# Muse Spark 1.1 Usage, Cost & Rank | OpenCode Data

> Muse Spark 1.1 costs $1.25 per 1M input tokens and $4.25 per 1M output tokens.

Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

- Page: https://opencode.ai/data/meta/muse-spark-1-1
- JSON: https://opencode.ai/data/meta/muse-spark-1-1.json
- Model ID: meta/muse-spark-1.1
- Lab: Meta

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-04-08 |
| Input modalities | text, image, pdf, video |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | No |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $1.25 | $4.25 | $0.15 | $1.25 |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| SWE-Bench Pro | 61.5 | resolve rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| Terminal-Bench | 80 | success rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| DeepSWE | 53.3 | resolve rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| MCP Atlas | 88.1 | success rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| JobBench | 54.7 | success rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| Toolathlon-Verified | 75.6 | success rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| Humanity's Last Exam | 62.1 | accuracy | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| OSWorld-Verified | 80.8 | success rate | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| Finance Agent | 57.2 | accuracy | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| CharXiv Reasoning | 88.4 | accuracy | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |
| BabyVision | 76.3 | accuracy | https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/ |

## 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.