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  4. /qwen3.8-max-0902 / video-to-text
Qwen
Qwen3.8 Max 0902
$ 
The qwen3.8-max-0902 video to text API provides robust capabilities for converting complex visual sequences into structured, actionable data. Designed for high-throughput environments on GPT Proto, this model excels at temporal understanding, object identification, and semantic transcription. Whether you are building automated indexing systems or content moderation pipelines, the qwen3.8-max-0902 video to text model offers the precision and scalability required for modern media workflows. Integrate this advanced vision-language engine today to transform your video assets into searchable, machine-readable text formats with ease and reliability.

模態

輸入: 文字輸入: 圖像輸入: 影片輸入: 文件輸入: 音訊
輸出: 文字

/

Qwen3.8 Max 0902 pricing

Estimate a request with real work scenarios. GPTProto token pricing is 10% below official rates.

用量數量單價小計
tokens
$1.8/1M$0.0027
tokens
$5.4/1M$0.0043
tokens
$2.25/1M$0.0067
tokens
$0.225/1M$0.0056
單次請求費用$0.0193
請求次數
儲值金額

儲值 $100 你將獲得:

1、

儲值額度永久有效。你總共會收到 $100.00。

2、

額外 10% 模型折扣,相較官方 Qwen API 可節省 $11.0902。

相關模型
所有模型
Qwen3.8 Max 0902
目前
$ 
byQwen$1.8/M input$5.4/M output
GLM 5.3 Flash
$ 
byZ-AI1.31M context$0.15/M input$0.5/M output
DeepSeek v4 Flash Vision Exp
$ 
byDeepSeek1.05M context$0.44/M input$1.32/M output
GLM 5.3
$ 
byZ-AI1.31M context$1.26/M input$3.96/M output
Gemini 3.7 Flash
$ 
byGoogle1.05M context$0.45/M input$2.25/M output
Grok 4.6
$ 
byGrok500K context$1.2/M input$3.6/M output
Qwen3.8 Max
$ 
byQwen1M context$1.8/M input$5.4/M output
Claude Opus 5
$ 
byClaude1M context$4.5/M input$22.5/M output
Gemini 3.6 Flash
$ 
byGoogle1.05M context$0.45/M input$2.25/M output
Gemini 3.5 Flash Lite
$ 
byGoogle1.05M context$0.18/M input$1.5/M output
Kimi K3
$ 
byMoonshotAI1.05M context$2.7/M input$13.5/M output
GPT 5.6 Luna
$ 
byOpenAI1.05M context$0.16/M input$0.96/M output
GPT 5.6 Terra
$ 
byOpenAI1.05M context$1.6/M input$9.6/M output
GPT 5.6 Sol
$ 
byOpenAI1.05M context$3.2/M input$16/M output
Grok 4.5
$ 
byGrok500K context$1.2/M input$3.6/M output
Claude Sonnet 5
$ 
byClaude1M context$1.8/M input$9/M output
MiniMax M3
$ 
byMiniMax1.05M context$0.48/M input$0.96/M output
GLM 5.2
$ 
byZ-AI1.05M context$1.26/M input$3.96/M output
Claude Fable 5
$ 
byClaude1M context$9/M input$45/M output
Qwen3.7 Max
$ 
byQwen1M context$0.36/M input$1.44/M output
DeepSeek v4 Flash
$ 
byDeepSeek1.05M context$0.44/M input$1.32/M output
DeepSeek v4 Pro
$ 
byDeepSeek1.05M context$1.32/M input$3.96/M output
Grok 4.3
$ 
byGrok1M context$0.75/M input$1.5/M output
Kimi K2.6
$ 
byMoonshotAI262K context$0.855/M input$3.6/M output
MiniMax M2.5
$ 
byMiniMax205K context$0.24/M input$0.96/M output
Kimi K2.5
$ 
byMoonshotAI262K context$0.54/M input$2.7/M output
Qwen Turbo
$ 
byQwen$0.045/M input$0.18/M output
Qwen Plus
$ 
byQwen1M context$0.36/M input$1.08/M output
Qwen3 Max
$ 
byQwen262K context$1.08/M input$5.4/M output
Doubao Seed 1.6 Thinking (Build 250715)
$ 
