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29:T1378,# QVQ Max â€” Visual Reasoning Model API

## What is QVQ Max?

QVQ Max is Alibaba Cloud's flagship visual reasoning model, developed by the Qwen team as the production successor to QVQ-72B-Preview. It is purpose-built for tasks that require extended logical reasoning over visual information â€” not just recognizing what is in an image, but thinking through it step-by-step to arrive at a well-reasoned answer.

Unlike general vision-language models that return instant direct answers, QVQ Max is a thinking-only model: every response begins with a transparent chain-of-thought reasoning process, visible in real time via streaming output. This makes it uniquely suited for complex visual problem-solving in education, research, analytics, and enterprise AI applications where accuracy and explainability are critical.

The model accepts text, images, and video frames within a 131,072-token context window, and always outputs its reasoning chain alongside the final answer.

## Key Features

- **Always-on chain-of-thought reasoning** â€” the model thinks through every problem before answering; reasoning is streamed in real time
- **131K token context window** â€” supports long multi-image prompts, video frames, and complex visual documents
- **Multi-image support** â€” compare, contrast, and reason across multiple images in a single call
- **Video understanding** â€” extract sequential reasoning from video frames with temporal grounding
- **Streaming-only output** â€” responses are delivered progressively, ideal for interactive and real-time applications
- **High accuracy on STEM problems** â€” excels at mathematics, physics, and chart interpretation tasks requiring inference, not just recognition

## Best Use Cases

**STEM Education and Tutoring**: Solve math, physics, and chemistry problems from textbook diagrams, handwritten notes, or whiteboard photos â€” with every reasoning step shown.

**Data Analysis and Business Intelligence**: Interpret complex charts, financial dashboards, and multi-variable graphs with detailed written explanations of trends and anomalies.

**Scientific and Medical Research**: Analyze annotated figures, experimental data plots, pathology slides, or research paper diagrams requiring domain-aware reasoning.

**Engineering and Architecture Review**: Read circuit schematics, system architecture diagrams, or CAD-style drawings and reason about design decisions or implementation steps.

**Visual QA for Enterprise**: Audit technical documents, annotated maps, compliance screenshots, or instructional manuals where inference over visual content is required.

**Code and UI Analysis**: Analyze screenshots of interfaces, error outputs, or code diagrams and reason about bugs, design improvements, or implementation steps.

## Prompt Tips and Output Quality

For best results with QVQ Max, frame your prompts as explicit questions that require reasoning, not just description. Instead of asking what is in this image, ask what mathematical principle the diagram demonstrates and to solve the problem step by step. The model performs best when it knows what conclusion to reason toward.

Since QVQ Max always shows its reasoning chain, you can reference intermediate steps in follow-up prompts. This makes it ideal for interactive, multi-turn analytical sessions.

For video inputs, specify the time range or frame numbers you want analyzed. For multi-image comparisons, number your images in the prompt and ask for explicit comparisons.

Be aware that thinking tokens are billed as output â€” for simple tasks, consider Qwen3 VL Flash as a more cost-effective alternative.

## FAQs

**Is the chain-of-thought reasoning optional?**
No â€” QVQ Max is a thinking-only model. Reasoning is always generated and cannot be disabled. This is by design, ensuring high accuracy on complex tasks.

**Does QVQ Max support video inputs?**
Yes â€” it can process video frames within its 131K context window, with temporal reasoning to understand sequences and events over time.

**How does QVQ Max compare to Qwen3 VL Flash?**
QVQ Max is optimized for deep, step-by-step reasoning on complex tasks and is always in thinking mode. Qwen3 VL Flash offers optional thinking mode at significantly lower cost, making it better for high-volume or simpler visual tasks.

**Why is QVQ Max streaming-only?**
Extended reasoning chains make streaming the natural output method â€” you see the model's thinking as it happens, which is valuable for interactive applications and educational use cases.

**What image formats are supported?**
QVQ Max accepts URLs and base64-encoded images in PNG, JPEG, and WebP formats. Multiple images can be included in a single prompt.

**Is QVQ Max suitable for real-time production workloads?**
It depends on the use case. For tasks requiring deep reasoning accuracy, QVQ Max is excellent. For high-throughput batch processing, Qwen3 VL Flash is more appropriate.2a:T819,## How to Use QVQ Max

QVQ Max is a visual reasoning model that thinks through problems step-by-step before answering. Here is how to get the best results.

