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ModelTape / Models / Muse Spark 1.3
Meta · API-only (closed weights)

Muse Spark 1.3

Muse Spark 1.3 is an API-only (closed weights) language model from Meta, released September 2, 2026. The context window is 1M tokens. On the Epoch Capabilities Index it scores 158.2, rank 26 of 256 rated models. API price: $1.25 input / $4.25 output per million tokens.

We’re excited to release Muse Spark 1.3, which delivers improved performance across agentic and coding tasks. Drawing on what we learned from months of broad adoption of Muse Code and Meta Model API, we’ve also made this model easier to use in real-world settings.

Check it against your own PC →

Lab
Meta
Released
September 2, 2026
Weights
API-only (closed weights)
Parameters
–
Context
1M
Price / M tokens
$1.25 / $4.25 in / out
Epoch ECI
158.2 #26

Strengths by category

ModelTape's 0–100 fit on Epoch AI benchmark results.

CategoryScoreBased on
Reasoning26.0Chess Puzzles, SimpleBench, Mystery Game Puzzles
Math60.3ProofBench, OTIS Mock AIME 2024-2025, FrontierMath-Tiers-1-3-v2-Private, FrontierMath-Tier-4-v2-Private
Coding61.1MirrorCode, FrontierSWE

Benchmark results

Benchmark (Epoch AI)Score
OTIS Mock AIME99.2%
FrontierMath (Tiers 1–3)78.3%
SimpleBench78.2%
ProofBench58.0%
MirrorCode54.4%
FrontierMath-Tier-4-v2-Private46.3%
Chess Puzzles33.7%
FrontierSWE25.8%
Mystery Game Puzzles17.4%

Links

Announcement · Epoch AI benchmarks

Models with a similar score

Muse Wonder · Kimi K3.1 Pro · GPT-5.6 Terra · Claude Haiku 5.5 · GPT-5.4 Pro · GPT-5.5 · Claude Opus 4.8 · DeepSeek V4.1 Flash 1017

More from Meta

Muse Ember · Muse Spark Open · Muse Wonder · Muse Wonder Full · Muse Spark 1.3 Contributor · Muse Glimmer · Muse Glimmer 30B assistant · Muse Spark 1.2

Data updated Thu, 12 Nov 2026 09:42 UTC. Benchmarks, Capabilities Index and model announcements: Epoch AI, “Capabilities & benchmarking” and “Data on AI models” (CC BY 4.0); category scores are ModelTape's own fit on Epoch's results. Releases and prices: models.dev (MIT). Sizes and quantizations: Hugging Face. “Runs on” figures are estimates from model size, quantization and memory bandwidth (±30–50%).
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