The company that made open weights mainstream now competes on discounts
Meta launched Muse Spark 1.2 and a coding agent called Muse Code, offering output tokens at 20 cents per million tokens. The pricing targets cost-sensitive users but requires data sharing for training.

- Meta released Muse Spark 1.2 and Muse Code, an open-weight coding agent, with output tokens priced at 20 cents per million tokens.
- The pricing model requires users to share data for training, contrasting with traditional open-weight models that avoid such requirements.
- Meta is prioritizing cost competitiveness over benchmark performance, targeting cost-sensitive developers and startups.
- Muse Code is designed to resume coding tasks after crashes, addressing reliability concerns in long-running processes.
Meta has doubled down on its strategy to make AI models more accessible by releasing Muse Spark 1.2, a new version of its open-weight model, alongside Muse Code, a coding agent designed to resume work seamlessly after crashes. The pricing model is aggressively competitive, with the cheapest tier priced at just 20 cents per million output tokens. However, this low cost comes with a trade-off: users must share their data for training purposes, raising privacy and control concerns.
The move signals Meta’s intent to compete on affordability rather than raw performance, a shift from the traditional focus on benchmark-leading models. While the pricing is attractive for cost-sensitive developers and startups, it leaves a gap in high-performance benchmarks, where other models may still hold an edge. The company’s push for open weights, models with publicly available weights, remains central to its strategy, but the data-sharing requirement introduces a new layer of complexity for users who prioritize privacy or proprietary data control.
The release of Muse Code, an agent designed to handle coding tasks and recover from interruptions, further extends Meta’s ecosystem. This agent is positioned as a practical tool for developers who need reliability in long-running tasks, though its integration with Muse Spark 1.2 will likely be a key factor in its adoption.
Developers gain access to a low-cost coding agent and model, but must weigh the data-sharing requirement against privacy needs.
Businesses can reduce AI costs but may face challenges in managing data-sharing policies and compliance.
Students and researchers can experiment with a powerful coding agent at minimal cost, though data-sharing terms may limit use in proprietary projects.
- open weights
- AI models whose weights (parameters) are publicly available, allowing users to inspect, modify, and deploy them without restrictions.
- output tokens
- The units of text generated by an AI model, often used as a pricing metric in commercial AI services.
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