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meta의 파비콘

Meta: Muse Image

meta/muse-image

벤치마크비교

Muse Image is an agentic image generation model from Meta that generates and edits images from text and reference images. Unlike single-pass image models, it reasons before it renders, breaking down multi-part prompts and refining its output within the chain of thought, and invokes web search for factual accuracy on knowledge-intensive prompts.

The model supports text-to-image generation, targeted image editing, multi-image composition, reference-image conditioning for style and subject consistency across a series, and precise text rendering within generated images. Iterative editing works by passing the previous output image back with a new instruction.

모달리티
가격
US$0.01/image
컨텍스트
66K
출시일
2026년 8월 26일
벤치마크비교
플레이그라운드벤치마크제공업체요금성능가동 시간앱활동자주 묻는 질문탐색

Example outputs summary for Meta: Muse Image

  • 텍스트를 이미지로 변환: Editorial architectural photograph of a contemporary neighborhood storefront at blue hour, glowing warmly against the dusk. Above the entrance, a warm-white neon sign in flowing hand-bent cursive reads exactly "OpenRouter", the only text in the scene. Large windows reveal a cozy, lived-in interior: wood shelves styled with books and ceramics, lush trailing plants, and soft pendant lighting. Potted plants and a bicycle rest by the entrance, and golden light spills across the wet pavement in gentle reflections. Straight-on composition, realistic materials, quiet street with no people. — Meta: Muse Image으로 생성
  • 텍스트를 이미지로 변환 2: Editorial architectural photograph of a contemporary neighborhood storefront at blue hour, glowing warmly against the dusk. Above the entrance, a warm-white neon sign in flowing hand-bent cursive reads exactly "OpenRouter", the only text in the scene. Large windows reveal a cozy, lived-in interior: wood shelves styled with books and ceramics, lush trailing plants, and soft pendant lighting. Potted plants and a bicycle rest by the entrance, and golden light spills across the wet pavement in gentle reflections. Straight-on composition, realistic materials, quiet street with no people. — Meta: Muse Image으로 생성
  • 텍스트를 이미지로 변환 3: Editorial architectural photograph of a contemporary neighborhood storefront at blue hour, glowing warmly against the dusk. Above the entrance, a warm-white neon sign in flowing hand-bent cursive reads exactly "OpenRouter", the only text in the scene. Large windows reveal a cozy, lived-in interior: wood shelves styled with books and ceramics, lush trailing plants, and soft pendant lighting. Potted plants and a bicycle rest by the entrance, and golden light spills across the wet pavement in gentle reflections. Straight-on composition, realistic materials, quiet street with no people. — Meta: Muse Image으로 생성

플레이그라운드

벤치마크

표준화된 평가 점수와 공통 벤치마크 프롬프트에 대한 이 모델 자체의 출력입니다. 백분율이 높을수록 좋으며, 각 순위는 해당 출처에서 평가한 모델 중 이 모델의 위치를 보여 줍니다.

