Meta Muse Spark 1.2
Muse Spark 1.2 is Meta’s proprietary Frontier model from August 5, 2026, designed for coding and agentic workflows, released by Meta Superintelligence Labs alongside the terminal agent Muse Code, with which it was co-trained. The cloud-only model under US jurisdiction (CLOUD Act) processes text, image, video, and audio with a context of 1,048,576 tokens (max. 131,072 output) and offers configurable reasoning effort up to ‘xhigh’.
- Proprietary
- Frontier
- OpenRouter
- Text
- Vision
- Audio
- Video
- Long Context
- Agentic Orchestrator
- Real-Time
Sovereign Risk: MEDIUM Meta is a US-based company and subject to the CLOUD Act; the model weights are proprietary and not publicly accessible (no self-hosting, no fine-tuning possible). Meta additionally offers a ‘Contributor’ pricing tier in which users agree, in exchange for significantly reduced costs, that their prompts may be used to train future Meta models — under this tier, the actual data risk increases considerably compared to the standard tier.
Key metrics
Score · Latency · Cost · Quality
- Total Score Silver
- 76.67
- Routine
- 46.43
- Reasoning
- 30.24
- LLM Judge Avg
- 3.93 / 5
- 100 Coverage
- Avg Task Duration
- 19.02s
- Real-Time
- Token Rate
- 114.94tok/s
- Output Rate
- P95 Latency
- 36.04s
- Top 5 %
- Total Tokens
- 127400
- Output Volume
- Cost per 1K
- $0.0042
- USD / 1K Requests
- Benchmark Cost
- $0.54
- Total · 127400 tok
Benchmark modules
10 modules · weighted · vs. model median & top performer
Token efficiency & latency
Consumption per module vs. model median
Token consumption per module
Performance profile
Key metrics – legend
Score
- Total Score
- Overall score derived from Routine and Reasoning scores. Basis for the fleet rank and tier assignment (Gold / Silver / Bronze).
- Routine
- Score for standard tasks: code generation, text transformation, structured outputs. Weighted by task complexity.
- Reasoning
- Score for analytical and logical tasks: multi-step problem solving, mathematical reasoning, structured argumentation.
- Rank
- Position in the fleet comparison, sorted by Total Score. Refers to the snapshot at the time of testing.
LLM Judge
- LLM Judge Ø
- Average quality rating by an independent judge model on a scale of 1–5.
- Coverage
- Share of tasks for which a judge rating is available. 100% means all responses were evaluated.
Latency & speed
- Avg. Task Duration
- Average time per task from the first to the last token of the response, measured across all benchmark tasks.
- Token Rate
- Output speed in tokens per second. Relevant for interactive applications and real-time streaming.
- P95 Latency
- 95th percentile of response times — shows outliers. Lower values mean fewer long wait times in edge cases.
Cost
- Cost per 1K
- API price per 1,000 output tokens in US dollars. Basis for cost calculation in production use.
- Benchmark Cost
- Total cost for the complete benchmark run of this model.