Flowtivity's analysis (dated September 2, 2026, updated September 3) attributes specific benchmark results to Meta for Muse Spark 1.3: 75.4% on DeepSWE 1.1 for end-to-end agentic software engineering, 88.8% on Terminal-Bench 2.1, 59.4% on SWEAtlas CodeBase QnA, and 98.5% on long-context retrieval (MRCR), all within a 1 The post highlights agentic behaviors—asking clarifying questions, confirming before consequential actions, and being better calibrated on irreversible steps—and characterizes Muse Spark 1.3 as Meta's "first closed, directly monetised frontier line" shipping on a four-week cadence. It notes that on JobBench, OSWorld 2.

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 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

Unite.AI reports on September 2, 2026 that Meta released Muse Spark 1.3 from Meta Superintelligence Labs, with same-day availability in Muse Code and the Meta Model API. The piece confirms Meta's claim of improved performance on agentic and coding tasks and notes that previously available reasoning modes are available The coverage paraphrases Meta's agentic workflow description: the model uses tools to generate context from messy and conflicting sources, proactively corrects gaps in its plan, and tracks what it has learned; it was trained across diverse harnesses for generalization. Unite.AI also repeats the collaborative behaviors—

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

AI/TLDR's model card for Muse Spark 1.3 confirms the September 2, 2026 release and same-day rollout in Muse Code and the Meta Model API, summarizing Meta's framing of the update as an efficiency-focused improvement shaped by months of usage data. It quantifies Meta's engineer comparisons as roughly 20% fewer tool calls The profile lists a 1M-token context window with modalities of text, vision, video, and documents, and proprietary weights delivered API-only with undisclosed parameter count. Pricing on the standard Meta Model API tier is given as $1.25 per million input tokens and $4.25 per million output tokens, with cached input at

Meta AI Research announced Muse Spark 1.3 on September 2, 2026, rolling it out the same day in Muse Code and the Meta Model API. The post states the model delivers improved performance on agentic and coding tasks, drawing on "months of broad adoption of Muse Code and Meta Model API," and frames the release as advancing The official post describes Muse Spark 1.3 as designed for longer-horizon work: given an open-ended objective it uses tools to generate context from "messy and conflicting sources," proactively corrects gaps in its plan, and tracks what it has learned, having been trained across a "diverse set of harnesses" to generali