# Muse Spark 1.3 Contributor Usage, Cost & Rank | OpenCode Data

> Muse Spark 1.3 Contributor ranked #3 by tokens across OpenCode last week, with 16% of tokens over the past two months. Muse Spark 1.3 Contributor costs $0.10 per 1M input tokens and $0.20 per 1M output tokens.

Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It improves long-horizon agent collaboration, instruction following, and coding efficiency relative to Muse Spark 1.2.

- Page: https://opencode.ai/data/meta/muse-spark-1-3-contributor
- JSON: https://opencode.ai/data/meta/muse-spark-1-3-contributor.json
- Model ID: meta/muse-spark-1.3-contributor
- Lab: Meta
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-09-02 |
| 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 | #3 |
| Tokens | 148T |
| Share of all tokens | 16% |
| Change vs previous 2 months | +100% |
| Unique users | 979K |
| Completed sessions | 324,839,117 |
| Average tokens per session | 456K |
| Average cost per session | $0.0008 |
| Total spend | $271,756 |
| Input tokens served from cache | 94.9% |
| Weekly retention | 67.5% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-09-02 | 117B | 2.6K | 17,199 |
| 2026-09-03 | 3.3T | 25K | 456,573 |
| 2026-09-04 | 4.4T | 29K | 2,583,192 |
| 2026-09-05 | 4.6T | 27K | 4,478,518 |
| 2026-09-06 | 4.6T | 28K | 6,788,255 |
| 2026-09-07 | 4.5T | 33K | 9,414,366 |
| 2026-09-08 | 4.6T | 35K | 9,960,470 |
| 2026-09-09 | 5.7T | 34K | 9,412,298 |
| 2026-09-10 | 5.9T | 35K | 9,606,328 |
| 2026-09-11 | 5.2T | 34K | 6,147,109 |
| 2026-09-12 | 5.5T | 31K | 5,202,045 |
| 2026-09-13 | 4.4T | 31K | 10,511,699 |
| 2026-09-14 | 5T | 38K | 6,231,805 |
| 2026-09-15 | 5.1T | 38K | 12,056,049 |
| 2026-09-16 | 5.3T | 38K | 13,970,453 |
| 2026-09-17 | 5.9T | 34K | 5,908,025 |
| 2026-09-18 | 5.9T | 36K | 8,939,007 |
| 2026-09-19 | 4.7T | 35K | 11,563,793 |
| 2026-09-20 | 4.8T | 29K | 10,516,006 |
| 2026-09-21 | 5.4T | 38K | 9,431,667 |
| 2026-09-22 | 5.7T | 36K | 16,229,461 |
| 2026-09-23 | 5.8T | 35K | 17,122,407 |
| 2026-09-24 | 5.4T | 34K | 16,780,329 |
| 2026-09-25 | 5.1T | 32K | 16,755,972 |
| 2026-09-26 | 5.1T | 29K | 16,849,754 |
| 2026-09-27 | 5.1T | 28K | 16,394,990 |
| 2026-09-28 | 5.4T | 34K | 16,458,135 |
| 2026-09-29 | 5.3T | 36K | 15,187,655 |
| 2026-09-30 | 4.8T | 34K | 16,228,824 |
| 2026-10-01 | 4.5T | 33K | 17,225,392 |
| 2026-10-02 | 1.2T | 18K | 6,411,341 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 22T | 14.9% |
| 2 | India | 13T | 9% |
| 3 | Germany | 7.8T | 5.3% |
| 4 | Brazil | 7.7T | 5.2% |
| 5 | Indonesia | 6.4T | 4.3% |
| 6 | Japan | 5.2T | 3.5% |
| 7 | Vietnam | 4.3T | 2.9% |
| 8 | Türkiye | 3.9T | 2.6% |
| 9 | Netherlands | 3.9T | 2.6% |
| 10 | Singapore | 3.8T | 2.6% |
| 11 | France | 3.5T | 2.4% |
| 12 | Egypt | 3.3T | 2.2% |
| 13 | Spain | 3.2T | 2.2% |
| 14 | United Kingdom | 3T | 2.1% |
| 15 | Canada | 2.8T | 1.9% |

## 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 | 1754 | Elo | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| JobBench | 64.9 | mean rubric score | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| OSWorld | 66.9 | mean partial score | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| OSWorld | 32 | binary completion rate | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| DeepSearchQA | 90.3 | F1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| Agentic IF Index | 57.8 | score | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| AutomationBench | 49.6 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| MRCR | 98.5 | mean sequence-match ratio | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| MRCR | 98.1 | mean sequence-match ratio | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| DeepSWE | 75.4 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| SWE-Atlas Codebase QnA | 59.4 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |
| Terminal-Bench | 88.8 | pass@1 | https://research.meta.ai/blog/introducing-muse-spark-1-3 |

## 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.