byBytedance262K context$0.0971/M input$0.9714/M output
Doubao Seed 1.6 Thinking (Build 250615)
$ 
byBytedance262K context$0.0971/M input$0.9714/M output
Doubao Seed 1.6 Flash (Build 250615)
$ 
byBytedance262K context$0.0182/M input$0.1821/M output
模型輸入 → 輸出
Qwen3.8 Max 0902目前
$ 
—$1.80 / $5.40 每 1M$2.25 / $0.23 每 1M
輸入: 文字輸入: 圖像輸入: 影片輸入: 文件輸入: 音訊
輸出: 文字
GLM 5.3 Flash
$ 
—1.31M$0.15 / $0.50 每 1M— / $0.03 每 1M
輸入: 文字輸入: 圖像輸入: 影片輸入: 文件
輸出: 文字
DeepSeek v4 Flash Vision Exp
$ 
—1.05M$0.44 / $1.32 每 1M— / $0.01 每 1M
輸入: 文字輸入: 圖像
輸出: 文字
GLM 5.3
$ 
1.31M$1.26 / $3.96 每 1M— / $0.23 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Gemini 3.7 Flash
$ 
1.05M$0.45 / $2.25 每 1M— / $0.04 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Grok 4.6
$ 
500K$1.20 / $3.60 每 1M— / $0.30 每 1M
輸入: 文字輸入: 圖像
輸出: 文字
Qwen3.8 Max
$ 
1M$1.80 / $5.40 每 1M$2.25 / $0.23 每 1M
輸入: 文字輸入: 圖像輸入: 影片輸入: 文件
輸出: 文字
Claude Opus 5
$ 
1M$4.50 / $22.50 每 1M$5.63 / $0.45 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Gemini 3.6 Flash
$ 
1.05M$0.45 / $2.25 每 1M— / $0.04 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Gemini 3.5 Flash Lite
$ 
1.05M$0.18 / $1.50 每 1M$0.02 / $0.02 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Kimi K3
$ 
1.05M$2.70 / $13.50 每 1M$0.27 / $0.27 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
GPT 5.6 Luna
$ 
1.05M$0.16 / $0.96 每 1M$0.20 / $0.02 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
GPT 5.6 Terra
$ 
1.05M$1.60 / $9.60 每 1M$2.00 / $0.16 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
GPT 5.6 Sol
$ 
1.05M$3.20 / $16.00 每 1M$4.00 / $0.32 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Grok 4.5
$ 
500K$1.20 / $3.60 每 1M$0.30 / $0.30 每 1M
輸入: 文字輸入: 圖像
輸出: 文字
Claude Sonnet 5
$ 
1M$1.80 / $9.00 每 1M$2.25 / $0.18 每 1M
輸入: 文字輸入: 文件
輸出: 文字
MiniMax M3
$ 
1.05M$0.48 / $0.96 每 1M$0.10 / $0.10 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
GLM 5.2
$ 
1.05M$1.26 / $3.96 每 1M$0.23 / $0.23 每 1M
輸入: 文字輸入: 圖像輸入: 文件
輸出: 文字
Claude Fable 5
$ 
1M$9.00 / $45.00 每 1M$11.25 / $0.90 每 1M
輸入: 文字輸入: 文件
輸出: 文字
Qwen3.7 Max
$ 
1M$0.36 / $1.44 每 1M$0.07 / $0.07 每 1M
輸入: 文字輸入: 文件
輸出: 文字
DeepSeek v4 Flash
$ 
—1.05M$0.44 / $1.32 每 1M— / $0.01 每 1M
輸入: 文字
輸出: 文字
DeepSeek v4 Pro
$ 
—1.05M$1.32 / $3.96 每 1M— / $0.04 每 1M
輸入: 文字
輸出: 文字
Grok 4.3
$ 
1M$0.75 / $1.50 每 1M$0.12 / $0.12 每 1M
輸入: 文字輸入: 圖像
輸出: 文字
Kimi K2.6
$ 
262K$0.85 / $3.60 每 1M$0.14 / $0.14 每 1M
輸入: 文字輸入: 文件
輸出: 文字
MiniMax M2.5
$ 
205K$0.24 / $0.96 每 1M$0.30 / $0.02 每 1M
輸入: 文字輸入: 文件
輸出: 文字
Kimi K2.5
$ 
262K$0.54 / $2.70 每 1M$0.09 / $0.09 每 1M
輸入: 文字輸入: 文件
輸出: 文字
Qwen Turbo
$ 
—$0.04 / $0.18 每 1M$0.009 / $0.009 每 1M
輸入: 文字
輸出: 文字
Qwen Plus
$ 
1M$0.36 / $1.08 每 1M$0.07 / $0.07 每 1M
輸入: 文字
輸出: 文字
Qwen3 Max
$ 
262K$1.08 / $5.40 每 1M$0.22 / $0.22 每 1M
輸入: 文字
輸出: 文字
Doubao Seed 1.6 Thinking (Build 250715)
$ 
262K$0.10 / $0.97 每 1M—
輸入: 文字輸入: 圖像
輸出: 文字
Doubao Seed 1.6 Thinking (Build 250615)
$ 
262K$0.10 / $0.97 每 1M—
輸入: 文字輸入: 圖像
輸出: 文字
Doubao Seed 1.6 Flash (Build 250615)
$ 
262K$0.02 / $0.18 每 1M—
輸入: 文字輸入: 圖像
輸出: 文字

