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

> Muse Spark 1.2 Contributor ranked #10 by tokens across OpenCode last week, with 4.3% of tokens over the past two months. Muse Spark 1.2 Contributor costs $0.10 per 1M input tokens and $0.20 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-contributor
- JSON: https://opencode.ai/data/meta/muse-spark-1-2-contributor.json
- Model ID: meta/muse-spark-1.2-contributor
- Lab: Meta
- Updated: 2026-10-02T08:39:37.000Z

## 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 |
| --- | --- | --- | --- |
| $0.10 | $0.20 | $0.0020 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #10 |
| Tokens | 39T |
| Share of all tokens | 4.3% |
| Change vs previous 2 months | +100% |
| Unique users | 456K |
| Completed sessions | 309,142,864 |
| Average tokens per session | 125K |
| Average cost per session | $0.0003 |
| Total spend | $99,523 |
| Input tokens served from cache | 91.5% |
| Weekly retention | 79.4% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-19 | 193B | 6.7K | 24,920 |
| 2026-08-20 | 916B | 16K | 173,731 |
| 2026-08-21 | 1.5T | 23K | 408,317 |
| 2026-08-22 | 1.2T | 17K | 404,301 |
| 2026-08-23 | 1.3T | 16K | 414,088 |
| 2026-08-24 | 1.4T | 16K | 174,823 |
| 2026-08-25 | 1.4T | 16K | 199,080 |
| 2026-08-26 | 1.7T | 20K | 252,829 |
| 2026-08-27 | 2.3T | 24K | 353,191 |
| 2026-08-28 | 2.1T | 22K | 353,833 |
| 2026-08-29 | 1.8T | 18K | 408,673 |
| 2026-08-30 | 1.9T | 19K | 517,228 |
| 2026-08-31 | 2.1T | 22K | 569,187 |
| 2026-09-01 | 2.3T | 22K | 545,644 |
| 2026-09-02 | 2.4T | 22K | 492,559 |
| 2026-09-03 | 1.5T | 18K | 396,726 |
| 2026-09-04 | 1.1T | 13K | 1,981,639 |
| 2026-09-05 | 767B | 9.3K | 3,911,207 |
| 2026-09-06 | 693B | 8.5K | 6,562,567 |
| 2026-09-07 | 762B | 9.9K | 9,552,118 |
| 2026-09-08 | 800B | 9.5K | 9,446,762 |
| 2026-09-09 | 700B | 8.6K | 8,856,025 |
| 2026-09-10 | 633B | 7.8K | 8,254,523 |
| 2026-09-11 | 494B | 6.9K | 4,695,213 |
| 2026-09-12 | 472B | 5.5K | 3,847,391 |
| 2026-09-13 | 487B | 6.1K | 9,278,162 |
| 2026-09-14 | 507B | 7.1K | 4,585,673 |
| 2026-09-15 | 502B | 7K | 9,754,239 |
| 2026-09-16 | 492B | 6.7K | 12,799,114 |
| 2026-09-17 | 407B | 5.2K | 4,198,798 |
| 2026-09-18 | 412B | 5.6K | 8,607,720 |
| 2026-09-19 | 425B | 6.3K | 11,903,170 |
| 2026-09-20 | 412B | 4.9K | 12,067,010 |
| 2026-09-21 | 455B | 5.3K | 9,984,665 |
| 2026-09-22 | 471B | 5.4K | 15,272,101 |
| 2026-09-23 | 452B | 5K | 15,926,599 |
| 2026-09-24 | 447B | 4.8K | 14,837,969 |
| 2026-09-25 | 269B | 2.8K | 15,612,097 |
| 2026-09-26 | 134B | 1.4K | 16,245,575 |
| 2026-09-27 | 120B | 1.1K | 16,823,838 |
| 2026-09-28 | 125B | 1.3K | 16,598,589 |
| 2026-09-29 | 106B | 1.3K | 14,104,708 |
| 2026-09-30 | 107B | 1.1K | 15,802,587 |
| 2026-10-01 | 103B | 1.1K | 14,612,302 |
| 2026-10-02 | 42B | 529 | 7,331,373 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 6.5T | 16.7% |
| 2 | India | 4T | 10.2% |
| 3 | Indonesia | 2.2T | 5.8% |
| 4 | Brazil | 1.9T | 4.8% |
| 5 | Germany | 1.5T | 4% |
| 6 | Japan | 1.5T | 3.9% |
| 7 | Singapore | 1.1T | 3% |
| 8 | Vietnam | 998B | 2.6% |
| 9 | Türkiye | 920B | 2.4% |
| 10 | Netherlands | 883B | 2.3% |
| 11 | Egypt | 847B | 2.2% |
| 12 | France | 773B | 2% |
| 13 | Spain | 757B | 2% |
| 14 | United Kingdom | 597B | 1.5% |
| 15 | Argentina | 593B | 1.5% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 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 |
| 11 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 687B |
| 12 | [gpt-6-luna](https://opencode.ai/data/openai/gpt-6-luna.md) | OpenAI | 640B |
| 13 | [deepseek-v4-pro](https://opencode.ai/data/deepseek/deepseek-v4-pro.md) | DeepSeek | 465B |
| 14 | [mimo-v2.6-pro](https://opencode.ai/data/xiaomi/mimo-v2-6-pro.md) | Xiaomi | 464B |
| 15 | [nemotron-3.5-lightning](https://opencode.ai/data/nvidia/nemotron-3-5-lightning.md) | NVIDIA | 267B |

## 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.