### Understanding Thinking Mode

Every QVQ Max response begins with an extended chain-of-thought â€” the model works through the problem visually and logically before providing its final answer. This reasoning is streamed in real time. You cannot disable thinking mode; it is always active. Budget for this when estimating response latency and token cost.

### Text-Only Reasoning

For pure text tasks, fill in the Prompt with a clear, specific question:

A train leaves City A at 9:00 AM traveling at 80 km/h. Another train leaves City B (500 km away) at 10:00 AM traveling toward City A at 100 km/h. At what time do they meet?

The model will reason through the problem and show its working before giving the final answer.

### Visual Problem Solving

Attach an image and pose a specific reasoning question:

- Math diagrams: Using the geometry diagram provided, calculate the area of the shaded region. Show all steps.
- Charts: What is the compound annual growth rate implied by this line chart between 2020 and 2024?
- Scientific figures: Analyze this circuit diagram and identify any potential failure points.
- Code screenshots: Review this error output screenshot and reason through the most likely cause.

### Multi-Image Analysis

When sending multiple images, number them explicitly in your prompt and ask for explicit comparisons between them.

### Video Understanding

For video inputs, specify the timeframe or action you want analyzed to get precise temporal reasoning.

### Tips for Best Output Quality

- Frame prompts as explicit reasoning questions, not just descriptive requests
- Reference specific elements in images: the red highlighted row, the leftmost column
- For follow-up questions, reference prior reasoning steps
- Use high-resolution images for diagram-heavy tasks
- For long sessions, summarize prior context to stay within the 131K token limit1e:["$","$L28",null,{"route":"qvq-max","modelDetails":{"title":"QVQ Max","thumbnail_image":"https://segmind-resources.s3.amazonaws.com/output/qvq-max-3929.webp","type":"textToText","model_type":"SO","is_depreciated":false,"meta_description":"Visual reasoning model with always-on chain-of-thought â€” solves math diagrams, charts, and complex visual problems step-by-step.","seo_description":"$29","guide":"$2a","pricing_guide":null,"artifacts":[],"default_image_output":"https://segmind-resources.s3.amazonaws.com/output/qvq-max-example-output.txt","default_response":null,"parameters":{"prompt":{"isRequired":true,"affectsPricing":false,"type":"str","description":"Question or instruction for model to reason through visually.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":0,"component":"textarea","displayValue":"Analyze this bar chart. What are the quarterly revenue figures shown? Calculate the total annual revenue and identify the quarter with the highest growth rate.","label":"prompt","default":"Analyze this bar chart. What are the quarterly revenue figures shown? Calculate the total annual revenue and identify the quarter with the highest growth rate."},"image":{"isRequired":false,"affectsPricing":false,"type":"image","description":"Image for model to analyze: diagrams, charts, or screenshots.","isEnum":false,"hidden":false,"isAdvanced":false,"displayOrder":1,"component":"image","label":"Image","default":"https://segmind-resources.s3.amazonaws.com/input/qvq-max-test-input.jpg","displayValue":"https://segmind-resources.s3.amazonaws.com/input/qvq-max-test-input.jpg"},"response_format":{"type":"object","format":"json","component":"textarea","label":"Response format","description":"Structured output: {\"type\": \"json_object\"} returns valid JSON (the prompt must mention JSON). This model does not enforce a JSON Schema.","displayValue":{"type":"json_object"},"isRequired":false,"isAdvanced":true,"hidden":false,"affectsPricing":false,"isEnum":false,"displayOrder":2}},"endpoints":{"main":"/v1/qvq-max"},"api_notes":[],"model_outputs":null,"llm_capabilities":null,"license":"","github_link":null,"inference_provider":"DASHSCOPE","created_ts":"2026-03-25 09:44:28.363486+00:00","slug":"qvq-max","name":"QVQ Max","description":"Chain-of-thought visual reasoning for math, charts, and diagrams.","category":"textToText","modality":"llm","input":"text","output":"text","provider":"ALIBABA","thumbnail_url":"https://segmind-resources.s3.amazonaws.com/output/qvq-max-3929.webp","is_new":false,"coming_soon":false,"popularity_score":0,"category_percentile":0,"average_cost":0.00240075,"latency_seconds":9.60836,"created_at":"2026-03-25 09:44:28.363486+00:00","parameters_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"prompt":{"type":"string","title":"Prompt","description":"Question or instruction for model to reason through visually.","default":"Analyze this bar chart. What are the quarterly revenue figures shown? Calculate the total annual revenue and identify the quarter with the highest growth rate.","x-ui-metadata":{"order":0,"advanced":false,"hidden":false}},"image":{"type":"string","format":"uri","x-file-metadata":{"allowed-types":["image/*"]},"title":"Image","description":"Image for model to analyze: diagrams, charts, or screenshots.","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; this model does not enforce a json_schema. Omit for plain text. The prompt must mention JSON.","examples":[{"type":"json_object"}],"x-ui-metadata":{"order":2,"advanced":true,"hidden":false}}},"required":["prompt"],"examples":[{"prompt":"Analyze this bar chart. What are the quarterly revenue figures shown? Calculate the total annual revenue and identify the quarter with the highest growth rate.","image":"https://segmind-resources.s3.amazonaws.com/input/qvq-max-test-input.jpg"}]}},"metrics":{"total_requests":null,"latency":9.60836},"children":"$L2b"}]
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2d:T724,{"@context":"https://schema.org","@type":"FAQPage","@id":"https://www.segmind.com/models/qvq-max#faq","mainEntity":[{"@type":"Question","name":"Is the chain-of-thought reasoning optional?","acceptedAnswer":{"@type":"Answer","text":"No â€” QVQ Max is a thinking-only model. Reasoning is always generated and cannot be disabled. This is by design, ensuring high accuracy on complex tasks."}},{"@type":"Question","name":"Does QVQ Max support video inputs?","acceptedAnswer":{"@type":"Answer","text":"Yes â€” it can process video frames within its 131K context window, with temporal reasoning to understand sequences and events over time."}},{"@type":"Question","name":"How does QVQ Max compare to Qwen3 VL Flash?","acceptedAnswer":{"@type":"Answer","text":"QVQ Max is optimized for deep, step-by-step reasoning on complex tasks and is always in thinking mode. Qwen3 VL Flash offers optional thinking mode at significantly lower cost, making it better for high-volume or simpler visual tasks."}},{"@type":"Question","name":"Why is QVQ Max streaming-only?","acceptedAnswer":{"@type":"Answer","text":"Extended reasoning chains make streaming the natural output method â€” you see the model's thinking as it happens, which is valuable for interactive applications and educational use cases."}},{"@type":"Question","name":"What image formats are supported?","acceptedAnswer":{"@type":"Answer","text":"QVQ Max accepts URLs and base64-encoded images in PNG, JPEG, and WebP formats. Multiple images can be included in a single prompt."}},{"@type":"Question","name":"Is QVQ Max suitable for real-time production workloads?","acceptedAnswer":{"@type":"Answer","text":"It depends on the use case. For tasks requiring deep reasoning accuracy, QVQ Max is excellent. For high-throughput batch processing, Qwen3 VL Flash is more appropriate."}}]}2f:T1378,# QVQ Max â€” Visual Reasoning Model API

