label":"prompt","default":"Analyze this invoice image. Extract the key details: vendor name, date, line items with prices, and total amount. Return the information in a structured format."},"image":{"isRequired":false,"affectsPricing":false,"type":"image","description":"Optional image for model to analyze alongside text prompt.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":1,"component":"image","label":"Image","default":"https://segmind-resources.s3.amazonaws.com/input/qwen3-vl-flash-test-input.jpg","displayValue":"https://segmind-resources.s3.amazonaws.com/input/qwen3-vl-flash-test-input.jpg"},"response_format":{"type":"object","format":"json","component":"textarea","label":"Response format","description":"Structured output. {\"type\": \"json_object\"} returns any valid JSON; {\"type\": \"json_schema\", \"json_schema\": {\"name\", \"strict\", \"schema\"}} enforces the given JSON Schema. Omit for plain text.","displayValue":{"type":"json_schema","json_schema":{"name":"answer","strict":true,"schema":{"type":"object","properties":{"answer":{"type":"string"}},"required":["answer"],"additionalProperties":false}}},"isRequired":false,"isAdvanced":true,"hidden":false,"affectsPricing":false,"isEnum":false,"displayOrder":2}},"endpoints":{"main":"/v1/qwen3-vl-flash"},"api_notes":[],"model_outputs":null,"llm_capabilities":null,"license":"","github_link":null,"inference_provider":"DASHSCOPE","created_ts":"2026-03-25 09:44:27.753308+00:00","slug":"qwen3-vl-flash","name":"Qwen 3 VL Flash","description":"Fast, affordable vision-language model with 262K context OCR.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-5506.webp","is_new":false,"coming_soon":false,"popularity_score":83,"category_percentile":98,"average_cost":0.00008282637632338787,"latency_seconds":1.90857,"created_at":"2026-03-25 09:44:27.753308+00:00","parameters_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"prompt":{"type":"string","title":"Prompt","description":"Text prompt or question for model analysis and reasoning.","default":"Analyze this invoice image. Extract the key details: vendor name, date, line items with prices, and total amount. Return the information in a structured format.","x-ui-metadata":{"order":0,"advanced":false,"hidden":false}},"image":{"type":"string","format":"uri","x-file-metadata":{"allowed-types":["image/*"]},"title":"Image","description":"Optional image for model to analyze alongside text prompt.","x-ui-metadata":{"order":1,"advanced":false,"hidden":false}},"response_format":{"type":"object","format":"json","title":"Response format","description":"Structured output. {\"type\": \"json_object\"} returns any valid JSON; {\"type\": \"json_schema\", \"json_schema\": {\"name\", \"strict\", \"schema\"}} enforces the given JSON Schema. Omit for plain text.","examples":[{"type":"json_schema","json_schema":{"name":"answer","strict":true,"schema":{"type":"object","properties":{"answer":{"type":"string"}},"required":["answer"],"additionalProperties":false}}}],"x-ui-metadata":{"order":2,"advanced":true,"hidden":false}}},"required":["prompt"],"examples":[{"prompt":"Analyze this invoice image. Extract the key details: vendor name, date, line items with prices, and total amount. Return the information in a structured format.","image":"https://segmind-resources.s3.amazonaws.com/input/qwen3-vl-flash-test-input.jpg"}]}},"metrics":{"total_requests":null,"latency":1.90857},"children":"$L2b"}]
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2d:T6fb,{"@context":"https://schema.org","@type":"FAQPage","@id":"https://www.segmind.com/models/qwen3-vl-flash#faq","mainEntity":[{"@type":"Question","name":"Does Qwen3 VL Flash support video inputs?","acceptedAnswer":{"@type":"Answer","text":"Yes â€” it can process video frames within its 262K token context using text-timestamp alignment for precise temporal reasoning."}},{"@type":"Question","name":"What is the difference between thinking and non-thinking mode?","acceptedAnswer":{"@type":"Answer","text":"Thinking mode enables chain-of-thought reasoning for complex, multi-step tasks. Non-thinking mode returns direct answers faster, making it ideal for high-volume batch workloads."}},{"@type":"Question","name":"How many images can I send per request?","acceptedAnswer":{"@type":"Answer","text":"Each image consumes up to 16,384 tokens. With a 262K context window, you can send many images per call, subject to total token limits."}},{"@type":"Question","name":"What languages does the OCR support?","acceptedAnswer":{"@type":"Answer","text":"The model supports 32 languages for OCR, including rare and ancient characters, with robustness to blur, tilt, and low-light conditions."}},{"@type":"Question","name":"How does Qwen3 VL Flash compare to GPT-4o Vision?","acceptedAnswer":{"@type":"Answer","text":"Qwen3 VL Flash offers significantly lower per-token cost while maintaining competitive accuracy on document understanding and visual QA benchmarks. GPT-4o Vision may have an edge in open-ended creative tasks."}},{"@type":"Question","name":"Can I fine-tune this model?","acceptedAnswer":{"@type":"Answer","text":"The Flash variant is a cloud-hosted model available via API. Fine-tuning is supported through Alibaba Cloud Model Studio, not directly via the Segmind API endpoint."}}]}2f:T117c,# Qwen3 VL Flash â€” Vision-Language Model API

## What is Qwen3 VL Flash?

Qwen3 VL Flash is a fast, cost-efficient vision-language model (VLM) developed by Alibaba Cloud. It accepts text, image, and video inputs together, reasoning across all modalities in a single API call. Built on the Qwen3-VL architecture â€” which includes Interleaved-MRoPE, DeepStack ViT fusion, and text-timestamp alignment â€” it brings frontier multimodal understanding to high-volume production workloads without the premium pricing of larger models.

