# Muse Spark 1.2 Usage, Cost & Rank | OpenCode Data

> Muse Spark 1.2 had 0% of tokens across OpenCode over the past two months. Muse Spark 1.2 costs $1.25 per 1M input tokens and $4.25 per 1M output tokens.

Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1 with improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows.

- Page: https://opencode.ai/data/meta/muse-spark-1-2
- JSON: https://opencode.ai/data/meta/muse-spark-1-2.json
- Model ID: meta/muse-spark-1.2
- Lab: Meta
- Updated: 2026-09-28T12:09:39.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-08-05 |
| 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 |
| --- | --- | --- | --- |
| $1.25 | $4.25 | $0.15 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | Unranked |
| Tokens | 30B |
| Share of all tokens | 0% |
| Change vs previous 2 months | +100% |
| Unique users | 5K |
| Completed sessions | 2,903 |
| Average tokens per session | 10M |
| Average cost per session | $0.1526 |
| Total spend | $443 |
| Input tokens served from cache | 88.2% |
| Weekly retention | 25.8% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-19 | 21B | 3.5K | 2,467 |
| 2026-08-20 | 9.2B | 1.5K | 425 |
| 2026-08-21 | 0 | 0 | 0 |
| 2026-08-22 | 0 | 0 | 0 |
| 2026-08-23 | 0 | 0 | 0 |
| 2026-08-24 | 0 | 0 | 0 |
| 2026-08-25 | 0 | 0 | 0 |
| 2026-08-26 | 0 | 0 | 0 |
| 2026-08-27 | 0 | 0 | 0 |
| 2026-08-28 | 0 | 0 | 0 |
| 2026-08-29 | 0 | 0 | 0 |
| 2026-08-30 | 0 | 0 | 0 |
| 2026-08-31 | 0 | 0 | 0 |
| 2026-09-01 | 0 | 0 | 0 |
| 2026-09-02 | 0 | 0 | 0 |
| 2026-09-03 | 0 | 0 | 0 |
| 2026-09-04 | 0 | 0 | 0 |
| 2026-09-05 | 0 | 0 | 0 |
| 2026-09-06 | 0 | 0 | 0 |
| 2026-09-07 | 0 | 0 | 0 |
| 2026-09-08 | 0 | 0 | 0 |
| 2026-09-09 | 0 | 0 | 0 |
| 2026-09-10 | 0 | 0 | 0 |
| 2026-09-11 | 0 | 0 | 0 |
| 2026-09-12 | 0 | 0 | 0 |
| 2026-09-13 | 0 | 0 | 0 |
| 2026-09-14 | 0 | 0 | 0 |
| 2026-09-15 | 0 | 0 | 0 |
| 2026-09-16 | 0 | 0 | 0 |
| 2026-09-17 | 0 | 0 | 3 |
| 2026-09-18 | 0 | 0 | 2 |
| 2026-09-19 | 0 | 0 | 4 |
| 2026-09-20 | 0 | 0 | 2 |
| 2026-09-21 | 0 | 0 | 0 |
| 2026-09-22 | 0 | 0 | 0 |
| 2026-09-23 | 0 | 0 | 0 |
| 2026-09-24 | 0 | 0 | 0 |
| 2026-09-25 | 0 | 0 | 0 |
| 2026-09-26 | 0 | 0 | 0 |
| 2026-09-27 | 0 | 0 | 0 |
| 2026-09-28 | 0 | 0 | 0 |
| 2026-09-29 | 0 | 0 | 0 |
| 2025-09-30 | 0 | 0 | 0 |
| 2025-10-01 | 0 | 0 | 0 |
| 2025-10-02 | 0 | 0 | 0 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 10B | 34.2% |
| 2 | China | 4.6B | 15.3% |
| 3 | Japan | 3.1B | 10.5% |
| 4 | Hong Kong | 1.6B | 5.5% |
| 5 | India | 1.5B | 5% |
| 6 | Singapore | 1.3B | 4.3% |
| 7 | Vietnam | 1.1B | 3.6% |
| 8 | Indonesia | 600M | 2.1% |
| 9 | Philippines | 600M | 2.1% |
| 10 | Canada | 400M | 1.5% |
| 11 | Finland | 400M | 1.2% |
| 12 | Brazil | 400M | 1.2% |
| 13 | Germany | 300M | 1.1% |
| 14 | Taiwan | 300M | 1% |
| 15 | Malaysia | 300M | 0.9% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 1 | [space-bunny](https://opencode.ai/data/unknown/space-bunny.md) | - | 57T |
| 2 | [deepseek-v4.1-flash](https://opencode.ai/data/deepseek/deepseek-v4-1-flash.md) | DeepSeek | 33T |
| 3 | [muse-spark-1.3-contributor](https://opencode.ai/data/meta/muse-spark-1-3-contributor.md) | Meta | 31T |
| 4 | [mimo-v2.6-flash](https://opencode.ai/data/xiaomi/mimo-v2-6-flash.md) | Xiaomi | 8.8T |
| 5 | [deepseek-v4-flash](https://opencode.ai/data/deepseek/deepseek-v4-flash.md) | DeepSeek | 5.7T |
| 6 | [nemotron-3-ultra](https://opencode.ai/data/nvidia/nemotron-3-ultra.md) | NVIDIA | 3.6T |
| 7 | [longcat-2.5-preview](https://opencode.ai/data/meituan/longcat-2-5-preview.md) | Meituan | 2.6T |
| 8 | [glm-5.3-flash](https://opencode.ai/data/zhipuai/glm-5-3-flash.md) | Zhipu | 2.1T |
| 9 | [mimo-v2.5](https://opencode.ai/data/xiaomi/mimo-v2-5.md) | Xiaomi | 794B |
| 10 | [muse-spark-1.2-contributor](https://opencode.ai/data/meta/muse-spark-1-2-contributor.md) | Meta | 738B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| GDPval-AA | 1615 | Elo | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| JobBench | 61.6 | mean rubric score | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| OSWorld | 47.6 | mean partial score | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| OSWorld | 17.9 | binary completion rate | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| DeepSearchQA | 85.9 | F1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| Agentic IF Index | 46.2 | score | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| AutomationBench | 38.2 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| MRCR | 66.3 | mean sequence-match ratio | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| MRCR | 55.5 | mean sequence-match ratio | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| DeepSWE | 55 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| SWE-Atlas Codebase QnA | 46.2 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| Terminal-Bench | 82.9 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| DeepSWE | 59.3 | pass@1 | https://research.meta.ai/blog/introducing-muse-code-and-muse-spark-1-2 |
| MCP Atlas | 90.3 | pass rate | https://research.meta.ai/blog/introducing-muse-code-and-muse-spark-1-2 |

## 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.