Understanding the qwen3.8-max-0902 video to text API

The qwen3.8-max-0902 video to text model is a state-of-the-art multimodal engine designed for high-fidelity conversion of video content into structured text. It is purpose-built for developers who need to automate the analysis of large-scale video data, offering superior accuracy in scene recognition and linguistic transcription.

By utilizing the qwen3.8-max-0902 video to text model on the GPT Proto platform, users benefit from:

  • Deep temporal analysis for accurate event logging.
  • High-resolution visual recognition capabilities.
  • Scalable API architecture suitable for production environments.
  • Seamless integration with existing text-based processing pipelines.

Who Should Use qwen3.8-max-0902 video to text for Video to Text API Workflows

Who Should Choose qwen3.8-max-0902 video to text for Video to Text API?

This model is ideal for developers and enterprises that require deep, nuanced interpretation of video files. It fits perfectly into workflows where visual elements need to be indexed, summarized, or audited at scale.

  • Media Archivers: Professionals who need to make thousands of hours of legacy footage searchable through automated tagging.
  • Content Moderators: Teams requiring an automated layer to detect and categorize visual events in real-time.
  • Educational Platforms: Developers creating tools to generate automated lecture notes and summaries from video-based content.
  • Market Researchers: Analysts looking to extract qualitative insights from large sets of consumer video feedback.

Pro Tips for Using qwen3.8-max-0902 video to text for Video to Text API

  • Pre-process your video files to ensure standard frame rates; this significantly improves the model's temporal consistency.
  • When requesting complex analysis, provide clear, concise prompts to help the model focus on specific visual regions or action sequences.
  • Use the API's batch processing features to handle high-volume video ingestion efficiently.
  • Verify the output against known ground truths in your initial testing to fine-tune your prompt engineering for specific content types.
  • Monitor your usage via the GPT Proto dashboard to keep your balance healthy and ensure consistent uptime for your application.

Harnessing the qwen3.8-max-0902 video to text API for Intelligent Media Analysis

Unlock the full potential of your visual library by integrating the qwen3.8-max-0902 video to text API. Start your implementation via the GPT Proto model dashboard and experience enterprise-grade multimodal processing.

Solving the Complexity of Visual Data Interpretation

In an era where video content dominates digital consumption, the ability to extract meaningful data from these files is critical. Traditional metadata tagging is often insufficient for modern requirements. The qwen3.8-max-0902 video to text API bridges the gap between raw visual input and structured text output. By utilizing advanced temporal-spatial awareness, the model analyzes frame sequences to produce accurate descriptions, transcriptions, and event logs. This process eliminates the labor-intensive manual review of video archives, allowing developers to focus on building features that rely on deep content understanding.

Automated Archival Indexing

For organizations managing massive libraries, the qwen3.8-max-0902 video to text API serves as a cornerstone for searchability. By converting visual narratives into text, you enable granular search queries that were previously impossible. When implementing this on GPT Proto, ensure your source files are optimized for processing to maintain high precision in identifying on-screen text, speakers, and environmental context.

Real-Time Content Moderation

Security and compliance teams benefit significantly from the qwen3.8-max-0902 video to text model's ability to identify sensitive content patterns. The API provides a scalable way to flag inappropriate segments by analyzing the flow of action and dialogue. This proactive approach ensures that your platform maintains safety standards without the overhead of human intervention for every frame.