With a 262,144-token context window, Qwen3 VL Flash can process entire multi-page documents, long-form conversations with image history, or extended video sequences in a single request. The model uniquely offers two inference modes: thinking mode, which activates a chain-of-thought reasoning pipeline for complex tasks, and non-thinking mode for rapid, direct-answer responses.

## Key Features

- **262K token context window** â€” process long documents, multi-image threads, and video sequences without chunking
- **Dual inference modes** â€” thinking mode for deep reasoning (up to 81,920 CoT tokens); non-thinking mode for high-throughput batch tasks
- **Multimodal input** â€” accepts text, images, and video frames in a single prompt
- **Advanced OCR** â€” supports 32 languages including rare scripts; handles low-light, rotated, and blurred documents
- **Visual agent support** â€” recognizes GUI elements, understands layout, and can drive multi-step automation tasks
- **Ultra-low cost** â€” tiered input pricing starting at $0.05 per 1M tokens, ideal for large-scale deployments

## Best Use Cases

**Document Intelligence**: Extract structured data from invoices, receipts, forms, and multi-page PDFs with multilingual OCR support.

**E-Commerce Automation**: Tag product images, extract attributes, and generate descriptions from catalog photos at scale.

**UI Parsing and Automated Testing**: Convert screenshots to code, audit interface layouts, or build visual QA pipelines over app screenshots.

**Visual Question Answering**: Answer questions over charts, tables, diagrams, and scientific figures for analytics and research tooling.

**Security and Inspection**: Detect anomalies in facility images, perform shelf audits, or automate visual compliance checks.

**Video Understanding**: Analyze instructional, surveillance, or media footage with precise temporal grounding.

## Prompt Tips and Output Quality

For best results with Qwen3 VL Flash, be specific about the output format you need. For OCR tasks, instruct the model to return structured JSON or markdown tables. For reasoning tasks, prefix your prompt with Chain-of-thought: or Step-by-step: to activate the thinking pipeline automatically. When sending images, use high-resolution inputs (at least 800px on the short edge) for OCR accuracy. For batch processing, non-thinking mode is recommended â€” it delivers faster responses and lower latency at equivalent cost.

Use system instructions to set the response language if you need multilingual output. For agent workflows, include a description of the UI layout or available tools in your system prompt.

## FAQs

**Does Qwen3 VL Flash support video inputs?**
Yes â€” it can process video frames within its 262K token context using text-timestamp alignment for precise temporal reasoning.

**What is the difference between thinking and non-thinking mode?**
Thinking mode enables chain-of-thought reasoning for complex, multi-step tasks. Non-thinking mode returns direct answers faster, making it ideal for high-volume batch workloads.

**How many images can I send per request?**
Each image consumes up to 16,384 tokens. With a 262K context window, you can send many images per call, subject to total token limits.

**What languages does the OCR support?**
The model supports 32 languages for OCR, including rare and ancient characters, with robustness to blur, tilt, and low-light conditions.

**How does Qwen3 VL Flash compare to GPT-4o Vision?**
Qwen3 VL Flash offers significantly lower per-token cost while maintaining competitive accuracy on document understanding and visual QA benchmarks. GPT-4o Vision may have an edge in open-ended creative tasks.

**Can I fine-tune this model?**
The Flash variant is a cloud-hosted model available via API. Fine-tuning is supported through Alibaba Cloud Model Studio, not directly via the Segmind API endpoint.30:T878,## How to Use Qwen3 VL Flash

Qwen3 VL Flash is a vision-language model that accepts a text prompt, an optional image, and returns a detailed response. Here is how to get the best results across different use cases.

### Text-Only Tasks

For pure text tasks â€” summarization, analysis, writing â€” simply fill in the **Prompt** field and leave **Image** blank. Use clear, direct instructions and specify your desired output format:

```
Summarize the following legal clause in plain English and highlight any risks: [paste text]
```

### Image Analysis and Visual QA

Attach an image and write a focused prompt describing what you want to extract or understand:

- OCR: "Extract all text from this invoice and return it as a JSON object with keys: vendor, date, total, line_items."
- Chart reading: "What is the highest revenue quarter shown in this bar chart? Return the value and year."
- Product tagging: "List the product attributes visible in this image: color, material, style, and any visible labels."

