# Muse Wonder Usage, Cost & Rank | OpenCode Data

> Muse Wonder costs $4.00 per 1M input tokens and $20.00 per 1M output tokens.

Muse Wonder is a frontier multimodal reasoning model from Meta for personal and professional agents, long-horizon coding, and computer use. It combines Active Memory, which carries relevant context across conversations, with Active Focus, which keeps it working through blockers toward a goal.

- Page: https://opencode.ai/data/meta/muse-wonder-1-0
- JSON: https://opencode.ai/data/meta/muse-wonder-1-0.json
- Model ID: meta/muse-wonder-1.0
- Lab: Meta

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-10-12 |
| Input modalities | text, image, video, pdf, audio |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | No |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $4.00 | $20.00 | $0.20 | - |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| Harvey Legal Agent Benchmark | 10 | all-pass rate | https://research.meta.ai/static/muse-wonder-model-card |
| Finance Agent | 56.6 | accuracy | https://research.meta.ai/static/muse-wonder-model-card |
| HealthBench Professional | 60.5 | length-normalized weighted rubric score | https://research.meta.ai/static/muse-wonder-model-card |
| AutomationBench | 39.1 | strict pass@1 | https://research.meta.ai/static/muse-wonder-model-card |
| GDP.pdf | 30.8 | score | https://research.meta.ai/static/muse-wonder-model-card |
| JobBench | 70.5 | mean reward | https://research.meta.ai/static/muse-wonder-model-card |
| OSWorld | 72.6 | mean partial score | https://research.meta.ai/static/muse-wonder-model-card |
| OSWorld | 34.3 | binary completion rate | https://research.meta.ai/static/muse-wonder-model-card |
| Terminal-Bench | 52 | accuracy | https://research.meta.ai/static/muse-wonder-model-card |
| DeepSWE | 78.8 | pass@1 | https://research.meta.ai/static/muse-wonder-model-card |
| SWE-Marathon | 53.1 | pass@1 | https://research.meta.ai/static/muse-wonder-model-card |
| ProgramBench | 50 | share of rollouts passing at least 95% of tests | https://research.meta.ai/static/muse-wonder-model-card |
| Artificial Analysis Intelligence Index | 52 | index score | https://research.meta.ai/static/muse-wonder-model-card |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.