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 3.8 Flashgemini-3.8-flash | Gemini Embedding 001gemini-embedding-001 |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | $0.75 / 1M | $0.075 / 1M |
| Output | $4.25 / 1M | $3.75 / 1M | $0.30 / 1M |
| Cache Write (5m) | $1.25 / 1M | $0.75 / 1M | $0.075 / 1M |
| Cache Write (1h) | $1.25 / 1M | $0.75 / 1M | $0.075 / 1M |
| Cache Read | $1.25 / 1M | $0.75 / 1M | $0.075 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Context | |||
| Max context | 1M | 1M | 128K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | No |
| JSON Mode | Yes | Yes | No |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Meta | ||
| Category | chat | chat | embedding |
| Charge type | Pay As You Go | 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 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines. |