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 | GPT-4o Mini Transcribegpt-4o-mini-transcribe | Gemini 3.8 Flashgemini-3.8-flash | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0.625 / 1M | $0.75 / 1M | $1.25 / 1M |
| Output | $0.625 / 1M | $3.75 / 1M | $4.25 / 1M |
| Cache Write (5m) | Not applicable | $0.75 / 1M | $1.25 / 1M |
| Cache Write (1h) | Not applicable | $0.75 / 1M | $1.25 / 1M |
| Cache Read | Not applicable | $0.75 / 1M | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 128K | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | No | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | No | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | Meta | |
| Category | voice | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments. | 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. | 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. |