### Reasoning and Complex Tasks

For multi-step or analytical tasks, ask the model to reason step by step:

```
Step by step, analyze this architectural floor plan and identify potential accessibility issues for wheelchair users.
```

This naturally triggers the model's thinking mode for deeper analysis.

### Document Processing at Scale

For batch OCR or document extraction pipelines, use concise prompts and request structured output formats (JSON, CSV, markdown tables). Non-thinking mode is implicitly used when your prompt is direct and does not require reasoning chains.

### Video and Multi-Image Analysis

When sending video frames or multiple images, provide a guiding question and reference frame numbers or timestamps in your prompt to help the model ground its answers temporally.

### Tips for Best Output Quality

- Use high-resolution images for OCR tasks (800px minimum on the short edge)
- Specify output format explicitly (JSON, list, table, markdown table)
- For multilingual documents, mention the source language in your prompt
- For GUI automation tasks, describe the target action clearly1f:[["$","script",null,{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"SoftwareApplication\",\"@id\":\"https://www.segmind.com/models/qwen3-vl-flash\",\"name\":\"Qwen 3 VL Flash\",\"description\":\"Fast, affordable vision-language API with 262K context for OCR, visual QA, and multimodal document analysis.\",\"url\":\"https://www.segmind.com/models/qwen3-vl-flash\",\"applicationCategory\":\"AI Model\",\"applicationSubCategory\":\"textToText\",\"operatingSystem\":\"Any\",\"image\":\"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-5506.webp\",\"offers\":{\"@type\":\"Offer\",\"availability\":\"https://schema.org/InStock\",\"priceCurrency\":\"USD\",\"price\":\"0.00008282637632338787\"},\"provider\":{\"@type\":\"Organization\",\"@id\":\"https://www.segmind.com/#organization\",\"name\":\"Segmind\",\"url\":\"https://www.segmind.com\",\"logo\":\"https://www.segmind.com/favicon.svg\"}}"}}],["$","script",null,{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"$2d"}}],null,["$","$L2e",null,{"route":"qwen3-vl-flash","modelDetails":{"title":"Qwen 3 VL Flash","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-5506.webp","type":"textToText","model_type":"SO","is_depreciated":false,"meta_description":"Fast, affordable vision-language API with 262K context for OCR, visual QA, and multimodal document analysis.","seo_description":"$2f","guide":"$30","pricing_guide":null,"artifacts":"$1e:props:modelDetails:artifacts","default_image_output":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-example-output.txt","default_response":null,"parameters":"$1e:props:modelDetails:parameters","endpoints":"$1e:props:modelDetails:endpoints","api_notes":"$1e:props:modelDetails:api_notes","model_outputs":null,"llm_capabilities":null,"license":"","github_link":null,"inference_provider":"DASHSCOPE","created_ts":"2026-03-25 09:44:27.753308+00:00","slug":"qwen3-vl-flash","name":"Qwen 3 VL Flash","description":"Fast, affordable vision-language model with 262K context OCR.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-flash-5506.webp","is_new":false,"coming_soon":false,"popularity_score":83,"category_percentile":98,"average_cost":0.00008282637632338787,"latency_seconds":1.90857,"created_at":"2026-03-25 09:44:27.753308+00:00","parameters_schema":"$1e:props:modelDetails:parameters_schema"},"relatedTemplates":[],"popularModels":[{"slug":"qwen-flash","title":"Qwen Flash","description":"Fastest low-cost LLM with 1M context for high-volume tasks.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen-flash-3929.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":1.00187,"average_cost":0.00010557038321167881,"created_at":"2026-03-25 11:20:18.841471+00:00","popularity_score":72,"popularity_scale":"score","category_percentile":93},{"slug":"qwen3-vl-plus","title":"Qwen 3 VL Plus","description":"Powerful visual QA and document analysis from images.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen3-vl-plus-3248.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":2.29675,"average_cost":0.000526159748542274,"created_at":"2026-03-25 09:44:27.011897+00:00","popularity_score":72,"popularity_scale":"score","category_percentile":95},{"slug":"qwen-plus","title":"Qwen Plus","description":"Mid-tier 1M context LLM for summarization and content tasks.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen-plus-5506.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":1.62626,"average_cost":0.0003784640371229699,"created_at":"2026-03-25 11:20:13.259483+00:00","popularity_score":60,"popularity_scale":"score","category_percentile":85},{"slug":"qwen3.5-plus","title":"Qwen 3.5 Plus","description":"Multimodal 1M context AI for image, video, and text.","type":"textToText","partner":"ALIBABA","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qwen3.5-plus-3739.webp","default_image_output":"","is_new":false,"coming_soon":false,"is_visible":true,"segmind":true,"is_depreciated":false,"parameters":null,"latency":36.44783,"average_cost":0.008198834101382488,"created_at":"2026-03-24 09:25:46.849838+00:00","popularity_score":45,"popularity_scale":"score","category_percentile":69}]}]]