## What is QVQ Max?

QVQ Max is Alibaba Cloud's flagship visual reasoning model, developed by the Qwen team as the production successor to QVQ-72B-Preview. It is purpose-built for tasks that require extended logical reasoning over visual information â€” not just recognizing what is in an image, but thinking through it step-by-step to arrive at a well-reasoned answer.

Unlike general vision-language models that return instant direct answers, QVQ Max is a thinking-only model: every response begins with a transparent chain-of-thought reasoning process, visible in real time via streaming output. This makes it uniquely suited for complex visual problem-solving in education, research, analytics, and enterprise AI applications where accuracy and explainability are critical.

The model accepts text, images, and video frames within a 131,072-token context window, and always outputs its reasoning chain alongside the final answer.

## Key Features

- **Always-on chain-of-thought reasoning** â€” the model thinks through every problem before answering; reasoning is streamed in real time
- **131K token context window** â€” supports long multi-image prompts, video frames, and complex visual documents
- **Multi-image support** â€” compare, contrast, and reason across multiple images in a single call
- **Video understanding** â€” extract sequential reasoning from video frames with temporal grounding
- **Streaming-only output** â€” responses are delivered progressively, ideal for interactive and real-time applications
- **High accuracy on STEM problems** â€” excels at mathematics, physics, and chart interpretation tasks requiring inference, not just recognition

## Best Use Cases

**STEM Education and Tutoring**: Solve math, physics, and chemistry problems from textbook diagrams, handwritten notes, or whiteboard photos â€” with every reasoning step shown.

