Muse Spark 1.3 is Meta’s multimodal reasoning model, designed for long-running agentic, multi-agent, and complex coding workflows. It can maintain context and task constraints across extended workflows, reconcile conflicting information, and request clarification or confirmation when needed. Compared with Muse Spark 1.2, Muse Spark 1.3 improves efficiency across long-horizon agentic and coding tasks, with fewer unnecessary turns, tool calls, and tokens, while producing more concise outputs.
Pricing
- Input Tokens: $1.375 /M tokens
- Output Tokens: $4.675 /M tokens
- Cache Read: $0.165 /M tokens
Input Modalities
- Text
- Vision
- Audio
- Video
Output Modalities
- Text
Context length
- 1.05M tokens
Capabilities
- Thinking
- Streaming
- Tool calling
- Web search
- URL context
- Code interpreter
- Computer use
- File search
- Memory tool
- Structured outputs
- Citations
- Prompt caching
- Background mode
- Server-side sessions
Providers
Meta meta-muse-spark-1.3
Pricing$1.375$4.675
Cache$0.165
Context1M
Max output1M
Latency4.5S
Throughput82.7TPS
Uptime
2.59% uptime 2 days ago
97.44% uptime yesterday
99.71% uptime today
Performance for muse-spark-1.3
Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).
Uptime
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Latency
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Throughput
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Try this model
Python
