Seedream 5.0 Flash vs Wan2.7 Image

Seedream 5.0 Flash vs Wan2.7 Image at a glance

Seedream 5.0 Flash from ByteDance and Wan2.7 Image from Alibaba Cloud are both in the Image Generation category, available through the EmpirioLabs API.

Key differences

• Seedream 5.0 Flash is the newer release (Sep 28, 2026); Wan2.7 Image was released on Apr 1, 2026.
• Capabilities listed only for Seedream 5.0 Flash: image generation, layer decomposition, interactive editing, multi reference, text rendering, multilingual text.
• Capabilities listed only for Wan2.7 Image: 4k.

Seedream 5.0 Flash vs Wan2.7 Image: specs

Seedream 5.0 Flash vs Wan2.7 Image: API pricing

Live rates from the EmpirioLabs catalog for both models. Each rate card always matches what the API charges.

Seedream 5.0 Flash vs Wan2.7 Image: API parameters

Request parameters each model lists on EmpirioLabs, with the allowed values or range where one applies. A dash marks a parameter the model does not list.

Seedream 5.0 Flash vs Wan2.7 Image: limits and notes

Seedream 5.0 Flash

Modes

• Text to image
• Single or multi-image to image
• Interactive editing with coordinate or sketch markers
• Layer decomposition into a base image plus up to 16 layers

Interactive editing

Mark the region to change directly on the input image with a sketch, circle, or arrow, then describe it in the prompt. For exact placement, use coordinate tags on a 0 to 1000 normalized grid:

Place the subject from image 1 <bbox>179 283 796 986</bbox> at the position in image 2 <bbox>118 331 933 871</bbox>.

Layer decomposition

Set layer_decomposition to true with exactly one input image to split it into a base image plus up to 16 transparent PNG layers. Every returned image carries its stacking order as z_index, and each layer also carries its position in the base image as bounding_box.absolute and bounding_box.normalized, plus a name and a description. Leave the prompt empty to let the model choose the elements, or name the elements or coordinate regions to separate. Resolution accepts 1K, 1.5K, or 2K, and matches the input image when unset.

Limits

• One output image per request, except layer decomposition, which returns the base image plus its layers
• Up to 10 reference images, and exactly one for layer decomposition or a transparent background
• 1K, 1.5K, and 2K resolution tiers
• PNG or JPEG output, and layers are always PNG
• A transparent background needs one input image that already has an alpha channel, and PNG output
• No grouped output, streaming, or web search

Billing

Every resolution tier bills at the same per-image rate, and reference images are free. Layer decomposition bills every returned image, the base image and each layer, at that same rate.

Content safety

Generated images pass an automated content-safety check. An image flagged as explicit is not returned. Because the image is generated before the check runs, a flagged generation is still billed.

Wan2.7 Image

Upload 1-9 images for editing mode; without images, operates as text-to-image.

Constraints

• Prompt max 5,000 characters
• Generation: 10-60s depending on resolution, image count, model variant, and thinking mode

Image inputs (editing mode)

• JPEG, PNG, BMP, or WEBP
• 240-8000 px per side
• Aspect ratio 1:8 to 8:1
• Max 20 MB per image

Capabilities

• Style transfer, object placement, scene blending, background replacement, multi-reference composition, interactive editing with bounding boxes
• Pro variant supports up to 4K output

Content safety

• Generated images pass an automated content-safety check. An image flagged as explicit is not returned. Because the image is generated before the check runs, a flagged generation is still billed.

How to call Seedream 5.0 Flash and Wan2.7 Image

Seedream 5.0 Flash runs through POST /v1/images/generations. The request returns a job_id right away; poll GET /v1/jobs/{job_id} until the job completes and read the output URLs from the result. Or add "sync": true to skip polling: the request stays open and returns the finished OpenAI-style image response directly, so OpenAI SDKs work as-is. Both models are called through the same endpoint. Set model to seedream-5-0-flash or wan2-7-image. Get an API key from the EmpirioLabs dashboard.

curl https://api.empiriolabs.ai/v1/images/generations \
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "seedream-5-0-flash",
    "prompt": "Describe what you want Seedream 5.0 Flash to generate."
  }'

Seedream 5.0 Flash vs Wan2.7 Image: tests and guides

Prompt tests and comparison runs that cover both models.

Guides Qwen Image 2.1 vs Qwen Image 3.0 vs Seedream 5.0 Flash vs Wan 2.7 Image: three prompts We gave Qwen Image 2.1, Qwen Image 3.0, Seedream 5.0 Flash and Wan 2.7 Image the same three prompts at 2K, one shot each,...

Seedream 5.0 Flash vs Wan2.7 Image: common questions

What is the difference between Seedream 5.0 Flash and Wan2.7 Image?

Seedream 5.0 Flash is the newer release (Sep 28, 2026); Wan2.7 Image was released on Apr 1, 2026. Capabilities listed only for Seedream 5.0 Flash: image generation, layer decomposition, interactive editing, multi reference, text rendering, multilingual text. Capabilities listed only for Wan2.7 Image: 4k. The specs, rate cards, and parameters on this page come from the live EmpirioLabs catalog.

Which is newer, Seedream 5.0 Flash or Wan2.7 Image?

Seedream 5.0 Flash is the newer release (Sep 28, 2026); Wan2.7 Image was released on Apr 1, 2026.

Which has the lower rate, Seedream 5.0 Flash or Wan2.7 Image?

The two models are priced on different units or tiers, so compare the live rate cards on this page. They always match what the API charges.

Can I switch between Seedream 5.0 Flash and Wan2.7 Image without rebuilding my integration?

Yes. Both models use POST /v1/images/generations with the same EmpirioLabs API key, so most integrations switch by changing the model value. Check the parameters table on this page first if you rely on a model-specific parameter.

Can I try Seedream 5.0 Flash and Wan2.7 Image in the browser before integrating?

Yes. Open each model in the EmpirioLabs playground and run the same prompt on both, with the parameters the API exposes.