**Data Analysis and Business Intelligence**: Interpret complex charts, financial dashboards, and multi-variable graphs with detailed written explanations of trends and anomalies.

**Scientific and Medical Research**: Analyze annotated figures, experimental data plots, pathology slides, or research paper diagrams requiring domain-aware reasoning.

**Engineering and Architecture Review**: Read circuit schematics, system architecture diagrams, or CAD-style drawings and reason about design decisions or implementation steps.

**Visual QA for Enterprise**: Audit technical documents, annotated maps, compliance screenshots, or instructional manuals where inference over visual content is required.

**Code and UI Analysis**: Analyze screenshots of interfaces, error outputs, or code diagrams and reason about bugs, design improvements, or implementation steps.

## Prompt Tips and Output Quality

For best results with QVQ Max, frame your prompts as explicit questions that require reasoning, not just description. Instead of asking what is in this image, ask what mathematical principle the diagram demonstrates and to solve the problem step by step. The model performs best when it knows what conclusion to reason toward.

Since QVQ Max always shows its reasoning chain, you can reference intermediate steps in follow-up prompts. This makes it ideal for interactive, multi-turn analytical sessions.

For video inputs, specify the time range or frame numbers you want analyzed. For multi-image comparisons, number your images in the prompt and ask for explicit comparisons.

Be aware that thinking tokens are billed as output â€” for simple tasks, consider Qwen3 VL Flash as a more cost-effective alternative.

## FAQs

**Is the chain-of-thought reasoning optional?**
No â€” QVQ Max is a thinking-only model. Reasoning is always generated and cannot be disabled. This is by design, ensuring high accuracy on complex tasks.

**Does QVQ Max support video inputs?**
Yes â€” it can process video frames within its 131K context window, with temporal reasoning to understand sequences and events over time.

**How does QVQ Max compare to Qwen3 VL Flash?**
QVQ Max is optimized for deep, step-by-step reasoning on complex tasks and is always in thinking mode. Qwen3 VL Flash offers optional thinking mode at significantly lower cost, making it better for high-volume or simpler visual tasks.

**Why is QVQ Max streaming-only?**
Extended reasoning chains make streaming the natural output method â€” you see the model's thinking as it happens, which is valuable for interactive applications and educational use cases.

**What image formats are supported?**
QVQ Max accepts URLs and base64-encoded images in PNG, JPEG, and WebP formats. Multiple images can be included in a single prompt.

**Is QVQ Max suitable for real-time production workloads?**
It depends on the use case. For tasks requiring deep reasoning accuracy, QVQ Max is excellent. For high-throughput batch processing, Qwen3 VL Flash is more appropriate.30:T819,## How to Use QVQ Max

QVQ Max is a visual reasoning model that thinks through problems step-by-step before answering. Here is how to get the best results.

### Understanding Thinking Mode

Every QVQ Max response begins with an extended chain-of-thought â€” the model works through the problem visually and logically before providing its final answer. This reasoning is streamed in real time. You cannot disable thinking mode; it is always active. Budget for this when estimating response latency and token cost.

### Text-Only Reasoning

For pure text tasks, fill in the Prompt with a clear, specific question:

A train leaves City A at 9:00 AM traveling at 80 km/h. Another train leaves City B (500 km away) at 10:00 AM traveling toward City A at 100 km/h. At what time do they meet?

The model will reason through the problem and show its working before giving the final answer.

### Visual Problem Solving

Attach an image and pose a specific reasoning question:

- Math diagrams: Using the geometry diagram provided, calculate the area of the shaded region. Show all steps.
- Charts: What is the compound annual growth rate implied by this line chart between 2020 and 2024?
- Scientific figures: Analyze this circuit diagram and identify any potential failure points.
- Code screenshots: Review this error output screenshot and reason through the most likely cause.

### Multi-Image Analysis

When sending multiple images, number them explicitly in your prompt and ask for explicit comparisons between them.

### Video Understanding

For video inputs, specify the timeframe or action you want analyzed to get precise temporal reasoning.

### Tips for Best Output Quality

- Frame prompts as explicit reasoning questions, not just descriptive requests
- Reference specific elements in images: the red highlighted row, the leftmost column
- For follow-up questions, reference prior reasoning steps
- Use high-resolution images for diagram-heavy tasks
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