Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | Muse Spark 1.3muse-spark-1.3 | Gemini Embedding 2gemini-embedding-2-preview |
|---|---|---|
| Pricing | ||
| Input | $1.25 / 1M | $0.60 / 1M |
| Output | $4.25 / 1M | $2.40 / 1M |
| Cache Write (5m) | $1.25 / 1M | $0.60 / 1M |
| Cache Write (1h) | $1.25 / 1M | $0.60 / 1M |
| Cache Read | $1.25 / 1M | $0.60 / 1M |
| Web Search | $0 / 1M | — |
| Context | ||
| Max context | 1M | 8.2K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | No |
| Streaming | Yes | No |
| Catalogue | ||
| Provider | Meta | |
| Category | chat | embedding |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows. | Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications. |