Meta's Muse Spark 1.3 Scores 75.4 on DeepSWE, Above Claude Opus 5 and GPT-5.6 Sol

Meta Superintelligence Labs released Muse Spark 1.3 and made it the model behind Muse Code, Meta's terminal-based coding agent. On DeepSWE v1.1, a long-horizon agentic coding benchmark, Meta reports 75.4 for Muse Spark 1.3 against 74.0 for Claude Opus 5 and 73.0 for GPT-5.6 Sol. The Telegram channel AI Post carried the same ranking.

The interesting number is not the one-and-a-half point lead. It is the 20-point jump from Muse Spark 1.2, which scored 55.0. A gain that large inside one minor version usually reflects either a genuine change in post-training and reinforcement-learning recipe or a change in how the agent harness runs the benchmark, and Meta has not published enough detail to separate the two. Independent verification of the DeepSWE result is still outstanding.

Muse Spark 1.3 is a closed model. Meta lists it as proprietary with no weights release, priced at $1.25 per million input tokens and $4.25 per million output tokens on the standard endpoint, with a roughly one-million-token context and text, image and video input. A much cheaper contributor endpoint is available to developers who let Meta train on their data, which is a data-acquisition instrument dressed as a pricing tier.

Meta's own framing sets the bar it will be judged against. Mark Zuckerberg called the release the largest single jump the team has made on coding and agentic work. That is a claim about sustained multi-step task completion, which is exactly the property that degrades fastest when a model leaves a benchmark harness and meets a real repository.