Tasks and inputs
Muse Image 1.0 is listed for Image-text-to-image, Text-to-image. It accepts text, image and produces image. Use the publisher’s input format and preprocessing for your chosen deployment.
Muse Image 1.0 generates and edits images through conversational or single-shot endpoints, with flat per-image Meta Model API billing.
Pricing conditions: $0.01 per successfully returned image. Built-in web and image search are included. The rate is per image, not per million tokens.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
Muse Image accepts a text brief and optional reference images, returning an image. A conversation can continue with successive edits, while single-shot generation and editing endpoints cover one-off requests. This makes the model relevant when a visual asset needs several directed revisions rather than a single prompt.
Use muse-image-1.0 with the Responses interface for conversational work, or the images generation and edit endpoints for individual operations. The surrounding application needs to preserve the returned image identifiers and conversation state when continuing an edit. Check the rendered asset after every revision rather than assuming that unchanged regions stayed fixed.
Meta Model API lists a flat $0.01 for each image successfully generated and returned. Built-in visual and web grounding are included in that image price. Token counts in a response are not an additional text-token bill for image generation. Image size, retention requirements and the number of revisions still matter when comparing the operating cost of an accepted asset.
Use a fixed set of briefs that includes text inside an image, a recognizable product and an edit that must preserve the surrounding composition. Inspect the actual pixels at delivery resolution. Count approved assets after corrections, and record every rejected generation and revision separately from the final selection.
Record the exact model identifier, deployment route and request settings with each result. Count complete runs, including rejected outputs, retries and tool calls. When the workflow creates files or code, open the files or execute the existing checks before accepting the result. A plausible first response can hide an incomplete final deliverable.
The specifications describe supported interfaces and request capacity. They do not establish a measured quality ranking or a latency guarantee for your account. SyncDev has not independently benchmarked this model. Choose a deployment after testing the languages, input formats and operating conditions that matter to your users.
Explore meta models, browse model tasks, or compare two models with the same acceptance criteria.
Compare the model on the work you need to complete.
Muse Image 1.0 is listed for Image-text-to-image, Text-to-image. It accepts text, image and produces image. Use the publisher’s input format and preprocessing for your chosen deployment.
Choose the output size and quality your design needs. Include drafts and revisions when budgeting an accepted image.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Alibaba
Qwen-Image-2.1-Turbo creates and edits images, including reference-guided and transparency workflows; hosted billing is separate from local deployment.
Alibaba
Qwen-Image-2.1-Pro creates and edits images, including reference-guided and transparency workflows; hosted billing is separate from local deployment.
krea
Krea 2 Raw: a base checkpoint for further training, not the recommended everyday inference release.
krea
Krea 2 Turbo: the post-trained and distilled Krea 2 checkpoint for image generation.
OpenAI
GPT-5.4 Image 2 from OpenAI: Image model for prompt-driven generation, editing, and visual design workflows.
Qwen
Qwen/Qwen-Image is a source-linked Hugging Face repository assigned to Text-to-image. Its recorded task interface accepts text and produces image.