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Bytedance modelsimage generation api

bytedance/seed/v2/mini

Seed V2 Mini by Bytedance - advanced AI model for llm. Delivers high-quality results with fast inference, suitable for both creative and production workflows.

Input
The text prompt or question for the model.

PNG, JPEG, WebP, or GIF · 20 MiB maximum each

URLs of images for visual understanding. Supported formats: JPEG, PNG, WebP. A maximum of 6 images is supported. Any additional images will be ignored.
URLs of videos for video understanding. Supported formats: MP4, MOV. Audio comprehension is not supported. A maximum of 3 videos is supported. Any additional videos will be ignored.
02
Controls randomness in the response. Lower values make output more focused and deterministic, higher values make it more creative. Range: 0 to 2.
165536
Controls the maximum length of the model's output, including both the model's response and its chain-of-thought content, measured in tokens. Range: 1 to 65536.
Optional prior conversation history for multi-turn conversations. Pass back the `messages` field from a previous response to provide context. The current `prompt`, `image_urls`, `video_urls`, and `system_prompt` are always appended as the latest user turn.
Controls the model's chain-of-thought reasoning. `enabled` always includes reasoning, `disabled` never includes reasoning, `auto` lets the model decide based on the query. Allowed values: enabled, disabled, auto.
01
Nucleus sampling parameter. The model considers tokens with top_p cumulative probability mass. Lower values narrow the token selection. Range: 0 to 1.
Optional system prompt to guide the model's behavior.
Controls the depth of reasoning before the model responds. Only applicable when `thinking` is `enabled` or `auto`. `minimal` for immediate response, `low` for faster response with light reasoning, `medium` for balanced speed and depth, `high` for deep analysis of complex issues. Allowed values: minimal, low, medium, high.
Idle

Example output — click Run to generate your own

API README

Bytedance Seed V2 Mini

Bytedance Seed V2 Mini is built for compact text processing for lightweight language understanding and generation. It gives creative and production teams a focused way to move from an approved brief or source asset to a reviewable result without fragmenting the job across unrelated tools. The model is most valuable when visual intent, brand suitability, and downstream usability all matter, because its output can enter an editorial, campaign, product, or content pipeline as a purposeful asset rather than an isolated experiment.

In practice, teams can use Bytedance Seed V2 Mini during a structured cycle of briefing, generation, comparison, and refinement. Establish the subject, audience, visual objective, and acceptance criteria first; prepare any reference media at suitable quality; then evaluate alternatives for composition, continuity, realism, and communication value before delivery. This workflow keeps creative judgment central while making repeated production easier to review, reproduce, and scale for the specific bytedance/seed/v2/mini task.

Highlights

Efficient short-form generation. Efficient short-form generation gives bytedance › seed › v2 › mini a recognizable technical advantage: reviewers can assess this property directly in the generated asset instead of inferring it from request mechanics.

Instruction following. bytedance › seed › v2 › mini applies instruction following to the visual or temporal result itself, helping artists make a meaningful quality decision during selection and refinement.

Multilingual text handling. For bytedance › seed › v2 › mini, multilingual text handling supports coherent assets across the intended creative workflow and distinguishes this capability from a simple format or delivery option.

Low-cost high-volume processing. The practical value of low-cost high-volume processing is visible in the finished media from bytedance › seed › v2 › mini, where it supports repeatable art direction rather than merely exposing another request setting.

Pricing

Prompt lengthPrice
Each started block of 1,000 characters$0.000100

When to Use

✅ Good fit❌ Consider alternatives
Use it when compact text processing for lightweight language understanding and generation is the central production goalChoose a different model when the required media task is fundamentally different
The team needs several reviewable creative alternativesExact deterministic reproduction is mandatory
Visual quality and practical downstream use both matterEditable source layers or native project files are required
A managed generation step fits the delivery workflowA live frame-by-frame interactive editor is essential
The documented inputs cover the available source assetsRequired source media or controls fall outside the documented fields

Prompt Guide

State the intended result first, then describe the subject, environment, action, visual treatment, and delivery constraints. Keep instructions concrete, avoid conflicting directions, and change one creative variable at a time when comparing outputs. For source-driven work, describe what should remain recognizable as clearly as what should change.

{
  "images": [
    "Describe the intended result in clear visual terms."
  ],
  "prompt": "What can you do?"
}

Technical Specs

PropertyDetails
top_pType / options: number · 0–1<br>Required: No<br>Description: Nucleus sampling parameter. The model considers tokens with top_p cumulative probability mass. Lower values narrow the token selection.
imagesType / options: array<br>Required: No<br>Description: URLs of images for visual understanding. Supported formats: JPEG, PNG, WebP. A maximum of 6 images is supported. Any additional images will be ignored.
promptType / options: string<br>Required: Yes<br>Description: The text prompt or question for the model.
videosType / options: array<br>Required: No<br>Description: URLs of videos for video understanding. Supported formats: MP4, MOV. Audio comprehension is not supported. A maximum of 3 videos is supported. Any additional videos will be ignored.
messagesType / options: array<br>Required: No<br>Description: Optional prior conversation history for multi-turn conversations. Pass back the messages field from a previous response to provide context. The current prompt, image_urls, video_urls, and system_prompt are always appended as the latest user turn.
thinkingType / options: string · enabled / disabled / auto<br>Required: No<br>Description: Controls the model's chain-of-thought reasoning. enabled always includes reasoning, disabled never includes reasoning, auto lets the model decide based on the query.
temperatureType / options: number · 0–2<br>Required: No<br>Description: Controls randomness in the response. Lower values make output more focused and deterministic, higher values make it more creative.
system_promptType / options: string<br>Required: No<br>Description: Optional system prompt to guide the model's behavior.
reasoning_effortType / options: string · minimal / low / medium / high<br>Required: No<br>Description: Controls the depth of reasoning before the model responds. Only applicable when thinking is enabled or auto. minimal for immediate response, low for faster response with light reasoning, medium for balanced speed and depth, high for deep analysis of complex issues.
max_completion_tokensType / options: integer · 1–65536<br>Required: No<br>Description: Controls the maximum length of the model's output, including both the model's response and its chain-of-thought content, measured in tokens.

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