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Model details

Muse Spark 1.3

Meta introduced Muse Spark 1.3 in early September 2026 as part of ongoing work on personal agent capabilities, according to Axios coverage published on the same day. The model is tracked on Artificial Analysis as a proprietary release from Meta, sitting within a comparison hub that breaks the family into tiered variants. That aggregator page lists a "max" variant with an Intelligence Index score of 53 and an "xhigh" variant scoring 52, giving prospective users a sense of how the tiers trade reasoning depth against cost and throughput. The framing in coverage around personal agents suggests Meta is positioning Muse Spark 1.3 for interactive, multi-step assistant experiences rather than purely batch generative tasks.

From a practical standpoint, Muse Spark 1.3 fits users who want a Meta-originated model with documented tiering and external benchmarking visibility, making it easier to weigh it against other proprietary options on intelligence, output speed, and per-task cost. The presence of distinct max and xhigh variants indicates flexibility for callers that need to balance capability and budget, while continued coverage tied to personal agent development points to forward momentum in agent-style use cases. Teams evaluating the model for production assistants or agent pipelines can lean on the Artificial Analysis comparison pages for ongoing intelligence and speed tracking as the ecosystem matures.

OpenCodemuse-spark-1.3muse

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OpenCode
Model key
muse-spark-1.3
Release date
Sep 2, 2026
Last updated
Sep 2, 2026
AI SDK package
@ai-sdk/openai
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.25
Output token cost
$4.25

Limits

Output tokens
131,072 tokens
Context window
1,048,576 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Muse Spark 1.3

OpenCode Zen

Coverage

Muse Spark 1.3 shipped on September 2, 2026, with the headline technical story being efficiency rather than benchmark rank: Meta engineers report the model needs roughly 20% fewer tool calls and about 25% fewer tokens than its predecessor Muse Spark 1.2 to complete equivalent agentic coding work. The piece emphasizes t Beyond the efficiency claims, Muse Spark 1.3 is described as a proprietary multimodal reasoning model built for long-horizon coding tasks involving multi-step planning, tool invocation, self-checking, and sustained execution. The article confirms availability through Muse Code, Meta's coding agent environment, and the

OpenCode Zen

CoverageBenchmark

Meta released Muse Spark 1.3 on September 2, 2026, from Meta Superintelligence Labs under Chief AI Officer Alexandr Wang, and the model is described as the company's most consequential update of the year for AI agent builders. Reported benchmarks include 75.4% on DeepSWE 1.1 for end-to-end agentic software engineering, Beyond raw benchmark scores, Muse Spark 1.3 is reported to finish equivalent agentic coding work with roughly 20% fewer tool calls and 25% fewer tokens than Muse Spark 1.2, per Meta engineers' internal comparisons, a distinction the article flags because the efficiency gains are vendor-reported rather than independentl

OpenCode Zen

CoverageBenchmark

AI Release Tracker logged Muse Spark 1.3 as released by Meta on September 2, 2026, arriving 23 days after Muse Glimmer and shipping with a 1M token context window. The entry lists third-party benchmark scores that independently confirm Meta's headline numbers: 75.4% on DeepSWE 1.1 (ranked 1, described as the best publi The page attributes the model to Meta and lists pricing at $1.25 per 1M input tokens and $4.25 per 1M output tokens, with a footnote noting rates were fetched from openrouter.ai on September 21, 2026 rather than from Meta's own list. The release window is recorded in clearly dated metadata, and benchmark sources are ci

OpenCode Zen

CoverageBenchmark

Artificial Analysis published an independent evaluation of Meta's Muse Spark 1.3 (xhigh) reasoning variant, placing it at 45 on the Artificial Analysis Intelligence Index (rank 25 of 212) and rating it above average among comparable models. The page documents real-world performance characteristics, scoring 237 tokens p Technical specifications confirmed on the page include multimodal support (text, image, and video input with text output), a 1M token context window, and an explicit reasoning-capable variant designation. The evaluation cost $1,655.27 to run on the Intelligence Index and generated 140M output tokens, more than the 88M

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