Meta's Muse Spark 1.3 is a frontier multimodal reasoning model for long-horizon coding and agentic workflows, with strong gains in computer use, browsing, professional tool use, codebase understanding, instruction following, and million-token retrieval. It accepts text, images, audio, video, and files, supports tool calling and structured output, and always reasons before answering.
Added Sep 2, 2026
Context Window
1.0M
Max Output
N/A
Avg output tokens (7d)
1.5K tokens
Input Price (Auto)
$1.25/1M
Output Price (Auto)
$4.25/1M
Cache Read (Auto)
$0.15/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
48.1
Coding Index
75.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
57.9%
Better than 81% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
29.8%
Better than 47% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
1586 Elo
Better than 92% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1672 Elo
Better than 95% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
26.6%
Better than 91% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
83.0%
Better than 95% of models compared
MLCR-AA
Medical long-context reasoning
43.3%
Better than 85% of models compared
Reasoning
HLE
Humanity's Last Exam
48.7%
Better than 95% of models compared
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
33.3%
Better than 83% of models compared
SciCode
Python programming for scientific computing
58.8%
Better than 92% of models compared
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
93.5%
Better than 97% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
83.0%
Better than 95% of models compared
Last updated Oct 1, 2026
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