Benchmark outputs summary for Meta: Muse Image

  • Full wine glass: A wine glass filled to the brim with white wine, the surface level with the rim of the glass, standing upright on a plain wooden table. Straight-on eye-level shot, glass upright and not tilted, no other objects in the frame. (이 프롬프트에서 60개 중 공동 10위위)
  • Closed umbrellas: Wide street-level photograph of about twelve people crossing a city street in heavy rain. Every person is carrying a closed, furled umbrella held down at their side. Not a single umbrella is open. Each umbrella is clearly visible. They are all getting soaked. No other people in the frame. (이 프롬프트에서 60개 중 공동 17위위)
  • Three fingers: Close straight-on photograph of an open right hand raised palm towards the camera, with exactly three fingers extended straight up and the other two folded down into the palm. Plain background, nothing else in the frame. (이 프롬프트에서 60개 중 공동 1위위)
  • Cards & dice: Exactly four playing cards face up in a single straight row on a polished marble counter, evenly spaced, all the same size, showing the seven of hearts, the eight of spades, the nine of diamonds and the ten of clubs from left to right. No other objects in the frame. Straight-on eye-level shot, soft even light. (이 프롬프트에서 60개 중 공동 1위위)
  • Long exact string: A white A4 poster on a brick wall reading exactly "SPRING CLEARANCE - EVERYTHING 40% OFF - SATURDAY 14 JUNE, 9AM TO 6PM". No other text in the frame. (이 프롬프트에서 60개 중 공동 1위위)
  • Multiple languages: Straight-on photograph of a white overhead directional sign in an international airport terminal, with a black arrow pointing right at the left end. The sign carries the same word in four scripts on four stacked lines, in this order from top to bottom: "DEPARTURES", then the Japanese 出発, then the Korean 출발, then the Arabic المغادرة. Clean sans-serif lettering, sharp focus, no other text anywhere in the frame. (이 프롬프트에서 60개 중 공동 1위위)
  • Occlusion: Straight-on eye-level photograph of a plain wooden desk. On the left, a closed hardcover book with a dark red cover stands upright, its front cover facing the camera. A blue ceramic mug stands on the desk to the right of the book and clearly further back, about 20 cm behind it, positioned so that the right edge of the book overlaps and hides the left third of the mug. The mug's handle points to the right and the whole rim of the mug is visible. Bare desk surface is visible between the book and the mug. Nothing else on the desk. Plain wall behind, soft even light. (이 프롬프트에서 60개 중 공동 1위위)
  • Mirror: Straight-on eye-level photograph of a bathroom vanity. A large rectangular mirror covers the wall behind the counter and its bottom edge meets the countertop, so both the objects and their reflections are visible. On the counter, a tall red toothbrush cup on the left and a tall green glass bottle on the right, both roughly 20 cm tall and well apart from each other. The mirror reflects the counter and the objects on it accurately. Nothing else in the frame. (이 프롬프트에서 60개 중 공동 2위위)
  • Plain zebra: Photograph of a zebra standing side-on in short grass. Its coat is a uniform solid grey with no stripes, no bands and no markings of any kind anywhere on its body. The whole animal is visible. Sharp focus. (이 프롬프트에서 60개 중 공동 19위위)
  • Times Square: Wide street-level photograph of Times Square in New York at night, wet asphalt reflecting the lights. Every billboard, screen, storefront window, awning, taxi door and road marking is completely blank — lit with plain colour fields and soft gradients only. No letters, no words, no numbers, no logos, no lettering of any kind anywhere in the frame. Sharp focus, straight-on shot. (이 프롬프트에서 60개 중 공동 11위위)
  • Minimal diff: From the reference photo, change only the sign text to "ROUTE 7 ESPRESSO - OPEN TILL 8". Keep the sign, wall, lighting and framing identical. (이 프롬프트에서 60개 중 공동 5위위)
  • Object removal: Remove only the second cup from the left in the reference photo. Leave the other four cups exactly as they are, in the same positions, and leave the shelf, towel, wall, plant and lighting untouched. (이 프롬프트에서 60개 중 공동 1위위)
  • Person removal: In the reference photo, remove only the person in the yellow coat. Their shadow on the pavement and their reflection in the shop window must be removed as well. Reconstruct the pavement and the window glass behind them. The person in the red coat and the person in the green coat must stay completely unchanged, including their own shadows and their own reflections. Change nothing else in the frame. (이 프롬프트에서 60개 중 공동 1위위)
  • Product placement: Product photograph of the ceramic mug from the reference image, standing upright on a café table. Show the same side of the mug, from the same side-on eye-level angle as the reference, with the handle on the right. The shape, proportions, colours, moulded relief and every painted detail must match the reference exactly. Only the setting and lighting change: soft daylight in a café interior, shallow depth of field. (이 프롬프트에서 60개 중 공동 1위위)
  • Four references: Place the person from the portrait reference at the desk in the scene reference, wearing the rust orange canvas jacket from its reference and holding the navy blue travel mug from its reference in one hand. Keep the face, the jacket and the mug exactly as they appear in the references — same colours, same details, same proportions. Do not change the room. (이 프롬프트에서 60개 중 공동 18위위)
  • Portraits: Studio headshot of a woman in a charcoal blazer against a mid-grey seamless backdrop, lit by a large softbox slightly above and to the left with a soft fill from the right, catchlights in both eyes. An 85mm look with shallow depth of field, skin texture and flyaway hairs kept rather than smoothed.
  • People in scenes: Photograph of four cooks working the pass in a restaurant kitchen at service, plating three dishes at once, several hands in frame and some of them overlapping. One cook partly hidden behind another, steam rising into hard overhead light, each face clearly a different person.
  • Animals: Photograph of a tabby cat asleep on a sunlit windowsill, fur lit from behind so the individual hairs glow at the edges, one ear turned toward the camera. Shallow depth of field, a houseplant soft in the background, quiet morning light.
  • Landscape and nature: Photograph of an old-growth temperate rainforest after rain, moss on every trunk and fallen log, ferns dense across the floor, thin fog between the far trees. Overcast light with no direct sun, everything from the nearest fern to the last trunk in focus.

