Muse Spark 1.3
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.
- Reasoning
- Vision
- Audio Input
- Video Input
- Native PDF input
- Tool Calling
- Structured Output
Added Sep 2, 2026
Pricing
Auto routing · per 1M tokens- Input
- $1.25
- Output
- $4.25
- Cache read
- $0.15
Specifications
- Context window
- 1M
- Max output
- N/A
- Avg output (7d)
- 1.3K tokens
- Longer than 79% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
48.1
Coding Index
75.8
Agentic Index
55.5
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
57.9%
Better than 80% 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)
1583 Elo
Better than 93% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1684 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
CritPt
Research-level physics reasoning
24.9%
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
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
43.6%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
32.9%
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
GDPval-AA (unversioned / legacy)
Economically valuable tasks
59.0%
Last updated Oct 15, 2026
Artificial AnalysisProviders
Auto routing is available for this model. Explicit provider selection is not available.
Loading provider options…
