Meta released Muse Spark 1.3, an agentic coding model aimed at long-horizon work with usability features like sustaining long threads and confirming consequential actions. Meta reports it used about 20% fewer tool calls and about 25% fewer tokens than Muse Spark 1.2 in internal comparisons. The model is available today in Muse Code and the Meta Model API (with closed weights), improving coding efficiency and agent run behavior while self-hosting remains unavailable and top reasoning mode stays gated.
Meta introduced contributor pricing for its Muse Spark AI coding model that discounts usage if users share their prompts and outputs for future training. For inputs, 1 million tokens drop from $1.25 to 10 cents, and outputs drop from $4.25 per million to 20 cents under the contributor tier. This changes user incentives toward providing data for evaluating and improving agentic tools while Meta compensates companies for that information.
Meta launched Muse Spark 1.3 and said it delivers its biggest improvements yet for coding and agentic tasks, with rollout through Muse Code and the Meta Model API plus open-weight releases planned. It reported 75.4% on the DeepSWE coding benchmark and 98.1% on the 512K-1M MRCR long-context test for Spark 1.3. Outside benchmarks found Spark 1.3 xhigh at 61 on an Intelligence Index at about $0.55 per task, pushing Gemini 3.8 Flash off the cost-versus-intelligence frontier and changing which models look most efficient for developers.
New York City banned student-facing generative AI for grades 2 through 8 for one year while requiring AI-literacy lessons for high schoolers and setting daily screen-time limits for younger grades. The ban covers nearly 600,000 students, with recommendations of no more than 30 minutes of daily one-to-one screen time for grades 3–5 and 45 minutes for grades 6–8. Teachers can still use approved AI for planning and administration, but students lose direct access under the new policy.
Meta rolled out Muse Spark 1.3 and evaluations placed it near the top for coding and agentic performance. Muse Spark 1.3 (xhigh) scored 61 on the Intelligence Index. Its progress mainly improves agentic/scientific reasoning and pricing, and whether open-weight 1.3 arrives could shift the open-weight rankings versus top labs.
Meta’s Muse Spark 1.3 was released, with community claims that it matches GPT-5.6-Sol on multiple evaluations and will ship open weights. The article cites a reported 90%+ pricing discount when users opt in to training. This adds an open-weight option and a much lower training cost for developers building agentic and coding workloads.
Meta released Muse Spark 1.3 as its most powerful large language model so far, aiming to match leading labs like OpenAI and Anthropic. The model scored 62 on Artificial Analysis’s Intelligence Index. Meta will start rolling it out to developers via its Model API and to Facebook, Instagram, and Meta AI in the coming days.
Every AI story that matters,
in your inbox by 8am.
TLDRocket reads all relevant sources, removes duplicate coverage, and summarises the
day in two minutes. Follow companies and topics for alerts, or get the
briefing in Slack. Free, no spam, unsubscribe anytime.
Reading TLDRocket needs no cookies, and the readership counts we rely on come from
our own cookieless analytics. Google Analytics is the exception: it sets cookies and
reports to Google, so it stays switched off until you allow it. You can change your
mind any time from “Cookie settings” in the footer.
Strictly necessary
Session security and form protection (tldrocket-session,
XSRF-TOKEN, 2 hours). The site cannot work without them,
so they need no consent.
Always on
Google Analytics 4 (_ga,
_ga_<id>, up to 2 years). Measures which
stories and sections readers use. Google acts as a third-party processor and may
store the data outside the EU. No advertising, no profiling, no data sold.