제공업체

이 모델은 하나의 제공업체에서 호스팅합니다. OpenRouter는 모든 요청을 해당 제공업체로 직접 전달하므로 라우팅 결정을 내릴 필요가 없습니다.

요금

고객이 이 모델에 실제로 지불하는 평균 가격을 제공업체가 게시한 가격과 나란히 표시합니다. 캐싱과 할인으로 인해 실제 지불 가격은 정가보다 훨씬 낮은 경우가 많습니다.

성능

처리량은 모델의 출력 속도입니다(초당 토큰 수 — 높을수록 좋습니다). 지연 시간은 전체 왕복 시간입니다(낮을수록 좋습니다). TTFT는 첫 토큰까지 걸리는 시간, 즉 출력이 나타나기까지 걸리는 시간입니다(낮을수록 좋습니다).

가동 시간

가동 시간은 지난 3일 동안 하나 이상의 제공업체가 요청에 응답한 시간의 비율입니다. 가용성은 추론이 성공적으로 제공된 시간의 비율입니다. OpenRouter는 지속적으로 모니터링하며 제공업체가 오류를 반환하면 차선의 제공업체를 사용합니다.

앱

이 모델에 가장 많은 트래픽을 보내는 공개 앱입니다. 실제 프로덕션 워크로드의 모습을 보여주는 유용한 지표이자 이 모델이 어떤 사용 사례에 가장 적합한지 알려주는 단서입니다.

활동

시간에 따른 이 모델의 토큰 사용량 및 요청 트래픽.

API

이 모델을 호출하는 데 바로 사용할 수 있는 코드입니다. OpenRouter의 API는 OpenAI와 호환되므로 대부분의 SDK는 기본 URL만 바꾸면 작동합니다. 모델 간에 달라지는 것은 아래 모델 슬러그뿐입니다.

현재 EU 역내 라우팅을 통해 사용할 수 없습니다. 리전 내 라우팅 문서

더 많은 모델 탐색

비전 기능을 갖춘 AI 모델: 이미지 이해를 위한 멀티모달 LLM컬렉션이미지 생성 모델컬렉션이미지 모델 순위순위
종단 간 지연 시간
US$0.0119.36s
99.97%
종단 간 지연 시간
19.36초

P50, 최상의 제공업체

가동 시간 (3일)모델에 연결할 수 있었습니다. 요청이 제공업체로 라우팅되었습니다.
99.99%
가용성(3일)모델이 제공업체 중 한 곳에서 추론 결과를 반환했습니다. 오류와 빈 응답은 실패로 집계됩니다.
99.08%

지난 3일간 가용성

최근 72시간
가용성 99.08%
3일 전2일 전어제현재

지난 24시간 동안의 가용성

OpenRouter Availability
98.97%

업스트림 제공업체에서 오류가 발생하면 요청 필터가 허용하는 경우 정상 상태인 다른 제공업체로 라우팅하여 복구할 수 있습니다. 제공업체별 가동 시간 데이터에 프로그래밍 방식으로 액세스할 때 사용하는 API는 엔드포인트 API입니다. 자세히 알아볼 수 있는 관련 옵션은 부하 분산 및 제공업체 라우팅 옵션입니다.

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60.7M토큰

자주 묻는 질문

Muse Image is an agentic image generation model from Meta that generates and edits images from text and reference images. Unlike single-pass image models, it reasons before it renders, breaking down multi-part prompts and refining its output within the chain of thought, and invokes web search for factual accuracy on knowledge-intensive prompts.

