BenchLM profiles Muse Spark 1.3 as a proprietary reasoning model released on September 2, 2026, with an API model ID of "muse-spark-1.3", a 1M-token context window, multimodal inputs (text, image, video, document), and text output, attributing these specs to a "Meta Muse Spark 1.3 model page." The page is explicitly la For pricing, BenchLM lists $1.25 per million input tokens and $4.25 per million output tokens, with $0.15 cached input and a $2.75 blended figure, all sourced from upstream tracking pages rather than a Meta rate card — consistent with the third-party-sourced pricing flagged in the aireleasetracker excerpt. Fields such

Meta's Muse Spark 1.3 (model ID meta/muse-spark-1.3) was released on OpenRouter on September 2, 2026, as a multimodal reasoning model targeting long-running agentic, multi-agent, and coding workflows. It supports text, image, video, and document inputs with text output, though audio understanding is flagged as not full OpenRouter routes all requests directly to Meta with no routing decisions. Measured performance shows 91 tokens per second P50 throughput, 2.65s P50 latency, and 100.00% uptime. The model scores 61.4% on the AutoExacto Bench (vgi bench 02823add), with an average tool call error rate of 1.42% and a structured output err

Meta released Muse Spark 1.3 on Wednesday, September 2, 2026, as an update that the company says significantly improves performance in coding and agentic tasks. In an interview with Axios, Meta AI chief Alexandr Wang described the model as "very competitive with frontier models" and tied the improvements to upcoming Me For developers, the practical signal in this Axios article is the explicit Meta-attributed framing of Muse Spark 1.3 as a coding- and agentic-focused step, with Wang directly linking the release to Mark Zuckerberg's stated personal-agent ambitions. No API identifiers, pricing, context window, or benchmark figures appea

Muse Spark 1.3 was released by Meta on Wednesday, September 2, 2026, arriving 23 days after Muse Glimmer, and is documented on aireleasetracker.com as a proprietary Meta model with a 1M-token context window. The supplied page exposes benchmark coverage across DeepSWE 1.1, SWEAtlas CodeBase QnA, JobBench, DeepSearchQA, For practical evaluation, aireleasetracker also surfaces a pricing row of $1.25 per million input tokens and $4.25 per million output tokens, but it flags these rates as fetched from openrouter.ai on September 3, 2026 rather than from Meta's own list price, and the provider cell in the excerpt references Meta as the en