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meta 的网站图标

Meta: Muse Image

meta/muse-image

基准测试比较

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.

模态
价格
US$0.01/image
上下文
66K
发布时间
2026年8月26日
基准测试比较
试验场基准测试提供商定价性能正常运行时间应用活动常见问题探索

Example outputs summary for Meta: Muse Image

  • 文生图: 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. — 使用 Meta: Muse Image 生成
  • 文生图 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. — 使用 Meta: Muse Image 生成
  • 文生图 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. — 使用 Meta: Muse Image 生成

试验场

基准测试

标准化评测得分,以及此模型针对共用基准测试提示词的实际输出。百分比越高越好,每项排名显示此模型在该来源所评测的模型中的位置。

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. (在此提示词中并列第 第 10 名,共 60 名)
  • 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. (在此提示词中并列第 第 17 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 2 名,共 60 名)
  • 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. (在此提示词中并列第 第 19 名,共 60 名)
  • 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. (在此提示词中并列第 第 11 名,共 60 名)
  • 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. (在此提示词中并列第 第 5 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 1 名,共 60 名)
  • 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. (在此提示词中并列第 第 18 名,共 60 名)
  • 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.

提供商

此模型由一家提供商托管。OpenRouter 将每个请求直接转发给该提供商,无需做出路由决策。

定价

客户为此模型实际支付的平均价格,与提供商公布的价格并列展示。由于缓存和折扣,实际支付的价格往往远低于标价。

性能

吞吐量是模型生成内容的速度(每秒 token 数,越高越好)。延迟是完整的往返时间(越低越好)。TTFT 是首个 token 的响应时间,即您看到内容出现前需要等待的时间(越低越好)。

正常运行时间

正常运行时间是指过去 3 天中至少有一家提供商响应请求的时间百分比。可用性是指成功提供推理服务的时间百分比。OpenRouter 会持续监控,并在某家提供商返回错误时使用次优提供商。

应用

向此模型发送最多流量的公开应用。这能很好地反映实际生产工作负载的情况,也能提示此模型最适合哪些用例。

活动

此模型的 token 用量和请求流量随时间的变化。

API

可直接用于调用此模型的代码。OpenRouter 的 API 与 OpenAI 兼容——大多数 SDK 只需替换基础 URL 即可使用。切换模型时,唯一需要更改的是下方的模型标识符。

目前无法通过 EU 区域内路由使用。 区域内路由文档

探索更多模型

具备视觉能力的 AI 模型:用于图像理解的多模态大语言模型集合图像生成模型集合图像模型排行榜排名
端到端延迟
US$0.0119.03s
99.97%
端到端延迟
19.03秒

P50,最佳提供商

正常运行时间(3 天)模型可访问。请求已路由至提供商。
99.99%
可用性(3 天)模型通过任意提供商返回了推理结果。错误和空响应均计为失败。
99.07%

过去 3 天的可用性

最近 72 小时
可用性 99.07%
3 天前2 天前昨天现在

过去 24 小时的可用性

OpenRouter Availability
98.97%

当上游提供商发生错误时,如果您的请求筛选条件允许,我们可以通过路由到另一家健康的提供商来恢复服务。您可以通过以下接口以编程方式访问各提供商的正常运行时间数据: 端点 API。了解更多有关我们的 负载均衡和提供商路由选项。

1.
https://www.lepetitheros.com/ 的网站图标
Le Petit H�ros
新增
510Mtoken
2.
https://playgroundai.com/ 的网站图标
Playground
新增
201Mtoken
3.
https://www.hayailearn.com/ 的网站图标
HayaiLearn
新增
118Mtoken
4.
https://word-ministries.com/ 的网站图标
Word Ministries
新增
102Mtoken
5.
https://foks.pipeline/ 的网站图标
FOKS Pipeline
新增
60.7Mtoken

常见问题

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 Image 的费用为 US$0.01/image。

Muse Image 接受 文本和图像 作为输入,并返回 图像。

Muse Image 于 2026年8月26日 发布。

更多模型,来自 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.

文本1.0M 上下文US$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.

文本1.0M 上下文US$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.

转录US$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.

文本1.0M 上下文US$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.

文本1.0M 上下文US$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.

文本1.0M 上下文US$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.

文本131K 上下文US$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.

文本1.0M 上下文US$1.25 / US$4.25
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.

文本1.0M 上下文US$1.25 / US$4.25