Muse Image의 비용은 US$0.01/image입니다.

Muse Image은(는) 텍스트 및 이미지을(를) 입력으로 받아 이미지을(를) 반환합니다.

Muse Image은 2026년 8월 26일에 출시되었습니다.

다음 제작사의 다른 모델: Meta

Muse Spark Open

Muse Spark Open is a coding-optimized open-weight model. Trained specifically for agentic coding workflows, it delivers higher first-attempt accuracy, reliable tool calling, and deep codebase understanding across the repos developers actually work in.

텍스트1.0M 컨텍스트US$1.25 / US$4.25
Muse Wonder

Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context window.

The model handles long-horizon work: it plans, delegates to sub-agents, calls tools in parallel, and recovers from errors, so multi-step tasks run to completion without step-by-step direction. It can also operate browsers and desktop and mobile applications, and works across many files in existing repositories to make and verify changes. Muse Wonder carries important context across conversations and retrieves the details relevant to the task at hand. And it's trained for Active Focus, so it works toward your goal even when a task gets challenging.

In Meta's testing, it performs well on complex engineering tasks, extended agentic sessions, and multi-file refactors to tackle developers' most demanding work.

텍스트1.0M 컨텍스트US$4 / US$20
Muse Voice Transcribe 1.0

Muse Voice Transcribe 1.0 is a synchronous speech-to-text model from Meta. It is suited for push-to-talk, endpointing, and speaker-aware transcription, with keyword biasing for domain terms and language biasing through language-name hints. It accepts mono 16-bit PCM WAV audio at 16 kHz or 24 kHz for recordings up to 10 minutes. It does not provide word-level timestamps or confidence scores, and other audio formats must be converted to WAV before upload.

음성 전사US$0.00005/second
Muse Spark 1.3 Contributor

Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information across extended tasks, work through conflicting inputs, and request clarification or confirmation when needed. Prompts and outputs may be used to improve Meta’s products.

텍스트1.0M 컨텍스트US$0.10 / US$0.20
Muse Spark 1.3

Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through conflicting inputs, and request clarification or confirmation when needed, with an emphasis on concise execution.

텍스트1.0M 컨텍스트US$1.25 / US$4.25
Muse Spark 1.2 Contributor

Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost. It’s meaningfully cheaper than Muse Spark 1.2. Your prompts and outputs may be used to improve Meta’s products, making it ideal for experimentation, learning, and early-stage projects without worry about spend.

It is a reasoning model from Meta, tailored for complex agentic tasks. It accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context window. The model is built to support multi-agent workflows, whether as either a main agent that plans and delegates or as a subagent executing in parallel. It works across multiple coding harnesses and supports structured output, parallel function calling, and configurable reasoning effort. In Meta’s testing, it performs well on multi-file refactors, extended debugging sessions, whole-repository generation, and tasks that stretch well past a single prompt.

텍스트1.0M 컨텍스트US$0.10 / US$0.20
Muse Glimmer 30B

Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon agentic and coding workflows, with multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across more than 100 languages.

텍스트131K 컨텍스트US$0.30 / US$1.20
Muse Spark 1.2

Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks. It accepts text, images, video, and PDF documents, returns text, and offers a 1M-token context window.

The model is built to support multi-agent workflows, whether as either a main agent that plans and delegates or as a subagent executing in parallel. It works across multiple coding harnesses and supports structured output, parallel function calling, and configurable reasoning effort. In Meta’s testing, it performs well on multi-file refactors, extended debugging sessions, whole-repository generation, and tasks that stretch well past a single prompt.

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Muse Spark 1.1

Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, and PDF documents and returns text, with a 1M-token context window.

The model is designed to orchestrate multi-agent workflows, acting as either a main agent that plans and delegates or as a subagent, and generalizes zero-shot to new tools, MCP servers, and custom skills. It supports structured output, parallel function calling, built-in search with citations, and configurable reasoning effort. Meta reports strong performance on real-world coding across large codebases, computer-use workflows, and visual-to-code generation.

텍스트1.0M 컨텍스트US$1.25 / US$4.25