The integration of qwen3.8-max-0902 video to text transforms how we process visual streams, turning hours of footage into searchable data in minutes. It is the most reliable tool for our automated archival workflow.

Seamless Integration on GPT Proto

GPT Proto is built to handle the high-concurrency demands of the qwen3.8-max-0902 video to text API. We provide a stable, high-uptime environment where you can manage your API keys, monitor performance, and scale your operations without friction. For detailed technical integration steps, please consult our official documentation.

FeatureStandard Modelsqwen3.8-max-0902 video to text on GPT Proto
Temporal ConsistencyBaselineHigh-Precision
Multimodal ContextBasicAdvanced
API ReliabilityVariableEnterprise-Grade
ScalabilityLimitedOptimized

Transparent Billing and Usage

Managing your usage is straightforward on GPT Proto. We utilize a flexible balance system rather than restrictive credits. You can easily Add Funds to your account to ensure uninterrupted service. For a comprehensive overview of your consumption and to manage your API settings, navigate to the User Dashboard. We prioritize financial transparency so you can focus on building your application. For more insights on optimizing your API usage, visit our blog for the latest technical updates.

Common Questions About qwen3.8-max-0902 video to text

Find answers to frequently asked questions regarding the implementation and usage of the qwen3.8-max-0902 video to text model.

What is the primary function of the qwen3.8-max-0902 video to text model?

The qwen3.8-max-0902 video to text model is designed to analyze video sequences and generate descriptive, actionable text outputs based on visual input.

How do I access the qwen3.8-max-0902 video to text API?

You can access the qwen3.8-max-0902 video to text API directly through the GPT Proto dashboard after setting up your account and adding funds.

Does the qwen3.8-max-0902 video to text model support long videos?

The qwen3.8-max-0902 video to text model is optimized for efficient processing, though we recommend segmenting very long videos for better temporal accuracy.

Is my data secure when using qwen3.8-max-0902 video to text?

GPT Proto treats all data processed by the qwen3.8-max-0902 video to text model with enterprise-grade privacy and security protocols.

Can I integrate qwen3.8-max-0902 video to text into my own app?

Yes, the qwen3.8-max-0902 video to text model is fully API-accessible, allowing for seamless integration into custom software solutions.

How do I manage costs for qwen3.8-max-0902 video to text?

You can manage your qwen3.8-max-0902 video to text usage costs by monitoring your balance in the billing center and choosing your recharge amounts.

What input formats are supported for qwen3.8-max-0902 video to text?

Check the GPT Proto documentation for the list of supported container formats for the qwen3.8-max-0902 video to text API.

Does qwen3.8-max-0902 video to text provide frame-by-frame analysis?

The qwen3.8-max-0902 video to text model uses advanced temporal sampling to provide high-quality analysis that covers the entire duration of the video.

Are there rate limits for the qwen3.8-max-0902 video to text API?

Rate limits for the qwen3.8-max-0902 video to text API are defined by your account tier on GPT Proto; details are available in the dashboard.

Can qwen3.8-max-0902 video to text identify specific objects?

Yes, the qwen3.8-max-0902 video to text model is highly capable of identifying and describing objects within the visual frame.

Is the qwen3.8-max-0902 video to text model suitable for real-time apps?

The qwen3.8-max-0902 video to text model is highly efficient, though real-time performance depends on your network latency and video resolution.

Where can I get help with qwen3.8-max-0902 video to text?

For help with qwen3.8-max-0902 video to text, consult our extensive documentation or contact the GPT Proto support team.

GPT Proto

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導覽

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  • 聊天
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功能

  • AI 包裝設計生成器
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  • AI 物件移除器
  • AI 圖片編輯器
  • AI 動作轉移
  • AI 浮水印移除工具
  • 線上 AI 圖片增強器
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Explore all features >

LLM

  • Qwen3.8 Max 0902
  • GLM 5.3
  • Claude Fable 5
  • DeepSeek v4 Pro
  • Gemini 3.7 Flash
  • Grok 4.6
  • GLM 5.3 Flash
  • DeepSeek v4 Flash Vision Exp
  • Qwen3.8 Max
  • Claude Opus 5
  • Gemini 3.6 Flash
  • Gemini 3.5 Flash Lite
  • Kimi K3
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