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Meta AI (FAIR)
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Introducing Muse Spark 1.1

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Muse Spark 1.1 has been introduced, featuring an updated meta model API. This release likely brings improvements and new features for users of the Muse Spark platform.

SynthePulse Insight · AI deep reading

Meta Enters with Low-Cost API, Adding New Variables to AI Price War

Version 2 · 7 sources

Meta releases Muse Spark 1.1 and opens its API for the first time, pricing far below OpenAI and Anthropic, but actual impact is constrained by token efficiency and production performance.

  • Meta released Muse Spark 1.1 on July 9, 2026, and opened it to developers for the first time via the Meta Model API.
  • API pricing is $4.25 per million output tokens, significantly lower than OpenAI and Anthropic's $25-50 range.
  • Muse Spark 1.1 supports a 1 million token context window and features multi-agent orchestration capabilities.
  • Meta claims Muse Spark 1.1 leads competitors on MCP Atlas and Humanity's Last Exam benchmarks, but scores lower than Opus 4.8 on SWE-Bench Pro.
  • The model is not open-sourced, continuing a closed-source strategy, and the API is currently limited to US developers.
Open section navigationRelease and Positioning

Release and Positioning

On July 9, 2026, Meta officially released Muse Spark 1.1, an upgraded version following Muse Spark in April, which Meta calls a "major upgrade." The model is designed for agentic tasks, coding, and computer use, and is now available in public preview to developers via the Meta Model API for the first time.

Muse Spark 1.1 supports a 1 million token context window and can actively manage context, including memory, retrieval, and information compression. Meta states it is trained to orchestrate multi-agent systems, acting as a lead agent that can plan and delegate tasks to parallel sub-agents, and as a sub-agent that can follow tasks and escalate when appropriate.

Pricing Strategy and Market Impact

The Meta Model API is priced at $1.25 per million input tokens, $4.25 per million output tokens, and $0.15 per million cached input tokens. This price is significantly lower than OpenAI's GPT-5.5 and Anthropic's Opus 4.8 (which range from $25 to $50 per million output tokens), and even lower than xAI's newly released Grok 4.5.

The Decoder analysis suggests that this aggressive pricing could pressure pure AI labs like OpenAI and Anthropic, as they rely on high profit margins to cover losses and support valuations. Tech giants like Meta and Google can treat the API as an ecosystem entry point without needing short-term profitability. Meanwhile, Chinese open-source models are also pushing prices down from another direction.

Performance and Benchmarks

Meta claims Muse Spark 1.1 scores 88.1 on the MCP Atlas benchmark and 62.1 on Humanity's Last Exam, leading Opus 4.8, GPT-5.5, and Gemini 3.1 Pro. However, on SWE-Bench Pro, Opus 4.8 leads with 69.2, while Muse Spark 1.1 scores 61.5.

The Decoder notes that Meta's benchmark results still need verification in production environments, and actual token efficiency may affect total cost. In the independent VALS-AI benchmark, Muse Spark 1.1 ranks fourth overall, but jumped 36 places on the "Vibe Code Bench" compared to its predecessor.

Safety and Availability

Meta states that it conducted extensive safety evaluations under the Advanced AI Expansion Framework before deployment, and that Muse Spark 1.1 operates within safe bounds across all frontier risk categories (chemical and biological, cybersecurity, and loss of control).

The API is currently limited to US developers, and each new account comes with $20 in free credits. The model is not open-sourced, continuing a closed-source strategy, contrasting with the previous open-source approach of Llama.

Credibility boundary

This article is based on Meta's official announcements and reports from The Decoder, The Verge, and other media. Benchmark results and performance claims are primarily from Meta and have not been independently verified; pricing and availability information comes from multiple consistent reports.

Insight takeaway

Meta's entry with a low-cost API may intensify the AI price war, but its actual impact depends on whether token efficiency and production performance meet promises.