Muse Spark – Text & Chat #13, Vision #6 on HuaLinYuan
Not yet on this site
Overview
Muse Spark from Meta earns a HuaLinYuan Index of 1489 in Text & Chat (rank #13) and 1294 in Vision (rank #6), showing strong dual-modality performance. Its competitive pricing at $1.25 per million input tokens and $4.25 per million output tokens makes it attractive for high-volume workloads.
The model suits teams needing both conversational fluency and visual reasoning without managing separate specialist systems. While not yet available on HuaLinYuan, comparable alternatives such as Gemini 3.7 Flash, Gemini 3.8 Flash, and Kimi K3 are ready to use today.
Strengths
- Top-15 Text & Chat ranking
- Top-10 Vision ranking
- Competitive per-token pricing
- Unified text and vision capability
Best for
- Multimodal customer support agents
- Visual document analysis pipelines
- Image-rich content moderation
- Cross-modal search and retrieval
Things to know
- Not yet available on HuaLinYuan
- Official benchmarks not disclosed
Muse Spark vs other models
FAQ
What does Muse Spark cost and is it free?
Muse Spark is not free. Official pricing is $1.25 per million input tokens and $4.25 per million output tokens. No free tier is mentioned in the available information.
What is Muse Spark best at?
Muse Spark excels at combined text and vision tasks, ranking #13 in Text & Chat and #6 in Vision on the HuaLinYuan Index. It handles conversational AI and visual reasoning in a single model.
How should I choose between Muse Spark and its peers?
If you need a unified text-vision model with strong board rankings and can wait for availability, Muse Spark is compelling. For immediate use on HuaLinYuan, Gemini 3.7 Flash, Gemini 3.8 Flash, and Kimi K3 offer similar multimodal capabilities today.
Can I use Muse Spark on HuaLinYuan or LongHua AI right now?
No, Muse Spark is not yet available on this site. You can try the comparable models already usable here: Gemini 3.7 Flash, Gemini 3.8 Flash, and Kimi K3.
Similar models you can use here
Tell LongHua Tree ASI what you want — it splits the task and assigns the strongest model to each part, then delivers the finished result.