How much does Muse Wonder cost?

Muse Wonder costs $4.00/M input tokens and $20.00/M output tokens, with separate rates for Cache Read at $0.20/M tokens.

What is the context length of Muse Wonder?

Muse Wonder has a 1,048,576 token context window.

Does Muse Wonder support tool calling and structured outputs?

Yes. Muse Wonder accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

What inputs and outputs does Muse Wonder support?

Muse Wonder accepts text, images, video, files such as PDFs, and audio as input and returns text.

Which providers serve Muse Wonder?

Muse Wonder is served by 2 providers on OpenRouter: Google Vertex and Meta. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.

When was Muse Wonder released?

Muse Wonder was released on October 12, 2026.

Meta: Muse Wonder

meta/muse-wonder

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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.

Modalities

In / out price

$4 / $20per 1M

Context

1.0M

Released

Oct 12, 2026

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).

Latency / throughput


	$4.00	$20.00	$0.20	1.45s	113 tok/s	99.90%
	$4.00	$20.00	$0.20	2.54s	85 tok/s	98.64%

Input $4.00Output $20.00
113 tok/s99.90% uptime
Input $4.00Output $20.00
85 tok/s98.64% uptime

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Weighted average

Input

$0.4311/M tokens

Output

$20/M tokens

Muse Wonder — Price history

Price history

No effective price data for this selection

		$0.425	$20.00	94.1%	92.1%
		$0.501	$20.00	92.1%	7.9%

Effective in $0.425Effective out $20.00
Token share
92.1%
Effective in $0.501Effective out $20.00
Token share
7.9%

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Uptime (3d)

100.00%

Availability (3d)

99.65%

Availability over the last 3 days

Last 72 hours

Availability 99.65%

3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability

99.78%

Without Routing

95.08%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for Meta: Muse Wonder (Artificial Analysis, Design Arena and Vals AI)
Source	Benchmark	Score
Artificial Analysis	Muse Wonder (Max) Intelligence Index	43.6
Artificial Analysis	Muse Wonder (Max) HLE	50.1%
Artificial Analysis	Muse Wonder (Max) AA-LCR	80.7%
Artificial Analysis	Muse Wonder (Max) GDPval-AA	63.5%
Artificial Analysis	Muse Wonder (Max) CritPt	28.3%
Artificial Analysis	Muse Wonder (Max) SciCode	60.3%
Artificial Analysis	Muse Wonder (Max) Terminal-Bench 4.0	52.0%
Artificial Analysis	Muse Wonder (Max) AA-Omniscience Accuracy	38.0%
Artificial Analysis	Muse Wonder (Max) AA-Omniscience Non-Hallucination Rate	79.1%
Design Arena	Muse Wonder (max) Agents Arena Blender3d Elo	1156
Design Arena	Muse Wonder (max) Agents Arena Codetovideo Elo	1207
Design Arena	Muse Wonder (max) Agents Arena Htmlslides Elo	1127
Design Arena	Muse Wonder (max) Agents Arena Pdf Elo	1183
Design Arena	Muse Wonder (max) Agents Arena Python-Pptxslides Elo	1029
Design Arena	Muse Wonder (max) Agents Arena Spreadsheet Elo	1248
Design Arena	Muse Wonder (max) Models Arena 3D Elo	1431
Design Arena	Muse Wonder (max) Models Arena Code Categories Elo	1392
Design Arena	Muse Wonder (max) Models Arena Data Visualization Elo	1288
Design Arena	Muse Wonder (max) Models Arena Game Development Elo	1414
Design Arena	Muse Wonder (max) Models Arena UI Component Elo	1362
Design Arena	Muse Wonder (max) Models Arena Website Elo	1378
Vals AI	Wonder Access to Justice Benchmark	73.0%
Vals AI	Wonder BioMysteryBench	67.0%
Vals AI	Wonder Code Migration v1.1	72.4%
Vals AI	Wonder CyberBench v1.1	80.4%
Vals AI	Wonder EMB v1.1	72.5%
Vals AI	Wonder Finance Agent v2.1	65.5%
Vals AI	Wonder Vals Finance Index	61.2%
Vals AI	Wonder Financial Coaching Bench	73.8%
Vals AI	Wonder HLAB-Vals-1.1	23.8%
Vals AI	Wonder Immigration	69.6%
Vals AI	Wonder IOI	66.9%
Vals AI	Wonder Vals Legal Index	75.3%
Vals AI	Wonder Legal Research Bench	50.5%
Vals AI	Wonder MysteryMechanism	27.5%
Vals AI	Wonder ProofBench v1.1	84.0%
Vals AI	Wonder Public Benefits Bench v1.1	70.2%
Vals AI	Wonder Vals Science Index	39.1%
Vals AI	Wonder SWE-bench Multimodal	45.6%
Vals AI	Wonder Tax Agent Bench	36.3%
Vals AI	Wonder Terminal-Bench 4.0	49.5%
Vals AI	Wonder Terminal-Bench-Science	22.9%
Vals AI	Wonder Vals Index	64.9%
Vals AI	Wonder Vibe Code Bench 1-100	26.6%
Vals AI	Wonder Vibe Code Bench v1.1	83.1%

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Portkey AI

Control panel for AI apps

39.9Btokens

Claude Code

Claude Code is Anthropic's agentic coding tool that reads your entire codebase, plans and executes changes across files, runs tests, and iterates on failures, all from natural language prompts.

17.9Btokens

Plato Computer Use Agent

new

15.3Btokens

pareto-autoresearch

new

11.1Btokens

Hermes Agent

Hermes Agent is an open-source, self-improving AI agent by Nous Research that runs persistently with memory across sessions, and builds reusable skills from experience. It comes with 40+ built-in tools, including web search, browser automation, and vision, plus scheduled automations and subagents.

7.6Btokens

Rest of OpenRouter

19B

Top 5 Apps

30.2M

Not currently available through EU in-region routing. In-region routing docs

More models from Meta

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Muse Ember accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context window.

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Muse Spark Open

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Transcription$0.00005/second

Muse Spark 1.3 Contributor

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Text1.0M context$0.10 / $0.20

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Muse Image

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Image$0.01/image

Muse Spark 1.2 Contributor

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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.

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Muse Glimmer 30B (batch)

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Muse Spark 1.2

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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.

Text1.0M context$1.25 / $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.

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Text1.0M context$1.25 / $4.25

Meta: Muse Wonder

Providers